HydroClimATe: hydrologic and climatic analysis toolkit

USGS Publications Warehouse

Dickinson, Jesse; Hanson, Randall T.; Predmore, Steven K.

2014-01-01

The potential consequences of climate variability and climate change have been identified as major issues for the sustainability and availability of the worldwide water resources. Unlike global climate change, climate variability represents deviations from the long-term state of the climate over periods of a few years to several decades. Currently, rich hydrologic time-series data are available, but the combination of data preparation and statistical methods developed by the U.S. Geological Survey as part of the Groundwater Resources Program is relatively unavailable to hydrologists and engineers who could benefit from estimates of climate variability and its effects on periodic recharge and water-resource availability. This report documents HydroClimATe, a computer program for assessing the relations between variable climatic and hydrologic time-series data. HydroClimATe was developed for a Windows operating system. The software includes statistical tools for (1) time-series preprocessing, (2) spectral analysis, (3) spatial and temporal analysis, (4) correlation analysis, and (5) projections. The time-series preprocessing tools include spline fitting, standardization using a normal or gamma distribution, and transformation by a cumulative departure. The spectral analysis tools include discrete Fourier transform, maximum entropy method, and singular spectrum analysis. The spatial and temporal analysis tool is empirical orthogonal function analysis. The correlation analysis tools are linear regression and lag correlation. The projection tools include autoregressive time-series modeling and generation of many realizations. These tools are demonstrated in four examples that use stream-flow discharge data, groundwater-level records, gridded time series of precipitation data, and the Multivariate ENSO Index.

Defense Applications of Signal Processing

DTIC Science & Technology

1999-08-27

class of multiscale autoregressive moving average (MARMA) processes. These are generalisations of ARMA models in time series analysis , and they contain...including the two theoretical sinusoidal components. Analysis of the amplitude and frequency time series provided some novel insight into the real...communication channels, underwater acoustic signals, radar systems , economic time series and biomedical signals [7]. The alpha stable (aS) distribution has

Fast Algorithms for Mining Co-evolving Time Series

DTIC Science & Technology

2011-09-01

Keogh et al., 2001, 2004] and (b) forecasting, like an autoregressive integrated moving average model ( ARIMA ) and related meth- ods [Box et al., 1994...computing hardware? We develop models to mine time series with missing values, to extract compact representation from time sequences, to segment the...sequences, and to do forecasting. For large scale data, we propose algorithms for learning time series models , in particular, including Linear Dynamical

Enabling Web-Based Analysis of CUAHSI HIS Hydrologic Data Using R and Web Processing Services

NASA Astrophysics Data System (ADS)

Ames, D. P.; Kadlec, J.; Bayles, M.; Seul, M.; Hooper, R. P.; Cummings, B.

2015-12-01

The CUAHSI Hydrologic Information System (CUAHSI HIS) provides open access to a large number of hydrological time series observation and modeled data from many parts of the world. Several software tools have been designed to simplify searching and access to the CUAHSI HIS datasets. These software tools include: Desktop client software (HydroDesktop, HydroExcel), developer libraries (WaterML R Package, OWSLib, ulmo), and the new interactive search website, http://data.cuahsi.org. An issue with using the time series data from CUAHSI HIS for further analysis by hydrologists (for example for verification of hydrological and snowpack models) is the large heterogeneity of the time series data. The time series may be regular or irregular, contain missing data, have different time support, and be recorded in different units. R is a widely used computational environment for statistical analysis of time series and spatio-temporal data that can be used to assess fitness and perform scientific analyses on observation data. R includes the ability to record a data analysis in the form of a reusable script. The R script together with the input time series dataset can be shared with other users, making the analysis more reproducible. The major goal of this study is to examine the use of R as a Web Processing Service for transforming time series data from the CUAHSI HIS and sharing the results on the Internet within HydroShare. HydroShare is an online data repository and social network for sharing large hydrological data sets such as time series, raster datasets, and multi-dimensional data. It can be used as a permanent cloud storage space for saving the time series analysis results. We examine the issues associated with running R scripts online: including code validation, saving of outputs, reporting progress, and provenance management. An explicit goal is that the script which is run locally should produce exactly the same results as the script run on the Internet. Our design can be used as a model for other studies that need to run R scripts on the web.

Proposal of Classification Method of Time Series Data in International Emissions Trading Market Using Agent-based Simulation

NASA Astrophysics Data System (ADS)

Nakada, Tomohiro; Takadama, Keiki; Watanabe, Shigeyoshi

This paper proposes the classification method using Bayesian analytical method to classify the time series data in the international emissions trading market depend on the agent-based simulation and compares the case with Discrete Fourier transform analytical method. The purpose demonstrates the analytical methods mapping time series data such as market price. These analytical methods have revealed the following results: (1) the classification methods indicate the distance of mapping from the time series data, it is easier the understanding and inference than time series data; (2) these methods can analyze the uncertain time series data using the distance via agent-based simulation including stationary process and non-stationary process; and (3) Bayesian analytical method can show the 1% difference description of the emission reduction targets of agent.

Nonlinear parametric model for Granger causality of time series

NASA Astrophysics Data System (ADS)

Marinazzo, Daniele; Pellicoro, Mario; Stramaglia, Sebastiano

2006-06-01

The notion of Granger causality between two time series examines if the prediction of one series could be improved by incorporating information of the other. In particular, if the prediction error of the first time series is reduced by including measurements from the second time series, then the second time series is said to have a causal influence on the first one. We propose a radial basis function approach to nonlinear Granger causality. The proposed model is not constrained to be additive in variables from the two time series and can approximate any function of these variables, still being suitable to evaluate causality. Usefulness of this measure of causality is shown in two applications. In the first application, a physiological one, we consider time series of heart rate and blood pressure in congestive heart failure patients and patients affected by sepsis: we find that sepsis patients, unlike congestive heart failure patients, show symmetric causal relationships between the two time series. In the second application, we consider the feedback loop in a model of excitatory and inhibitory neurons: we find that in this system causality measures the combined influence of couplings and membrane time constants.

Multivariate time series clustering on geophysical data recorded at Mt. Etna from 1996 to 2003

NASA Astrophysics Data System (ADS)

Di Salvo, Roberto; Montalto, Placido; Nunnari, Giuseppe; Neri, Marco; Puglisi, Giuseppe

2013-02-01

Time series clustering is an important task in data analysis issues in order to extract implicit, previously unknown, and potentially useful information from a large collection of data. Finding useful similar trends in multivariate time series represents a challenge in several areas including geophysics environment research. While traditional time series analysis methods deal only with univariate time series, multivariate time series analysis is a more suitable approach in the field of research where different kinds of data are available. Moreover, the conventional time series clustering techniques do not provide desired results for geophysical datasets due to the huge amount of data whose sampling rate is different according to the nature of signal. In this paper, a novel approach concerning geophysical multivariate time series clustering is proposed using dynamic time series segmentation and Self Organizing Maps techniques. This method allows finding coupling among trends of different geophysical data recorded from monitoring networks at Mt. Etna spanning from 1996 to 2003, when the transition from summit eruptions to flank eruptions occurred. This information can be used to carry out a more careful evaluation of the state of volcano and to define potential hazard assessment at Mt. Etna.

Measuring Complexity and Predictability of Time Series with Flexible Multiscale Entropy for Sensor Networks

PubMed Central

Zhou, Renjie; Yang, Chen; Wan, Jian; Zhang, Wei; Guan, Bo; Xiong, Naixue

2017-01-01

Measurement of time series complexity and predictability is sometimes the cornerstone for proposing solutions to topology and congestion control problems in sensor networks. As a method of measuring time series complexity and predictability, multiscale entropy (MSE) has been widely applied in many fields. However, sample entropy, which is the fundamental component of MSE, measures the similarity of two subsequences of a time series with either zero or one, but without in-between values, which causes sudden changes of entropy values even if the time series embraces small changes. This problem becomes especially severe when the length of time series is getting short. For solving such the problem, we propose flexible multiscale entropy (FMSE), which introduces a novel similarity function measuring the similarity of two subsequences with full-range values from zero to one, and thus increases the reliability and stability of measuring time series complexity. The proposed method is evaluated on both synthetic and real time series, including white noise, 1/f noise and real vibration signals. The evaluation results demonstrate that FMSE has a significant improvement in reliability and stability of measuring complexity of time series, especially when the length of time series is short, compared to MSE and composite multiscale entropy (CMSE). The proposed method FMSE is capable of improving the performance of time series analysis based topology and traffic congestion control techniques. PMID:28383496

Measuring Complexity and Predictability of Time Series with Flexible Multiscale Entropy for Sensor Networks.

PubMed

Zhou, Renjie; Yang, Chen; Wan, Jian; Zhang, Wei; Guan, Bo; Xiong, Naixue

2017-04-06

Measurement of time series complexity and predictability is sometimes the cornerstone for proposing solutions to topology and congestion control problems in sensor networks. As a method of measuring time series complexity and predictability, multiscale entropy (MSE) has been widely applied in many fields. However, sample entropy, which is the fundamental component of MSE, measures the similarity of two subsequences of a time series with either zero or one, but without in-between values, which causes sudden changes of entropy values even if the time series embraces small changes. This problem becomes especially severe when the length of time series is getting short. For solving such the problem, we propose flexible multiscale entropy (FMSE), which introduces a novel similarity function measuring the similarity of two subsequences with full-range values from zero to one, and thus increases the reliability and stability of measuring time series complexity. The proposed method is evaluated on both synthetic and real time series, including white noise, 1/f noise and real vibration signals. The evaluation results demonstrate that FMSE has a significant improvement in reliability and stability of measuring complexity of time series, especially when the length of time series is short, compared to MSE and composite multiscale entropy (CMSE). The proposed method FMSE is capable of improving the performance of time series analysis based topology and traffic congestion control techniques.

On statistical inference in time series analysis of the evolution of road safety.

PubMed

Commandeur, Jacques J F; Bijleveld, Frits D; Bergel-Hayat, Ruth; Antoniou, Constantinos; Yannis, George; Papadimitriou, Eleonora

2013-11-01

Data collected for building a road safety observatory usually include observations made sequentially through time. Examples of such data, called time series data, include annual (or monthly) number of road traffic accidents, traffic fatalities or vehicle kilometers driven in a country, as well as the corresponding values of safety performance indicators (e.g., data on speeding, seat belt use, alcohol use, etc.). Some commonly used statistical techniques imply assumptions that are often violated by the special properties of time series data, namely serial dependency among disturbances associated with the observations. The first objective of this paper is to demonstrate the impact of such violations to the applicability of standard methods of statistical inference, which leads to an under or overestimation of the standard error and consequently may produce erroneous inferences. Moreover, having established the adverse consequences of ignoring serial dependency issues, the paper aims to describe rigorous statistical techniques used to overcome them. In particular, appropriate time series analysis techniques of varying complexity are employed to describe the development over time, relating the accident-occurrences to explanatory factors such as exposure measures or safety performance indicators, and forecasting the development into the near future. Traditional regression models (whether they are linear, generalized linear or nonlinear) are shown not to naturally capture the inherent dependencies in time series data. Dedicated time series analysis techniques, such as the ARMA-type and DRAG approaches are discussed next, followed by structural time series models, which are a subclass of state space methods. The paper concludes with general recommendations and practice guidelines for the use of time series models in road safety research. Copyright © 2012 Elsevier Ltd. All rights reserved.

Modeling Non-Gaussian Time Series with Nonparametric Bayesian Model.

PubMed

Xu, Zhiguang; MacEachern, Steven; Xu, Xinyi

2015-02-01

We present a class of Bayesian copula models whose major components are the marginal (limiting) distribution of a stationary time series and the internal dynamics of the series. We argue that these are the two features with which an analyst is typically most familiar, and hence that these are natural components with which to work. For the marginal distribution, we use a nonparametric Bayesian prior distribution along with a cdf-inverse cdf transformation to obtain large support. For the internal dynamics, we rely on the traditionally successful techniques of normal-theory time series. Coupling the two components gives us a family of (Gaussian) copula transformed autoregressive models. The models provide coherent adjustments of time scales and are compatible with many extensions, including changes in volatility of the series. We describe basic properties of the models, show their ability to recover non-Gaussian marginal distributions, and use a GARCH modification of the basic model to analyze stock index return series. The models are found to provide better fit and improved short-range and long-range predictions than Gaussian competitors. The models are extensible to a large variety of fields, including continuous time models, spatial models, models for multiple series, models driven by external covariate streams, and non-stationary models.

A high-fidelity weather time series generator using the Markov Chain process on a piecewise level

NASA Astrophysics Data System (ADS)

Hersvik, K.; Endrerud, O.-E. V.

2017-12-01

A method is developed for generating a set of unique weather time-series based on an existing weather series. The method allows statistically valid weather variations to take place within repeated simulations of offshore operations. The numerous generated time series need to share the same statistical qualities as the original time series. Statistical qualities here refer mainly to the distribution of weather windows available for work, including durations and frequencies of such weather windows, and seasonal characteristics. The method is based on the Markov chain process. The core new development lies in how the Markov Process is used, specifically by joining small pieces of random length time series together rather than joining individual weather states, each from a single time step, which is a common solution found in the literature. This new Markov model shows favorable characteristics with respect to the requirements set forth and all aspects of the validation performed.

On the Prony series representation of stretched exponential relaxation

NASA Astrophysics Data System (ADS)

Mauro, John C.; Mauro, Yihong Z.

2018-09-01

Stretched exponential relaxation is a ubiquitous feature of homogeneous glasses. The stretched exponential decay function can be derived from the diffusion-trap model, which predicts certain critical values of the fractional stretching exponent, β. In practical implementations of glass relaxation models, it is computationally convenient to represent the stretched exponential function as a Prony series of simple exponentials. Here, we perform a comprehensive mathematical analysis of the Prony series approximation of the stretched exponential relaxation, including optimized coefficients for certain critical values of β. The fitting quality of the Prony series is analyzed as a function of the number of terms in the series. With a sufficient number of terms, the Prony series can accurately capture the time evolution of the stretched exponential function, including its "fat tail" at long times. However, it is unable to capture the divergence of the first-derivative of the stretched exponential function in the limit of zero time. We also present a frequency-domain analysis of the Prony series representation of the stretched exponential function and discuss its physical implications for the modeling of glass relaxation behavior.

Solutions for the diurnally forced advection-diffusion equation to estimate bulk fluid velocity and diffusivity in streambeds from temperature time series

Treesearch

Charles H. Luce; Daniele Tonina; Frank Gariglio; Ralph Applebee

2013-01-01

Work over the last decade has documented methods for estimating fluxes between streams and streambeds from time series of temperature at two depths in the streambed. We present substantial extension to the existing theory and practice of using temperature time series to estimate streambed water fluxes and thermal properties, including (1) a new explicit analytical...

Statistical tools for analysis and modeling of cosmic populations and astronomical time series: CUDAHM and TSE

NASA Astrophysics Data System (ADS)

Loredo, Thomas; Budavari, Tamas; Scargle, Jeffrey D.

2018-01-01

This presentation provides an overview of open-source software packages addressing two challenging classes of astrostatistics problems. (1) CUDAHM is a C++ framework for hierarchical Bayesian modeling of cosmic populations, leveraging graphics processing units (GPUs) to enable applying this computationally challenging paradigm to large datasets. CUDAHM is motivated by measurement error problems in astronomy, where density estimation and linear and nonlinear regression must be addressed for populations of thousands to millions of objects whose features are measured with possibly complex uncertainties, potentially including selection effects. An example calculation demonstrates accurate GPU-accelerated luminosity function estimation for simulated populations of $10^6$ objects in about two hours using a single NVIDIA Tesla K40c GPU. (2) Time Series Explorer (TSE) is a collection of software in Python and MATLAB for exploratory analysis and statistical modeling of astronomical time series. It comprises a library of stand-alone functions and classes, as well as an application environment for interactive exploration of times series data. The presentation will summarize key capabilities of this emerging project, including new algorithms for analysis of irregularly-sampled time series.

Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis.

PubMed

Wang, Jin; Sun, Xiangping; Nahavandi, Saeid; Kouzani, Abbas; Wu, Yuchuan; She, Mary

2014-11-01

Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Adaptive time-variant models for fuzzy-time-series forecasting.

PubMed

Wong, Wai-Keung; Bai, Enjian; Chu, Alice Wai-Ching

2010-12-01

A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, and other domains. Related studies mainly focus on three factors, namely, the partition of discourse, the content of forecasting rules, and the methods of defuzzification, all of which greatly influence the prediction accuracy of forecasting models. These studies use fixed analysis window sizes for forecasting. In this paper, an adaptive time-variant fuzzy-time-series forecasting model (ATVF) is proposed to improve forecasting accuracy. The proposed model automatically adapts the analysis window size of fuzzy time series based on the prediction accuracy in the training phase and uses heuristic rules to generate forecasting values in the testing phase. The performance of the ATVF model is tested using both simulated and actual time series including the enrollments at the University of Alabama, Tuscaloosa, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The experiment results show that the proposed ATVF model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series forecasting models.

Multiscale multifractal time irreversibility analysis of stock markets

NASA Astrophysics Data System (ADS)

Jiang, Chenguang; Shang, Pengjian; Shi, Wenbin

2016-11-01

Time irreversibility is one of the most important properties of nonstationary time series. Complex time series often demonstrate even multiscale time irreversibility, such that not only the original but also coarse-grained time series are asymmetric over a wide range of scales. We study the multiscale time irreversibility of time series. In this paper, we develop a method called multiscale multifractal time irreversibility analysis (MMRA), which allows us to extend the description of time irreversibility to include the dependence on the segment size and statistical moments. We test the effectiveness of MMRA in detecting multifractality and time irreversibility of time series generated from delayed Henon map and binomial multifractal model. Then we employ our method to the time irreversibility analysis of stock markets in different regions. We find that the emerging market has higher multifractality degree and time irreversibility compared with developed markets. In this sense, the MMRA method may provide new angles in assessing the evolution stage of stock markets.

Time series pCO2 at a coastal mooring: Internal consistency, seasonal cycles, and interannual variability

NASA Astrophysics Data System (ADS)

Reimer, Janet J.; Cai, Wei-Jun; Xue, Liang; Vargas, Rodrigo; Noakes, Scott; Hu, Xinping; Signorini, Sergio R.; Mathis, Jeremy T.; Feely, Richard A.; Sutton, Adrienne J.; Sabine, Christopher; Musielewicz, Sylvia; Chen, Baoshan; Wanninkhof, Rik

2017-08-01

Marine carbonate system monitoring programs often consist of multiple observational methods that include underway cruise data, moored autonomous time series, and discrete water bottle samples. Monitored parameters include all, or some of the following: partial pressure of CO2 of the water (pCO2w) and air, dissolved inorganic carbon (DIC), total alkalinity (TA), and pH. Any combination of at least two of the aforementioned parameters can be used to calculate the others. In this study at the Gray's Reef (GR) mooring in the South Atlantic Bight (SAB) we: examine the internal consistency of pCO2w from underway cruise, moored autonomous time series, and calculated from bottle samples (DIC-TA pairing); describe the seasonal to interannual pCO2w time series variability and air-sea flux (FCO2), as well as describe the potential sources of pCO2w variability; and determine the source/sink for atmospheric pCO2. Over the 8.5 years of GR mooring time series, mooring-underway and mooring-bottle calculated-pCO2w strongly correlate with r-values > 0.90. pCO2w and FCO2 time series follow seasonal thermal patterns; however, seasonal non-thermal processes, such as terrestrial export, net biological production, and air-sea exchange also influence variability. The linear slope of time series pCO2w increases by 5.2 ± 1.4 μatm y-1 with FCO2 increasing 51-70 mmol m-2 y-1. The net FCO2 sign can switch interannually with the magnitude varying greatly. Non-thermal pCO2w is also increasing over the time series, likely indicating that terrestrial export and net biological processes drive the long term pCO2w increase.

Network structure of multivariate time series.

PubMed

Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

2015-10-21

Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

Homogenising time series: beliefs, dogmas and facts

NASA Astrophysics Data System (ADS)

Domonkos, P.

2011-06-01

In the recent decades various homogenisation methods have been developed, but the real effects of their application on time series are still not known sufficiently. The ongoing COST action HOME (COST ES0601) is devoted to reveal the real impacts of homogenisation methods more detailed and with higher confidence than earlier. As a part of the COST activity, a benchmark dataset was built whose characteristics approach well the characteristics of real networks of observed time series. This dataset offers much better opportunity than ever before to test the wide variety of homogenisation methods, and analyse the real effects of selected theoretical recommendations. Empirical results show that real observed time series usually include several inhomogeneities of different sizes. Small inhomogeneities often have similar statistical characteristics than natural changes caused by climatic variability, thus the pure application of the classic theory that change-points of observed time series can be found and corrected one-by-one is impossible. However, after homogenisation the linear trends, seasonal changes and long-term fluctuations of time series are usually much closer to the reality than in raw time series. Some problems around detecting multiple structures of inhomogeneities, as well as that of time series comparisons within homogenisation procedures are discussed briefly in the study.

Interrupted Time Series Versus Statistical Process Control in Quality Improvement Projects.

PubMed

Andersson Hagiwara, Magnus; Andersson Gäre, Boel; Elg, Mattias

2016-01-01

To measure the effect of quality improvement interventions, it is appropriate to use analysis methods that measure data over time. Examples of such methods include statistical process control analysis and interrupted time series with segmented regression analysis. This article compares the use of statistical process control analysis and interrupted time series with segmented regression analysis for evaluating the longitudinal effects of quality improvement interventions, using an example study on an evaluation of a computerized decision support system.

OceanSITES: Sustained Ocean Time Series Observations in the Global Ocean.

NASA Astrophysics Data System (ADS)

Weller, R. A.; Gallage, C.; Send, U.; Lampitt, R. S.; Lukas, R.

2016-02-01

Time series observations at critical or representative locations are an essential element of a global ocean observing system that is unique and complements other approaches to sustained observing. OceanSITES is an international group of oceanographers associated with such time series sites. OceanSITES exists to promote the continuation and extension of ocean time series sites around the globe. It also exists to plan and oversee the global array of sites in order to address the needs of research, climate change detection, operational applications, and policy makers. OceanSITES is a voluntary group that sits as an Action Group of the JCOMM-OPS Data Buoy Cooperation Panel, where JCOMM-OPS is the operational ocean observing oversight group of the Joint Commission on Oceanography and Marine Meteorology of the International Oceanographic Commission and the World Meteorological Organization. The way forward includes working to complete the global array, moving toward multidisciplinary instrumentation on a subset of the sites, and increasing utilization of the time series data, which are freely available from two Global Data Assembly Centers, one at the National Data Buoy Center and one at Coriolis at IFREMER. One recnet OceanSITES initiative and several results from OceanSITES time series sites are presented. The recent initiative was the assembly of a pool of temperature/conductivity recorders fro provision to OceanSITES sites in order to provide deep ocean temperature and salinity time series. Examples from specific sites include: a 15-year record of surface meteorology and air-sea fluxes from off northern Chile that shows evidence of long-term trends in surface forcing; change in upper ocean salinity and stratification in association with regional change in the hydrological cycle can be seen at the Hawaii time series site; results from monitoring Atlantic meridional transport; and results from a European multidisciplinary time series site.

The Press Relations of a Local School District: An Analysis of the Emergence of School Issues.

ERIC Educational Resources Information Center

Morris, Jon R.; Guenter, Cornelius

Press coverage of a suburban midwest school district is analyzed as a set of time series of observations including the amount and quality of coverage. Possible shifts in these series because of the emergence of controversial issues are analyzed statistically using the Integrated Moving Average Time Series Model. Evidence of significant shifts in…

Forecasting daily meteorological time series using ARIMA and regression models

NASA Astrophysics Data System (ADS)

Murat, Małgorzata; Malinowska, Iwona; Gos, Magdalena; Krzyszczak, Jaromir

2018-04-01

The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.

BiGGEsTS: integrated environment for biclustering analysis of time series gene expression data

PubMed Central

Gonçalves, Joana P; Madeira, Sara C; Oliveira, Arlindo L

2009-01-01

Background The ability to monitor changes in expression patterns over time, and to observe the emergence of coherent temporal responses using expression time series, is critical to advance our understanding of complex biological processes. Biclustering has been recognized as an effective method for discovering local temporal expression patterns and unraveling potential regulatory mechanisms. The general biclustering problem is NP-hard. In the case of time series this problem is tractable, and efficient algorithms can be used. However, there is still a need for specialized applications able to take advantage of the temporal properties inherent to expression time series, both from a computational and a biological perspective. Findings BiGGEsTS makes available state-of-the-art biclustering algorithms for analyzing expression time series. Gene Ontology (GO) annotations are used to assess the biological relevance of the biclusters. Methods for preprocessing expression time series and post-processing results are also included. The analysis is additionally supported by a visualization module capable of displaying informative representations of the data, including heatmaps, dendrograms, expression charts and graphs of enriched GO terms. Conclusion BiGGEsTS is a free open source graphical software tool for revealing local coexpression of genes in specific intervals of time, while integrating meaningful information on gene annotations. It is freely available at: . We present a case study on the discovery of transcriptional regulatory modules in the response of Saccharomyces cerevisiae to heat stress. PMID:19583847

A new method for reconstruction of solar irradiance

NASA Astrophysics Data System (ADS)

Privalsky, Victor

2018-07-01

The purpose of this research is to show how time series should be reconstructed using an example with the data on total solar irradiation (TSI) of the Earth and on sunspot numbers (SSN) since 1749. The traditional approach through regression equation(s) is designed for time-invariant vectors of random variables and is not applicable to time series, which present random functions of time. The autoregressive reconstruction (ARR) method suggested here requires fitting a multivariate stochastic difference equation to the target/proxy time series. The reconstruction is done through the scalar equation for the target time series with the white noise term excluded. The time series approach is shown to provide a better reconstruction of TSI than the correlation/regression method. A reconstruction criterion is introduced which allows one to define in advance the achievable level of success in the reconstruction. The conclusion is that time series, including the total solar irradiance, cannot be reconstructed properly if the data are not treated as sample records of random processes and analyzed in both time and frequency domains.

Atmospheric turbulence simulation for Shuttle orbiter

NASA Technical Reports Server (NTRS)

Tatom, F. B.; Smith, S. R.

1979-01-01

An improved non-recursive model for atmospheric turbulence along the flight path of the Shuttle Orbiter is developed which provides for simulation of instantaneous vertical and horizontal gusts at the vehicle center-of-gravity, and also for simulation of instantaneous gust gradients. Based on this model the time series for both gusts and gust gradients are generated and stored on a series of magnetic tapes. Section 2 provides a description of the various technical considerations associated with the turbulence simulation model. Included in this section are descriptions of the digital filter simulation model, the von Karman spectra with finite upper limits, and the final non recursive turbulence simulation model which was used to generate the time series. Section 2 provides a description of the various technical considerations associated with the turbulence simulation model. Included in this section are descriptions of the digial filter simulation model, the von Karman spectra with finite upper limits, and the final non recursive turbulence simulation model which was used to generate the time series. Section 3 provides a description of the time series as currently recorded on magnetic tape. Conclusions and recommendations are presented in Section 4.

A probabilistic method for constructing wave time-series at inshore locations using model scenarios

USGS Publications Warehouse

Long, Joseph W.; Plant, Nathaniel G.; Dalyander, P. Soupy; Thompson, David M.

2014-01-01

Continuous time-series of wave characteristics (height, period, and direction) are constructed using a base set of model scenarios and simple probabilistic methods. This approach utilizes an archive of computationally intensive, highly spatially resolved numerical wave model output to develop time-series of historical or future wave conditions without performing additional, continuous numerical simulations. The archive of model output contains wave simulations from a set of model scenarios derived from an offshore wave climatology. Time-series of wave height, period, direction, and associated uncertainties are constructed at locations included in the numerical model domain. The confidence limits are derived using statistical variability of oceanographic parameters contained in the wave model scenarios. The method was applied to a region in the northern Gulf of Mexico and assessed using wave observations at 12 m and 30 m water depths. Prediction skill for significant wave height is 0.58 and 0.67 at the 12 m and 30 m locations, respectively, with similar performance for wave period and direction. The skill of this simplified, probabilistic time-series construction method is comparable to existing large-scale, high-fidelity operational wave models but provides higher spatial resolution output at low computational expense. The constructed time-series can be developed to support a variety of applications including climate studies and other situations where a comprehensive survey of wave impacts on the coastal area is of interest.

Comparison of different synthetic 5-min rainfall time series regarding their suitability for urban drainage modelling

NASA Astrophysics Data System (ADS)

van der Heijden, Sven; Callau Poduje, Ana; Müller, Hannes; Shehu, Bora; Haberlandt, Uwe; Lorenz, Manuel; Wagner, Sven; Kunstmann, Harald; Müller, Thomas; Mosthaf, Tobias; Bárdossy, András

2015-04-01

For the design and operation of urban drainage systems with numerical simulation models, long, continuous precipitation time series with high temporal resolution are necessary. Suitable observed time series are rare. As a result, intelligent design concepts often use uncertain or unsuitable precipitation data, which renders them uneconomic or unsustainable. An expedient alternative to observed data is the use of long, synthetic rainfall time series as input for the simulation models. Within the project SYNOPSE, several different methods to generate synthetic precipitation data for urban drainage modelling are advanced, tested, and compared. The presented study compares four different approaches of precipitation models regarding their ability to reproduce rainfall and runoff characteristics. These include one parametric stochastic model (alternating renewal approach), one non-parametric stochastic model (resampling approach), one downscaling approach from a regional climate model, and one disaggregation approach based on daily precipitation measurements. All four models produce long precipitation time series with a temporal resolution of five minutes. The synthetic time series are first compared to observed rainfall reference time series. Comparison criteria include event based statistics like mean dry spell and wet spell duration, wet spell amount and intensity, long term means of precipitation sum and number of events, and extreme value distributions for different durations. Then they are compared regarding simulated discharge characteristics using an urban hydrological model on a fictitious sewage network. First results show a principal suitability of all rainfall models but with different strengths and weaknesses regarding the different rainfall and runoff characteristics considered.

Complexity multiscale asynchrony measure and behavior for interacting financial dynamics

NASA Astrophysics Data System (ADS)

Yang, Ge; Wang, Jun; Niu, Hongli

2016-08-01

A stochastic financial price process is proposed and investigated by the finite-range multitype contact dynamical system, in an attempt to study the nonlinear behaviors of real asset markets. The viruses spreading process in a finite-range multitype system is used to imitate the interacting behaviors of diverse investment attitudes in a financial market, and the empirical research on descriptive statistics and autocorrelation behaviors of return time series is performed for different values of propagation rates. Then the multiscale entropy analysis is adopted to study several different shuffled return series, including the original return series, the corresponding reversal series, the random shuffled series, the volatility shuffled series and the Zipf-type shuffled series. Furthermore, we propose and compare the multiscale cross-sample entropy and its modification algorithm called composite multiscale cross-sample entropy. We apply them to study the asynchrony of pairs of time series under different time scales.

Cross-Sectional Time Series Designs: A General Transformation Approach.

ERIC Educational Resources Information Center

Velicer, Wayne F.; McDonald, Roderick P.

1991-01-01

The general transformation approach to time series analysis is extended to the analysis of multiple unit data by the development of a patterned transformation matrix. The procedure includes alternatives for special cases and requires only minor revisions in existing computer software. (SLD)

IDENTIFICATION OF REGIME SHIFTS IN TIME SERIES USING NEIGHBORHOOD STATISTICS

EPA Science Inventory

The identification of alternative dynamic regimes in ecological systems requires several lines of evidence. Previous work on time series analysis of dynamic regimes includes mainly model-fitting methods. We introduce two methods that do not use models. These approaches use state-...

EnvironmentalWaveletTool: Continuous and discrete wavelet analysis and filtering for environmental time series

NASA Astrophysics Data System (ADS)

Galiana-Merino, J. J.; Pla, C.; Fernandez-Cortes, A.; Cuezva, S.; Ortiz, J.; Benavente, D.

2014-10-01

A MATLAB-based computer code has been developed for the simultaneous wavelet analysis and filtering of several environmental time series, particularly focused on the analyses of cave monitoring data. The continuous wavelet transform, the discrete wavelet transform and the discrete wavelet packet transform have been implemented to provide a fast and precise time-period examination of the time series at different period bands. Moreover, statistic methods to examine the relation between two signals have been included. Finally, the entropy of curves and splines based methods have also been developed for segmenting and modeling the analyzed time series. All these methods together provide a user-friendly and fast program for the environmental signal analysis, with useful, practical and understandable results.

An Analysis of AH-1Z Helicopter Pilots and Qualifications: The Impact of Fleet Squadron Training Progression Timelines

DTIC Science & Technology

2018-03-01

remaining 3000- series time block. Although there are fewer flight hours when compared to AHC, the coordination with external agencies may increase the...training duration to complete FAC(A). Additionally, pilots may conduct 4000 to 6000 series events concurrently, thus expanding the training time . 4...A)I training. Because FAC(A)I is included in the 5000- series training block, the time period cannot be finitely determined. 8. FL and AMC are the

Conditional heteroscedasticity as a leading indicator of ecological regime shifts.

PubMed

Seekell, David A; Carpenter, Stephen R; Pace, Michael L

2011-10-01

Regime shifts are massive, often irreversible, rearrangements of nonlinear ecological processes that occur when systems pass critical transition points. Ecological regime shifts sometimes have severe consequences for human well-being, including eutrophication in lakes, desertification, and species extinctions. Theoretical and laboratory evidence suggests that statistical anomalies may be detectable leading indicators of regime shifts in ecological time series, making it possible to foresee and potentially avert incipient regime shifts. Conditional heteroscedasticity is persistent variance characteristic of time series with clustered volatility. Here, we analyze conditional heteroscedasticity as a potential leading indicator of regime shifts in ecological time series. We evaluate conditional heteroscedasticity by using ecological models with and without four types of critical transition. On approaching transition points, all time series contain significant conditional heteroscedasticity. This signal is detected hundreds of time steps in advance of the regime shift. Time series without regime shifts do not have significant conditional heteroscedasticity. Because probability values are easily associated with tests for conditional heteroscedasticity, detection of false positives in time series without regime shifts is minimized. This property reduces the need for a reference system to compare with the perturbed system.

Directionality volatility in electroencephalogram time series

NASA Astrophysics Data System (ADS)

Mansor, Mahayaudin M.; Green, David A.; Metcalfe, Andrew V.

2016-06-01

We compare time series of electroencephalograms (EEGs) from healthy volunteers with EEGs from subjects diagnosed with epilepsy. The EEG time series from the healthy group are recorded during awake state with their eyes open and eyes closed, and the records from subjects with epilepsy are taken from three different recording regions of pre-surgical diagnosis: hippocampal, epileptogenic and seizure zone. The comparisons for these 5 categories are in terms of deviations from linear time series models with constant variance Gaussian white noise error inputs. One feature investigated is directionality, and how this can be modelled by either non-linear threshold autoregressive models or non-Gaussian errors. A second feature is volatility, which is modelled by Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) processes. Other features include the proportion of variability accounted for by time series models, and the skewness and the kurtosis of the residuals. The results suggest these comparisons may have diagnostic potential for epilepsy and provide early warning of seizures.

Integrated method for chaotic time series analysis

DOEpatents

Hively, Lee M.; Ng, Esmond G.

1998-01-01

Methods and apparatus for automatically detecting differences between similar but different states in a nonlinear process monitor nonlinear data. Steps include: acquiring the data; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis; obtaining time serial trends in the nonlinear measures; and determining by comparison whether differences between similar but different states are indicated.

Transition Icons for Time-Series Visualization and Exploratory Analysis.

PubMed

Nickerson, Paul V; Baharloo, Raheleh; Wanigatunga, Amal A; Manini, Todd M; Tighe, Patrick J; Rashidi, Parisa

2018-03-01

The modern healthcare landscape has seen the rapid emergence of techniques and devices that temporally monitor and record physiological signals. The prevalence of time-series data within the healthcare field necessitates the development of methods that can analyze the data in order to draw meaningful conclusions. Time-series behavior is notoriously difficult to intuitively understand due to its intrinsic high-dimensionality, which is compounded in the case of analyzing groups of time series collected from different patients. Our framework, which we call transition icons, renders common patterns in a visual format useful for understanding the shared behavior within groups of time series. Transition icons are adept at detecting and displaying subtle differences and similarities, e.g., between measurements taken from patients receiving different treatment strategies or stratified by demographics. We introduce various methods that collectively allow for exploratory analysis of groups of time series, while being free of distribution assumptions and including simple heuristics for parameter determination. Our technique extracts discrete transition patterns from symbolic aggregate approXimation representations, and compiles transition frequencies into a bag of patterns constructed for each group. These transition frequencies are normalized and aligned in icon form to intuitively display the underlying patterns. We demonstrate the transition icon technique for two time-series datasets-postoperative pain scores, and hip-worn accelerometer activity counts. We believe transition icons can be an important tool for researchers approaching time-series data, as they give rich and intuitive information about collective time-series behaviors.

Dynamic and Regression Modeling of Ocean Variability in the Tide-Gauge Record at Seasonal and Longer Periods

NASA Technical Reports Server (NTRS)

Hill, Emma M.; Ponte, Rui M.; Davis, James L.

2007-01-01

Comparison of monthly mean tide-gauge time series to corresponding model time series based on a static inverted barometer (IB) for pressure-driven fluctuations and a ocean general circulation model (OM) reveals that the combined model successfully reproduces seasonal and interannual changes in relative sea level at many stations. Removal of the OM and IB from the tide-gauge record produces residual time series with a mean global variance reduction of 53%. The OM is mis-scaled for certain regions, and 68% of the residual time series contain a significant seasonal variability after removal of the OM and IB from the tide-gauge data. Including OM admittance parameters and seasonal coefficients in a regression model for each station, with IB also removed, produces residual time series with mean global variance reduction of 71%. Examination of the regional improvement in variance caused by scaling the OM, including seasonal terms, or both, indicates weakness in the model at predicting sea-level variation for constricted ocean regions. The model is particularly effective at reproducing sea-level variation for stations in North America, Europe, and Japan. The RMS residual for many stations in these areas is 25-35 mm. The production of "cleaner" tide-gauge time series, with oceanographic variability removed, is important for future analysis of nonsecular and regionally differing sea-level variations. Understanding the ocean model's strengths and weaknesses will allow for future improvements of the model.

Behavioral Correlates of System Operational Readiness (SOR): Summary of Workshop Proceedings.

DTIC Science & Technology

1983-10-01

1978) time series ARIMA models Use ARIMA models for (Box & Jenkins, 1976) interrupted time series Stage 7. Interpretation 7.1 Formatting and re...This report describes a 2-day conference called to explore the methodology required to develop a behavioral model of system operational readiness (SOR...Participants discussed (4) the behavioral variables that should be included in the model , (2) the system level measures that should be included, (3

Homogenising time series: Beliefs, dogmas and facts

NASA Astrophysics Data System (ADS)

Domonkos, P.

2010-09-01

For obtaining reliable information about climate change and climate variability the use of high quality data series is essentially important, and one basic tool of quality improvements is the statistical homogenisation of observed time series. In the recent decades large number of homogenisation methods has been developed, but the real effects of their application on time series are still not known entirely. The ongoing COST HOME project (COST ES0601) is devoted to reveal the real impacts of homogenisation methods more detailed and with higher confidence than earlier. As part of the COST activity, a benchmark dataset was built whose characteristics approach well the characteristics of real networks of observed time series. This dataset offers much better opportunity than ever to test the wide variety of homogenisation methods, and analyse the real effects of selected theoretical recommendations. The author believes that several old theoretical rules have to be re-evaluated. Some examples of the hot questions, a) Statistically detected change-points can be accepted only with the confirmation of metadata information? b) Do semi-hierarchic algorithms for detecting multiple change-points in time series function effectively in practise? c) Is it good to limit the spatial comparison of candidate series with up to five other series in the neighbourhood? Empirical results - those from the COST benchmark, and other experiments too - show that real observed time series usually include several inhomogeneities of different sizes. Small inhomogeneities seem like part of the climatic variability, thus the pure application of classic theory that change-points of observed time series can be found and corrected one-by-one is impossible. However, after homogenisation the linear trends, seasonal changes and long-term fluctuations of time series are usually much closer to the reality, than in raw time series. The developers and users of homogenisation methods have to bear in mind that the eventual purpose of homogenisation is not to find change-points, but to have the observed time series with statistical properties those characterise well the climate change and climate variability.

Coil-to-coil physiological noise correlations and their impact on fMRI time-series SNR

PubMed Central

Triantafyllou, C.; Polimeni, J. R.; Keil, B.; Wald, L. L.

2017-01-01

Purpose Physiological nuisance fluctuations (“physiological noise”) are a major contribution to the time-series Signal to Noise Ratio (tSNR) of functional imaging. While thermal noise correlations between array coil elements have a well-characterized effect on the image Signal to Noise Ratio (SNR0), the element-to-element covariance matrix of the time-series fluctuations has not yet been analyzed. We examine this effect with a goal of ultimately improving the combination of multichannel array data. Theory and Methods We extend the theoretical relationship between tSNR and SNR0 to include a time-series noise covariance matrix Ψt, distinct from the thermal noise covariance matrix Ψ0, and compare its structure to Ψ0 and the signal coupling matrix SSH formed from the signal intensity vectors S. Results Inclusion of the measured time-series noise covariance matrix into the model relating tSNR and SNR0 improves the fit of experimental multichannel data and is shown to be distinct from Ψ0 or SSH. Conclusion Time-series noise covariances in array coils are found to differ from Ψ0 and more surprisingly, from the signal coupling matrix SSH. Correct characterization of the time-series noise has implications for the analysis of time-series data and for improving the coil element combination process. PMID:26756964

Constructing networks from a dynamical system perspective for multivariate nonlinear time series.

PubMed

Nakamura, Tomomichi; Tanizawa, Toshihiro; Small, Michael

2016-03-01

We describe a method for constructing networks for multivariate nonlinear time series. We approach the interaction between the various scalar time series from a deterministic dynamical system perspective and provide a generic and algorithmic test for whether the interaction between two measured time series is statistically significant. The method can be applied even when the data exhibit no obvious qualitative similarity: a situation in which the naive method utilizing the cross correlation function directly cannot correctly identify connectivity. To establish the connectivity between nodes we apply the previously proposed small-shuffle surrogate (SSS) method, which can investigate whether there are correlation structures in short-term variabilities (irregular fluctuations) between two data sets from the viewpoint of deterministic dynamical systems. The procedure to construct networks based on this idea is composed of three steps: (i) each time series is considered as a basic node of a network, (ii) the SSS method is applied to verify the connectivity between each pair of time series taken from the whole multivariate time series, and (iii) the pair of nodes is connected with an undirected edge when the null hypothesis cannot be rejected. The network constructed by the proposed method indicates the intrinsic (essential) connectivity of the elements included in the system or the underlying (assumed) system. The method is demonstrated for numerical data sets generated by known systems and applied to several experimental time series.

Using NASA's Giovanni System to Simulate Time-Series Stations in the Outflow Region of California's Eel River

NASA Technical Reports Server (NTRS)

Acker, James G.; Shen, Suhung; Leptoukh, Gregory G.; Lee, Zhongping

2012-01-01

Oceanographic time-series stations provide vital data for the monitoring of oceanic processes, particularly those associated with trends over time and interannual variability. There are likely numerous locations where the establishment of a time-series station would be desirable, but for reasons of funding or logistics, such establishment may not be feasible. An alternative to an operational time-series station is monitoring of sites via remote sensing. In this study, the NASA Giovanni data system is employed to simulate the establishment of two time-series stations near the outflow region of California s Eel River, which carries a high sediment load. Previous time-series analysis of this location (Acker et al. 2009) indicated that remotely-sensed chl a exhibits a statistically significant increasing trend during summer (low flow) months, but no apparent trend during winter (high flow) months. Examination of several newly-available ocean data parameters in Giovanni, including 8-day resolution data, demonstrates the differences in ocean parameter trends at the two locations compared to regionally-averaged time-series. The hypothesis that the increased summer chl a values are related to increasing SST is evaluated, and the signature of the Eel River plume is defined with ocean optical parameters.

waterData--An R package for retrieval, analysis, and anomaly calculation of daily hydrologic time series data, version 1.0

USGS Publications Warehouse

Ryberg, Karen R.; Vecchia, Aldo V.

2012-01-01

Hydrologic time series data and associated anomalies (multiple components of the original time series representing variability at longer-term and shorter-term time scales) are useful for modeling trends in hydrologic variables, such as streamflow, and for modeling water-quality constituents. An R package, called waterData, has been developed for importing daily hydrologic time series data from U.S. Geological Survey streamgages into the R programming environment. In addition to streamflow, data retrieval may include gage height and continuous physical property data, such as specific conductance, pH, water temperature, turbidity, and dissolved oxygen. The package allows for importing daily hydrologic data into R, plotting the data, fixing common data problems, summarizing the data, and the calculation and graphical presentation of anomalies.

Cyberpark 2000: Protected Areas Management Pilot Project. Satellite time series vegetation monitoring

NASA Astrophysics Data System (ADS)

Monteleone, M.; Lanorte, A.; Lasaponara, R.

2009-04-01

Cyberpark 2000 is a project funded by the UE Regional Operating Program of the Apulia Region (2000-2006). The main objective of the Cyberpark 2000 project is to develop a new assessment model for the management and monitoring of protected areas in Foggia Province (Apulia Region) based on Information and Communication Technologies. The results herein described are placed inside the research activities finalized to develop an environmental monitoring system knowledge based on the use of satellite time series. This study include: - A- satellite time series of high spatial resolution data for supporting the analysis of fire static risk factors through land use mapping and spectral/quantitative characterization of vegetation fuels; - B- satellite time series of MODIS for supporting fire dynamic risk evaluation of study area - Integrated fire detection by using thermal imaging cameras placed on panoramic view-points; - C - integrated high spatial and high temporal satellite time series for supporting studies in change detection factors or anomalies in vegetation covers; - D - satellite time-series for monitoring: (i) post fire vegetation recovery and (ii) spatio/temporal vegetation dynamics in unburned and burned vegetation covers.

Time series modeling in traffic safety research.

PubMed

Lavrenz, Steven M; Vlahogianni, Eleni I; Gkritza, Konstantina; Ke, Yue

2018-08-01

The use of statistical models for analyzing traffic safety (crash) data has been well-established. However, time series techniques have traditionally been underrepresented in the corresponding literature, due to challenges in data collection, along with a limited knowledge of proper methodology. In recent years, new types of high-resolution traffic safety data, especially in measuring driver behavior, have made time series modeling techniques an increasingly salient topic of study. Yet there remains a dearth of information to guide analysts in their use. This paper provides an overview of the state of the art in using time series models in traffic safety research, and discusses some of the fundamental techniques and considerations in classic time series modeling. It also presents ongoing and future opportunities for expanding the use of time series models, and explores newer modeling techniques, including computational intelligence models, which hold promise in effectively handling ever-larger data sets. The information contained herein is meant to guide safety researchers in understanding this broad area of transportation data analysis, and provide a framework for understanding safety trends that can influence policy-making. Copyright © 2017 Elsevier Ltd. All rights reserved.

Cafe Data

ERIC Educational Resources Information Center

DePaolo, Concetta A.; Robinson, David F.

2011-01-01

In this paper we present time series data collected from a cafe run by business students at a Midwestern public university. The data were collected over a ten-week period during the spring semester of 2010. These data can be used in introductory courses to illustrate basic concepts of time series and forecasting, including trend, seasonality, and…

Including trait-based early warning signals helps predict population collapse

PubMed Central

Clements, Christopher F.; Ozgul, Arpat

2016-01-01

Foreseeing population collapse is an on-going target in ecology, and this has led to the development of early warning signals based on expected changes in leading indicators before a bifurcation. Such signals have been sought for in abundance time-series data on a population of interest, with varying degrees of success. Here we move beyond these established methods by including parallel time-series data of abundance and fitness-related trait dynamics. Using data from a microcosm experiment, we show that including information on the dynamics of phenotypic traits such as body size into composite early warning indices can produce more accurate inferences of whether a population is approaching a critical transition than using abundance time-series alone. By including fitness-related trait information alongside traditional abundance-based early warning signals in a single metric of risk, our generalizable approach provides a powerful new way to assess what populations may be on the verge of collapse. PMID:27009968

Integrated method for chaotic time series analysis

DOEpatents

Hively, L.M.; Ng, E.G.

1998-09-29

Methods and apparatus for automatically detecting differences between similar but different states in a nonlinear process monitor nonlinear data are disclosed. Steps include: acquiring the data; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis; obtaining time serial trends in the nonlinear measures; and determining by comparison whether differences between similar but different states are indicated. 8 figs.

A Continuous Long-Term Record of Magnetic-Storm Occurrence and Intensity

NASA Astrophysics Data System (ADS)

Love, J. J.

2007-05-01

Hourly magnetometer data have been produced by ground-based magnetic observatories for over a century. These data are used for a wide variety of applications, including many for space physics. In particular, hourly data from a longitudinal necklace of mid-latitude observatories can be used to construct a time series recording the storm-time disturbance index Dst, one of the most useful scalar summaries of magnetic storm intensity which is generally interpreted in terms of an equivalent equatorial magnetospheric ring current. Dst has been routinely calculated in a temporally piece-wise fashion since the IGY using a subset of the available observatories: four or five stations, typically including Honolulu (HON), San Juan (SJG), Kakioka Japan (KAK), Hermanus South Africa (HER), and Alibag India (ABG). In this presentation we discuss a single continuous Dst time series made using a denser and more uniform distribution of observatories than that which is standard: including, additionally, Watheroo Australia (WAT), Apia Samoa (API), and Vassouras Brazil (VSS). Starting first with the data from each individual observatory, we subtract the geomagnetic secular variation, caused primarily by the core dynamo, and the solar-quiet (Sq) variation, caused primarily by the ionospheric dynamo. The latter requires careful spectral analysis, and those intermediate results are, themselves, of scientific interest. Following this, we combine the disturbance residuals from each station to form the continuous Dst time series. Statistics deduced from this model allow us to quantify the likelihood of storm occurrence and intensity, both of which are modulated in time by the solar cycle. This analysis is accomplished using a 50 year Dst time series. The prospects for constructing a longer continuous Dst time series are discussed.

40 CFR 63.1361 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-07-01

... can be one release or a series of releases over a short time period due to a malfunction in the... or a series of devices. Examples include incinerators, carbon adsorption units, condensers, flares... do not occur simultaneously in a batch operation. A batch process consists of a series of batch...

Microbial oceanography and the Hawaii Ocean Time-series programme.

PubMed

Karl, David M; Church, Matthew J

2014-10-01

The Hawaii Ocean Time-series (HOT) programme has been tracking microbial and biogeochemical processes in the North Pacific Subtropical Gyre since October 1988. The near-monthly time series observations have revealed previously undocumented phenomena within a temporally dynamic ecosystem that is vulnerable to climate change. Novel microorganisms, genes and unexpected metabolic pathways have been discovered and are being integrated into our evolving ecological paradigms. Continued research, including higher-frequency observations and at-sea experimentation, will help to provide a comprehensive scientific understanding of microbial processes in the largest biome on Earth.

Decadal GPS Time Series and Velocity Fields Spanning the North American Continent and Beyond: New Data Products, Cyberinfrastructure and Case Studies from the EarthScope Plate Boundary Observatory (PBO) and Other Regional Networks

NASA Astrophysics Data System (ADS)

Phillips, D. A.; Herring, T.; Melbourne, T. I.; Murray, M. H.; Szeliga, W. M.; Floyd, M.; Puskas, C. M.; King, R. W.; Boler, F. M.; Meertens, C. M.; Mattioli, G. S.

2017-12-01

The Geodesy Advancing Geosciences and EarthScope (GAGE) Facility, operated by UNAVCO, provides a diverse suite of geodetic data, derived products and cyberinfrastructure services to support community Earth science research and education. GPS data and products including decadal station position time series and velocities are provided for 2000+ continuous GPS stations from the Plate Boundary Observatory (PBO) and other networks distributed throughout the high Arctic, North America, and Caribbean regions. The position time series contain a multitude of signals in addition to the secular motions, including coseismic and postseismic displacements, interseismic strain accumulation, and transient signals associated with hydrologic and other processes. We present our latest velocity field solutions, new time series offset estimate products, and new time series examples associated with various phenomena. Position time series, and the signals they contain, are inherently dependent upon analysis parameters such as network scaling and reference frame realization. The estimation of scale changes for example, a common practice, has large impacts on vertical motion estimates. GAGE/PBO velocities and time series are currently provided in IGS (IGb08) and North America (NAM08, IGb08 rotated to a fixed North America Plate) reference frames. We are reprocessing all data (1996 to present) as part of the transition from IGb08 to IGS14 that began in 2017. New NAM14 and IGS14 data products are discussed. GAGE/PBO GPS data products are currently generated using onsite computing clusters. As part of an NSF funded EarthCube Building Blocks project called "Deploying MultiFacility Cyberinfrastructure in Commercial and Private Cloud-based Systems (GeoSciCloud)", we are investigating performance, cost, and efficiency differences between local computing resources and cloud based resources. Test environments include a commercial cloud provider (Amazon/AWS), NSF cloud-like infrastructures within XSEDE (TACC, the Texas Advanced Computing Center), and in-house cyberinfrastructures. Preliminary findings from this effort are presented. Web services developed by UNAVCO to facilitate the discovery, customization and dissemination of GPS data and products are also presented.

Exploratory Causal Analysis in Bivariate Time Series Data

NASA Astrophysics Data System (ADS)

McCracken, James M.

Many scientific disciplines rely on observational data of systems for which it is difficult (or impossible) to implement controlled experiments and data analysis techniques are required for identifying causal information and relationships directly from observational data. This need has lead to the development of many different time series causality approaches and tools including transfer entropy, convergent cross-mapping (CCM), and Granger causality statistics. In this thesis, the existing time series causality method of CCM is extended by introducing a new method called pairwise asymmetric inference (PAI). It is found that CCM may provide counter-intuitive causal inferences for simple dynamics with strong intuitive notions of causality, and the CCM causal inference can be a function of physical parameters that are seemingly unrelated to the existence of a driving relationship in the system. For example, a CCM causal inference might alternate between ''voltage drives current'' and ''current drives voltage'' as the frequency of the voltage signal is changed in a series circuit with a single resistor and inductor. PAI is introduced to address both of these limitations. Many of the current approaches in the times series causality literature are not computationally straightforward to apply, do not follow directly from assumptions of probabilistic causality, depend on assumed models for the time series generating process, or rely on embedding procedures. A new approach, called causal leaning, is introduced in this work to avoid these issues. The leaning is found to provide causal inferences that agree with intuition for both simple systems and more complicated empirical examples, including space weather data sets. The leaning may provide a clearer interpretation of the results than those from existing time series causality tools. A practicing analyst can explore the literature to find many proposals for identifying drivers and causal connections in times series data sets, but little research exists of how these tools compare to each other in practice. This work introduces and defines exploratory causal analysis (ECA) to address this issue along with the concept of data causality in the taxonomy of causal studies introduced in this work. The motivation is to provide a framework for exploring potential causal structures in time series data sets. ECA is used on several synthetic and empirical data sets, and it is found that all of the tested time series causality tools agree with each other (and intuitive notions of causality) for many simple systems but can provide conflicting causal inferences for more complicated systems. It is proposed that such disagreements between different time series causality tools during ECA might provide deeper insight into the data than could be found otherwise.

qFeature

DOE Office of Scientific and Technical Information (OSTI.GOV)

2015-09-14

This package contains statistical routines for extracting features from multivariate time-series data which can then be used for subsequent multivariate statistical analysis to identify patterns and anomalous behavior. It calculates local linear or quadratic regression model fits to moving windows for each series and then summarizes the model coefficients across user-defined time intervals for each series. These methods are domain agnostic-but they have been successfully applied to a variety of domains, including commercial aviation and electric power grid data.

Production, consumption, and prices of softwood products in North America: regional time series data, 1950 to 1985.

Treesearch

Darius M. Adams; Kristine C. Jackson; Richard W. Haynes

1988-01-01

This report provides 35 years of information on softwood timber production and consumption in the United States and Canada. Included are regional time series on production and prices of softwood lumber, plywood, residues, and pulpwood; timber harvest volumes and values; production costs; and recovery factors.

Time series models of environmental exposures: Good predictions or good understanding.

PubMed

Barnett, Adrian G; Stephen, Dimity; Huang, Cunrui; Wolkewitz, Martin

2017-04-01

Time series data are popular in environmental epidemiology as they make use of the natural experiment of how changes in exposure over time might impact on disease. Many published time series papers have used parameter-heavy models that fully explained the second order patterns in disease to give residuals that have no short-term autocorrelation or seasonality. This is often achieved by including predictors of past disease counts (autoregression) or seasonal splines with many degrees of freedom. These approaches give great residuals, but add little to our understanding of cause and effect. We argue that modelling approaches should rely more on good epidemiology and less on statistical tests. This includes thinking about causal pathways, making potential confounders explicit, fitting a limited number of models, and not over-fitting at the cost of under-estimating the true association between exposure and disease. Copyright © 2017 Elsevier Inc. All rights reserved.

Multiresolution analysis of Bursa Malaysia KLCI time series

NASA Astrophysics Data System (ADS)

Ismail, Mohd Tahir; Dghais, Amel Abdoullah Ahmed

2017-05-01

In general, a time series is simply a sequence of numbers collected at regular intervals over a period. Financial time series data processing is concerned with the theory and practice of processing asset price over time, such as currency, commodity data, and stock market data. The primary aim of this study is to understand the fundamental characteristics of selected financial time series by using the time as well as the frequency domain analysis. After that prediction can be executed for the desired system for in sample forecasting. In this study, multiresolution analysis which the assist of discrete wavelet transforms (DWT) and maximal overlap discrete wavelet transform (MODWT) will be used to pinpoint special characteristics of Bursa Malaysia KLCI (Kuala Lumpur Composite Index) daily closing prices and return values. In addition, further case study discussions include the modeling of Bursa Malaysia KLCI using linear ARIMA with wavelets to address how multiresolution approach improves fitting and forecasting results.

Mathematical Sciences Division 1992 Programs

DTIC Science & Technology

1992-10-01

statistical theory that underlies modern signal analysis . There is a strong emphasis on stochastic processes and time series , particularly those which...include optimal resource planning and real- time scheduling of stochastic shop-floor processes. Scheduling systems will be developed that can adapt to...make forecasts for the length-of-service time series . Protocol analysis of these sessions will be used to idenify relevant contextual features and to

Closed-Loop Optimal Control Implementations for Space Applications

DTIC Science & Technology

2016-12-01

analyses of a series of optimal control problems, several real- time optimal control algorithms are developed that continuously adapt to feedback on the...through the analyses of a series of optimal control problems, several real- time optimal control algorithms are developed that continuously adapt to...information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering

Time Series Decomposition into Oscillation Components and Phase Estimation.

PubMed

Matsuda, Takeru; Komaki, Fumiyasu

2017-02-01

Many time series are naturally considered as a superposition of several oscillation components. For example, electroencephalogram (EEG) time series include oscillation components such as alpha, beta, and gamma. We propose a method for decomposing time series into such oscillation components using state-space models. Based on the concept of random frequency modulation, gaussian linear state-space models for oscillation components are developed. In this model, the frequency of an oscillator fluctuates by noise. Time series decomposition is accomplished by this model like the Bayesian seasonal adjustment method. Since the model parameters are estimated from data by the empirical Bayes' method, the amplitudes and the frequencies of oscillation components are determined in a data-driven manner. Also, the appropriate number of oscillation components is determined with the Akaike information criterion (AIC). In this way, the proposed method provides a natural decomposition of the given time series into oscillation components. In neuroscience, the phase of neural time series plays an important role in neural information processing. The proposed method can be used to estimate the phase of each oscillation component and has several advantages over a conventional method based on the Hilbert transform. Thus, the proposed method enables an investigation of the phase dynamics of time series. Numerical results show that the proposed method succeeds in extracting intermittent oscillations like ripples and detecting the phase reset phenomena. We apply the proposed method to real data from various fields such as astronomy, ecology, tidology, and neuroscience.

Coil-to-coil physiological noise correlations and their impact on functional MRI time-series signal-to-noise ratio.

PubMed

Triantafyllou, Christina; Polimeni, Jonathan R; Keil, Boris; Wald, Lawrence L

2016-12-01

Physiological nuisance fluctuations ("physiological noise") are a major contribution to the time-series signal-to-noise ratio (tSNR) of functional imaging. While thermal noise correlations between array coil elements have a well-characterized effect on the image Signal to Noise Ratio (SNR 0 ), the element-to-element covariance matrix of the time-series fluctuations has not yet been analyzed. We examine this effect with a goal of ultimately improving the combination of multichannel array data. We extend the theoretical relationship between tSNR and SNR 0 to include a time-series noise covariance matrix Ψ t , distinct from the thermal noise covariance matrix Ψ 0 , and compare its structure to Ψ 0 and the signal coupling matrix SS H formed from the signal intensity vectors S. Inclusion of the measured time-series noise covariance matrix into the model relating tSNR and SNR 0 improves the fit of experimental multichannel data and is shown to be distinct from Ψ 0 or SS H . Time-series noise covariances in array coils are found to differ from Ψ 0 and more surprisingly, from the signal coupling matrix SS H . Correct characterization of the time-series noise has implications for the analysis of time-series data and for improving the coil element combination process. Magn Reson Med 76:1708-1719, 2016. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Approaches in highly parameterized inversion: TSPROC, a general time-series processor to assist in model calibration and result summarization

USGS Publications Warehouse

Westenbroek, Stephen M.; Doherty, John; Walker, John F.; Kelson, Victor A.; Hunt, Randall J.; Cera, Timothy B.

2012-01-01

The TSPROC (Time Series PROCessor) computer software uses a simple scripting language to process and analyze time series. It was developed primarily to assist in the calibration of environmental models. The software is designed to perform calculations on time-series data commonly associated with surface-water models, including calculation of flow volumes, transformation by means of basic arithmetic operations, and generation of seasonal and annual statistics and hydrologic indices. TSPROC can also be used to generate some of the key input files required to perform parameter optimization by means of the PEST (Parameter ESTimation) computer software. Through the use of TSPROC, the objective function for use in the model-calibration process can be focused on specific components of a hydrograph.

Real-time Series Resistance Monitoring in PV Systems; NREL (National Renewable Energy Laboratory)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deceglie, M. G.; Silverman, T. J.; Marion, B.

We apply the physical principles of a familiar method, suns-Voc, to a new application: the real-time detection of series resistance changes in modules and systems operating outside. The real-time series resistance (RTSR) method that we describe avoids the need for collecting IV curves or constructing full series-resistance-free IV curves. RTSR is most readily deployable at the module level on apply the physical principles of a familiar method, suns-Voc, to a new application: the real-time detection of series resistance changes in modules and systems operating outside. The real-time series resistance (RTSR) method that we describe avoids the need for collecting IVmore » curves or constructing full series-resistance-free IV curves. RTSR is most readily deployable at the module level on micro-inverters or module-integrated electronics, but it can also be extended to full strings. Automated detection of series resistance increases can provide early warnings of some of the most common reliability issues, which also pose fire risks, including broken ribbons, broken solder bonds, and contact problems in the junction or combiner box. We describe the method in detail and describe a sample application to data collected from modules operating in the field.« less

Determining a Prony Series for a Viscoelastic Material From Time Varying Strain Data

NASA Technical Reports Server (NTRS)

Tzikang, Chen

2000-01-01

In this study a method of determining the coefficients in a Prony series representation of a viscoelastic modulus from rate dependent data is presented. Load versus time test data for a sequence of different rate loading segments is least-squares fitted to a Prony series hereditary integral model of the material tested. A nonlinear least squares regression algorithm is employed. The measured data includes ramp loading, relaxation, and unloading stress-strain data. The resulting Prony series which captures strain rate loading and unloading effects, produces an excellent fit to the complex loading sequence.

An Observation Analysis Tool for time-series analysis and sensor management in the FREEWAT GIS environment for water resources management

NASA Astrophysics Data System (ADS)

Cannata, Massimiliano; Neumann, Jakob; Cardoso, Mirko; Rossetto, Rudy; Foglia, Laura; Borsi, Iacopo

2017-04-01

In situ time-series are an important aspect of environmental modelling, especially with the advancement of numerical simulation techniques and increased model complexity. In order to make use of the increasing data available through the requirements of the EU Water Framework Directive, the FREEWAT GIS environment incorporates the newly developed Observation Analysis Tool for time-series analysis. The tool is used to import time-series data into QGIS from local CSV files, online sensors using the istSOS service, or MODFLOW model result files and enables visualisation, pre-processing of data for model development, and post-processing of model results. OAT can be used as a pre-processor for calibration observations, integrating the creation of observations for calibration directly from sensor time-series. The tool consists in an expandable Python library of processing methods and an interface integrated in the QGIS FREEWAT plug-in which includes a large number of modelling capabilities, data management tools and calibration capacity.

Sea change: Charting the course for biogeochemical ocean time-series research in a new millennium

NASA Astrophysics Data System (ADS)

Church, Matthew J.; Lomas, Michael W.; Muller-Karger, Frank

2013-09-01

Ocean time-series provide vital information needed for assessing ecosystem change. This paper summarizes the historical context, major program objectives, and future research priorities for three contemporary ocean time-series programs: The Hawaii Ocean Time-series (HOT), the Bermuda Atlantic Time-series Study (BATS), and the CARIACO Ocean Time-Series. These three programs operate in physically and biogeochemically distinct regions of the world's oceans, with HOT and BATS located in the open-ocean waters of the subtropical North Pacific and North Atlantic, respectively, and CARIACO situated in the anoxic Cariaco Basin of the tropical Atlantic. All three programs sustain near-monthly shipboard occupations of their field sampling sites, with HOT and BATS beginning in 1988, and CARIACO initiated in 1996. The resulting data provide some of the only multi-disciplinary, decadal-scale determinations of time-varying ecosystem change in the global ocean. Facilitated by a scoping workshop (September 2010) sponsored by the Ocean Carbon Biogeochemistry (OCB) program, leaders of these time-series programs sought community input on existing program strengths and for future research directions. Themes that emerged from these discussions included: 1. Shipboard time-series programs are key to informing our understanding of the connectivity between changes in ocean-climate and biogeochemistry 2. The scientific and logistical support provided by shipboard time-series programs forms the backbone for numerous research and education programs. Future studies should be encouraged that seek mechanistic understanding of ecological interactions underlying the biogeochemical dynamics at these sites. 3. Detecting time-varying trends in ocean properties and processes requires consistent, high-quality measurements. Time-series must carefully document analytical procedures and, where possible, trace the accuracy of analyses to certified standards and internal reference materials. 4. Leveraged implementation, testing, and validation of autonomous and remote observing technologies at time-series sites provide new insights into spatiotemporal variability underlying ecosystem changes. 5. The value of existing time-series data for formulating and validating ecosystem models should be promoted. In summary, the scientific underpinnings of ocean time-series programs remain as strong and important today as when these programs were initiated. The emerging data inform our knowledge of the ocean's biogeochemistry and ecology, and improve our predictive capacity about planetary change.

Testing for nonlinearity in time series: The method of surrogate data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Theiler, J.; Galdrikian, B.; Longtin, A.

1991-01-01

We describe a statistical approach for identifying nonlinearity in time series; in particular, we want to avoid claims of chaos when simpler models (such as linearly correlated noise) can explain the data. The method requires a careful statement of the null hypothesis which characterizes a candidate linear process, the generation of an ensemble of surrogate'' data sets which are similar to the original time series but consistent with the null hypothesis, and the computation of a discriminating statistic for the original and for each of the surrogate data sets. The idea is to test the original time series against themore » null hypothesis by checking whether the discriminating statistic computed for the original time series differs significantly from the statistics computed for each of the surrogate sets. We present algorithms for generating surrogate data under various null hypotheses, and we show the results of numerical experiments on artificial data using correlation dimension, Lyapunov exponent, and forecasting error as discriminating statistics. Finally, we consider a number of experimental time series -- including sunspots, electroencephalogram (EEG) signals, and fluid convection -- and evaluate the statistical significance of the evidence for nonlinear structure in each case. 56 refs., 8 figs.« less

Bayesian dynamic modeling of time series of dengue disease case counts.

PubMed

Martínez-Bello, Daniel Adyro; López-Quílez, Antonio; Torres-Prieto, Alexander

2017-07-01

The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model's short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful models for decision-making in public health.

A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress

PubMed Central

2018-01-01

The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i) the proposed model is different from the previous models lacking the concept of time series; (ii) the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii) the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies. PMID:29765399

A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress.

PubMed

Cheng, Ching-Hsue; Chan, Chia-Pang; Yang, Jun-He

2018-01-01

The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i) the proposed model is different from the previous models lacking the concept of time series; (ii) the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii) the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies.

Estimating rainfall time series and model parameter distributions using model data reduction and inversion techniques

NASA Astrophysics Data System (ADS)

Wright, Ashley J.; Walker, Jeffrey P.; Pauwels, Valentijn R. N.

2017-08-01

Floods are devastating natural hazards. To provide accurate, precise, and timely flood forecasts, there is a need to understand the uncertainties associated within an entire rainfall time series, even when rainfall was not observed. The estimation of an entire rainfall time series and model parameter distributions from streamflow observations in complex dynamic catchments adds skill to current areal rainfall estimation methods, allows for the uncertainty of entire rainfall input time series to be considered when estimating model parameters, and provides the ability to improve rainfall estimates from poorly gauged catchments. Current methods to estimate entire rainfall time series from streamflow records are unable to adequately invert complex nonlinear hydrologic systems. This study aims to explore the use of wavelets in the estimation of rainfall time series from streamflow records. Using the Discrete Wavelet Transform (DWT) to reduce rainfall dimensionality for the catchment of Warwick, Queensland, Australia, it is shown that model parameter distributions and an entire rainfall time series can be estimated. Including rainfall in the estimation process improves streamflow simulations by a factor of up to 1.78. This is achieved while estimating an entire rainfall time series, inclusive of days when none was observed. It is shown that the choice of wavelet can have a considerable impact on the robustness of the inversion. Combining the use of a likelihood function that considers rainfall and streamflow errors with the use of the DWT as a model data reduction technique allows the joint inference of hydrologic model parameters along with rainfall.

NASA standard: Trend analysis techniques

NASA Technical Reports Server (NTRS)

1990-01-01

Descriptive and analytical techniques for NASA trend analysis applications are presented in this standard. Trend analysis is applicable in all organizational elements of NASA connected with, or supporting, developmental/operational programs. This document should be consulted for any data analysis activity requiring the identification or interpretation of trends. Trend analysis is neither a precise term nor a circumscribed methodology: it generally connotes quantitative analysis of time-series data. For NASA activities, the appropriate and applicable techniques include descriptive and graphical statistics, and the fitting or modeling of data by linear, quadratic, and exponential models. Usually, but not always, the data is time-series in nature. Concepts such as autocorrelation and techniques such as Box-Jenkins time-series analysis would only rarely apply and are not included in this document. The basic ideas needed for qualitative and quantitative assessment of trends along with relevant examples are presented.

77 FR 31964 - Energy Conservation Program: Energy Conservation Standards for Residential Dishwashers

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-30

... year used for discounting the NPV of total consumer costs and savings, for the time-series of costs and... does not imply that the time-series of cost and benefits from which the annualized values were... each TSL, DOE has included tables that present a summary of the results of DOE's quantitative analysis...

Base Stability of Aminocyclopropeniums

DTIC Science & Technology

2017-11-01

stability, a series of aminocyclopropeniums were synthesized and their base stability probed in situ using time -resolved proton nuclear magnetic resonance...reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions...tested for their utility in anion exchange membranes for alkaline fuel cells. A series of aminocyclopropeniums were synthesized and their base

Application of time series analysis for assessing reservoir trophic status

Treesearch

Paris Honglay Chen; Ka-Chu Leung

2000-01-01

This study is to develop and apply a practical procedure for the time series analysis of reservoir eutrophication conditions. A multiplicative decomposition method is used to determine the trophic variations including seasonal, circular, long-term and irregular changes. The results indicate that (1) there is a long high peak for seven months from April to October...

Defect-Repairable Latent Feature Extraction of Driving Behavior via a Deep Sparse Autoencoder

PubMed Central

Taniguchi, Tadahiro; Takenaka, Kazuhito; Bando, Takashi

2018-01-01

Data representing driving behavior, as measured by various sensors installed in a vehicle, are collected as multi-dimensional sensor time-series data. These data often include redundant information, e.g., both the speed of wheels and the engine speed represent the velocity of the vehicle. Redundant information can be expected to complicate the data analysis, e.g., more factors need to be analyzed; even varying the levels of redundancy can influence the results of the analysis. We assume that the measured multi-dimensional sensor time-series data of driving behavior are generated from low-dimensional data shared by the many types of one-dimensional data of which multi-dimensional time-series data are composed. Meanwhile, sensor time-series data may be defective because of sensor failure. Therefore, another important function is to reduce the negative effect of defective data when extracting low-dimensional time-series data. This study proposes a defect-repairable feature extraction method based on a deep sparse autoencoder (DSAE) to extract low-dimensional time-series data. In the experiments, we show that DSAE provides high-performance latent feature extraction for driving behavior, even for defective sensor time-series data. In addition, we show that the negative effect of defects on the driving behavior segmentation task could be reduced using the latent features extracted by DSAE. PMID:29462931

Forecasting Hourly Water Demands With Seasonal Autoregressive Models for Real-Time Application

NASA Astrophysics Data System (ADS)

Chen, Jinduan; Boccelli, Dominic L.

2018-02-01

Consumer water demands are not typically measured at temporal or spatial scales adequate to support real-time decision making, and recent approaches for estimating unobserved demands using observed hydraulic measurements are generally not capable of forecasting demands and uncertainty information. While time series modeling has shown promise for representing total system demands, these models have generally not been evaluated at spatial scales appropriate for representative real-time modeling. This study investigates the use of a double-seasonal time series model to capture daily and weekly autocorrelations to both total system demands and regional aggregated demands at a scale that would capture demand variability across a distribution system. Emphasis was placed on the ability to forecast demands and quantify uncertainties with results compared to traditional time series pattern-based demand models as well as nonseasonal and single-seasonal time series models. Additional research included the implementation of an adaptive-parameter estimation scheme to update the time series model when unobserved changes occurred in the system. For two case studies, results showed that (1) for the smaller-scale aggregated water demands, the log-transformed time series model resulted in improved forecasts, (2) the double-seasonal model outperformed other models in terms of forecasting errors, and (3) the adaptive adjustment of parameters during forecasting improved the accuracy of the generated prediction intervals. These results illustrate the capabilities of time series modeling to forecast both water demands and uncertainty estimates at spatial scales commensurate for real-time modeling applications and provide a foundation for developing a real-time integrated demand-hydraulic model.

Increasing the quality, comparability and accessibility of phytoplankton species composition time-series data

NASA Astrophysics Data System (ADS)

Zingone, Adriana; Harrison, Paul J.; Kraberg, Alexandra; Lehtinen, Sirpa; McQuatters-Gollop, Abigail; O'Brien, Todd; Sun, Jun; Jakobsen, Hans H.

2015-09-01

Phytoplankton diversity and its variation over an extended time scale can provide answers to a wide range of questions relevant to societal needs. These include human health, the safe and sustained use of marine resources and the ecological status of the marine environment, including long-term changes under the impact of multiple stressors. The analysis of phytoplankton data collected at the same place over time, as well as the comparison among different sampling sites, provide key information for assessing environmental change, and evaluating new actions that must be made to reduce human induced pressures on the environment. To achieve these aims, phytoplankton data may be used several decades later by users that have not participated in their production, including automatic data retrieval and analysis. The methods used in phytoplankton species analysis vary widely among research and monitoring groups, while quality control procedures have not been implemented in most cases. Here we highlight some of the main differences in the sampling and analytical procedures applied to phytoplankton analysis and identify critical steps that are required to improve the quality and inter-comparability of data obtained at different sites and/or times. Harmonization of methods may not be a realistic goal, considering the wide range of purposes of phytoplankton time-series data collection. However, we propose that more consistent and detailed metadata and complementary information be recorded and made available along with phytoplankton time-series datasets, including description of the procedures and elements allowing for a quality control of the data. To keep up with the progress in taxonomic research, there is a need for continued training of taxonomists, and for supporting and complementing existing web resources, in order to allow a constant upgrade of knowledge in phytoplankton classification and identification. Efforts towards the improvement of metadata recording, data annotation and quality control procedures will ensure the internal consistency of phytoplankton time series and facilitate their comparability and accessibility, thus strongly increasing the value of the precious information they provide. Ultimately, the sharing of quality controlled data will allow one to recoup the high cost of obtaining the data through the multiple use of the time-series data in various projects over many decades.

Rapid Calculation of Spacecraft Trajectories Using Efficient Taylor Series Integration

NASA Technical Reports Server (NTRS)

Scott, James R.; Martini, Michael C.

2011-01-01

A variable-order, variable-step Taylor series integration algorithm was implemented in NASA Glenn's SNAP (Spacecraft N-body Analysis Program) code. SNAP is a high-fidelity trajectory propagation program that can propagate the trajectory of a spacecraft about virtually any body in the solar system. The Taylor series algorithm's very high order accuracy and excellent stability properties lead to large reductions in computer time relative to the code's existing 8th order Runge-Kutta scheme. Head-to-head comparison on near-Earth, lunar, Mars, and Europa missions showed that Taylor series integration is 15.8 times faster than Runge- Kutta on average, and is more accurate. These speedups were obtained for calculations involving central body, other body, thrust, and drag forces. Similar speedups have been obtained for calculations that include J2 spherical harmonic for central body gravitation. The algorithm includes a step size selection method that directly calculates the step size and never requires a repeat step. High-order Taylor series integration algorithms have been shown to provide major reductions in computer time over conventional integration methods in numerous scientific applications. The objective here was to directly implement Taylor series integration in an existing trajectory analysis code and demonstrate that large reductions in computer time (order of magnitude) could be achieved while simultaneously maintaining high accuracy. This software greatly accelerates the calculation of spacecraft trajectories. At each time level, the spacecraft position, velocity, and mass are expanded in a high-order Taylor series whose coefficients are obtained through efficient differentiation arithmetic. This makes it possible to take very large time steps at minimal cost, resulting in large savings in computer time. The Taylor series algorithm is implemented primarily through three subroutines: (1) a driver routine that automatically introduces auxiliary variables and sets up initial conditions and integrates; (2) a routine that calculates system reduced derivatives using recurrence relations for quotients and products; and (3) a routine that determines the step size and sums the series. The order of accuracy used in a trajectory calculation is arbitrary and can be set by the user. The algorithm directly calculates the motion of other planetary bodies and does not require ephemeris files (except to start the calculation). The code also runs with Taylor series and Runge-Kutta used interchangeably for different phases of a mission.

Phase synchronization based minimum spanning trees for analysis of financial time series with nonlinear correlations

NASA Astrophysics Data System (ADS)

Radhakrishnan, Srinivasan; Duvvuru, Arjun; Sultornsanee, Sivarit; Kamarthi, Sagar

2016-02-01

The cross correlation coefficient has been widely applied in financial time series analysis, in specific, for understanding chaotic behaviour in terms of stock price and index movements during crisis periods. To better understand time series correlation dynamics, the cross correlation matrices are represented as networks, in which a node stands for an individual time series and a link indicates cross correlation between a pair of nodes. These networks are converted into simpler trees using different schemes. In this context, Minimum Spanning Trees (MST) are the most favoured tree structures because of their ability to preserve all the nodes and thereby retain essential information imbued in the network. Although cross correlations underlying MSTs capture essential information, they do not faithfully capture dynamic behaviour embedded in the time series data of financial systems because cross correlation is a reliable measure only if the relationship between the time series is linear. To address the issue, this work investigates a new measure called phase synchronization (PS) for establishing correlations among different time series which relate to one another, linearly or nonlinearly. In this approach the strength of a link between a pair of time series (nodes) is determined by the level of phase synchronization between them. We compare the performance of phase synchronization based MST with cross correlation based MST along selected network measures across temporal frame that includes economically good and crisis periods. We observe agreement in the directionality of the results across these two methods. They show similar trends, upward or downward, when comparing selected network measures. Though both the methods give similar trends, the phase synchronization based MST is a more reliable representation of the dynamic behaviour of financial systems than the cross correlation based MST because of the former's ability to quantify nonlinear relationships among time series or relations among phase shifted time series.

On Stabilizing the Variance of Dynamic Functional Brain Connectivity Time Series

PubMed Central

Fransson, Peter

2016-01-01

Abstract Assessment of dynamic functional brain connectivity based on functional magnetic resonance imaging (fMRI) data is an increasingly popular strategy to investigate temporal dynamics of the brain's large-scale network architecture. Current practice when deriving connectivity estimates over time is to use the Fisher transformation, which aims to stabilize the variance of correlation values that fluctuate around varying true correlation values. It is, however, unclear how well the stabilization of signal variance performed by the Fisher transformation works for each connectivity time series, when the true correlation is assumed to be fluctuating. This is of importance because many subsequent analyses either assume or perform better when the time series have stable variance or adheres to an approximate Gaussian distribution. In this article, using simulations and analysis of resting-state fMRI data, we analyze the effect of applying different variance stabilization strategies on connectivity time series. We focus our investigation on the Fisher transformation, the Box–Cox (BC) transformation and an approach that combines both transformations. Our results show that, if the intention of stabilizing the variance is to use metrics on the time series, where stable variance or a Gaussian distribution is desired (e.g., clustering), the Fisher transformation is not optimal and may even skew connectivity time series away from being Gaussian. Furthermore, we show that the suboptimal performance of the Fisher transformation can be substantially improved by including an additional BC transformation after the dynamic functional connectivity time series has been Fisher transformed. PMID:27784176

On Stabilizing the Variance of Dynamic Functional Brain Connectivity Time Series.

PubMed

Thompson, William Hedley; Fransson, Peter

2016-12-01

Assessment of dynamic functional brain connectivity based on functional magnetic resonance imaging (fMRI) data is an increasingly popular strategy to investigate temporal dynamics of the brain's large-scale network architecture. Current practice when deriving connectivity estimates over time is to use the Fisher transformation, which aims to stabilize the variance of correlation values that fluctuate around varying true correlation values. It is, however, unclear how well the stabilization of signal variance performed by the Fisher transformation works for each connectivity time series, when the true correlation is assumed to be fluctuating. This is of importance because many subsequent analyses either assume or perform better when the time series have stable variance or adheres to an approximate Gaussian distribution. In this article, using simulations and analysis of resting-state fMRI data, we analyze the effect of applying different variance stabilization strategies on connectivity time series. We focus our investigation on the Fisher transformation, the Box-Cox (BC) transformation and an approach that combines both transformations. Our results show that, if the intention of stabilizing the variance is to use metrics on the time series, where stable variance or a Gaussian distribution is desired (e.g., clustering), the Fisher transformation is not optimal and may even skew connectivity time series away from being Gaussian. Furthermore, we show that the suboptimal performance of the Fisher transformation can be substantially improved by including an additional BC transformation after the dynamic functional connectivity time series has been Fisher transformed.

Real-Time Series Resistance Monitoring in PV Systems Without the Need for I-V Curves

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deceglie, Michael G.; Silverman, Timothy J.; Marion, Bill

We apply the physical principles of a familiar method, suns-V oc, to a new application: the real-time detection of series resistance changes in modules and systems operating outside. The real-time series resistance (RTSR) method that we describe avoids the need for collecting I-V curves or constructing full series resistance-free I-V curves. RTSR is most readily deployable at the module level on microinverters or module-integrated electronics, but it can also be extended to full strings. We found that automated detection of series resistance increases can provide early warnings of some of the most common reliability issues, which also pose fire risks,more » including broken ribbons, broken solder bonds, and contact problems in the junction or combiner box. We also describe the method in detail and describe a sample application to data collected from modules operating in the field.« less

Real-Time Series Resistance Monitoring in PV Systems Without the Need for IV Curves

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deceglie, Michael G.; Silverman, Timothy J.; Marion, Bill

We apply the physical principles of a familiar method, suns-Voc, to a new application: the real-time detection of series resistance changes in modules and systems operating outside. The real-time series resistance (RTSR) method that we describe avoids the need for collecting IV curves or constructing full series-resistance-free IV curves. RTSR is most readily deployable at the module level on micro-inverters or module-integrated electronics, but it can also be extended to full strings. Automated detection of series resistance increases can provide early warnings of some of the most common reliability issues, which also pose fire risks, including broken ribbons, broken soldermore » bonds, and contact problems in the junction or combiner box. We describe the method in detail and describe a sample application to data collected from modules operating in the field.« less

Time Series Data Visualization in World Wide Telescope

NASA Astrophysics Data System (ADS)

Fay, J.

WorldWide Telescope provides a rich set of timer series visualization for both archival and real time data. WWT consists of both interactive desktop tools for interactive immersive visualization and HTML5 web based controls that can be utilized in customized web pages. WWT supports a range of display options including full dome, power walls, stereo and virtual reality headsets.

Understanding the source of multifractality in financial markets

NASA Astrophysics Data System (ADS)

Barunik, Jozef; Aste, Tomaso; Di Matteo, T.; Liu, Ruipeng

2012-09-01

In this paper, we use the generalized Hurst exponent approach to study the multi-scaling behavior of different financial time series. We show that this approach is robust and powerful in detecting different types of multi-scaling. We observe a puzzling phenomenon where an apparent increase in multifractality is measured in time series generated from shuffled returns, where all time-correlations are destroyed, while the return distributions are conserved. This effect is robust and it is reproduced in several real financial data including stock market indices, exchange rates and interest rates. In order to understand the origin of this effect we investigate different simulated time series by means of the Markov switching multifractal model, autoregressive fractionally integrated moving average processes with stable innovations, fractional Brownian motion and Levy flights. Overall we conclude that the multifractality observed in financial time series is mainly a consequence of the characteristic fat-tailed distribution of the returns and time-correlations have the effect to decrease the measured multifractality.

NASA standard: Trend analysis techniques

NASA Technical Reports Server (NTRS)

1988-01-01

This Standard presents descriptive and analytical techniques for NASA trend analysis applications. Trend analysis is applicable in all organizational elements of NASA connected with, or supporting, developmental/operational programs. Use of this Standard is not mandatory; however, it should be consulted for any data analysis activity requiring the identification or interpretation of trends. Trend Analysis is neither a precise term nor a circumscribed methodology, but rather connotes, generally, quantitative analysis of time-series data. For NASA activities, the appropriate and applicable techniques include descriptive and graphical statistics, and the fitting or modeling of data by linear, quadratic, and exponential models. Usually, but not always, the data is time-series in nature. Concepts such as autocorrelation and techniques such as Box-Jenkins time-series analysis would only rarely apply and are not included in this Standard. The document presents the basic ideas needed for qualitative and quantitative assessment of trends, together with relevant examples. A list of references provides additional sources of information.

TIMESERIESSTREAMING.VI: LabVIEW program for reliable data streaming of large analog time series

NASA Astrophysics Data System (ADS)

Czerwinski, Fabian; Oddershede, Lene B.

2011-02-01

With modern data acquisition devices that work fast and very precise, scientists often face the task of dealing with huge amounts of data. These need to be rapidly processed and stored onto a hard disk. We present a LabVIEW program which reliably streams analog time series of MHz sampling. Its run time has virtually no limitation. We explicitly show how to use the program to extract time series from two experiments: For a photodiode detection system that tracks the position of an optically trapped particle and for a measurement of ionic current through a glass capillary. The program is easy to use and versatile as the input can be any type of analog signal. Also, the data streaming software is simple, highly reliable, and can be easily customized to include, e.g., real-time power spectral analysis and Allan variance noise quantification. Program summaryProgram title: TimeSeriesStreaming.VI Catalogue identifier: AEHT_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEHT_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 250 No. of bytes in distributed program, including test data, etc.: 63 259 Distribution format: tar.gz Programming language: LabVIEW ( http://www.ni.com/labview/) Computer: Any machine running LabVIEW 8.6 or higher Operating system: Windows XP and Windows 7 RAM: 60-360 Mbyte Classification: 3 Nature of problem: For numerous scientific and engineering applications, it is highly desirable to have an efficient, reliable, and flexible program to perform data streaming of time series sampled with high frequencies and possibly for long time intervals. This type of data acquisition often produces very large amounts of data not easily streamed onto a computer hard disk using standard methods. Solution method: This LabVIEW program is developed to directly stream any kind of time series onto a hard disk. Due to optimized timing and usage of computational resources, such as multicores and protocols for memory usage, this program provides extremely reliable data acquisition. In particular, the program is optimized to deal with large amounts of data, e.g., taken with high sampling frequencies and over long time intervals. The program can be easily customized for time series analyses. Restrictions: Only tested in Windows-operating LabVIEW environments, must use TDMS format, acquisition cards must be LabVIEW compatible, driver DAQmx installed. Running time: As desirable: microseconds to hours

Time series smoother for effect detection.

PubMed

You, Cheng; Lin, Dennis K J; Young, S Stanley

2018-01-01

In environmental epidemiology, it is often encountered that multiple time series data with a long-term trend, including seasonality, cannot be fully adjusted by the observed covariates. The long-term trend is difficult to separate from abnormal short-term signals of interest. This paper addresses how to estimate the long-term trend in order to recover short-term signals. Our case study demonstrates that the current spline smoothing methods can result in significant positive and negative cross-correlations from the same dataset, depending on how the smoothing parameters are chosen. To circumvent this dilemma, three classes of time series smoothers are proposed to detrend time series data. These smoothers do not require fine tuning of parameters and can be applied to recover short-term signals. The properties of these smoothers are shown with both a case study using a factorial design and a simulation study using datasets generated from the original dataset. General guidelines are provided on how to discover short-term signals from time series with a long-term trend. The benefit of this research is that a problem is identified and characteristics of possible solutions are determined.

Time series smoother for effect detection

PubMed Central

Lin, Dennis K. J.; Young, S. Stanley

2018-01-01

In environmental epidemiology, it is often encountered that multiple time series data with a long-term trend, including seasonality, cannot be fully adjusted by the observed covariates. The long-term trend is difficult to separate from abnormal short-term signals of interest. This paper addresses how to estimate the long-term trend in order to recover short-term signals. Our case study demonstrates that the current spline smoothing methods can result in significant positive and negative cross-correlations from the same dataset, depending on how the smoothing parameters are chosen. To circumvent this dilemma, three classes of time series smoothers are proposed to detrend time series data. These smoothers do not require fine tuning of parameters and can be applied to recover short-term signals. The properties of these smoothers are shown with both a case study using a factorial design and a simulation study using datasets generated from the original dataset. General guidelines are provided on how to discover short-term signals from time series with a long-term trend. The benefit of this research is that a problem is identified and characteristics of possible solutions are determined. PMID:29684033

A hybrid-domain approach for modeling climate data time series

NASA Astrophysics Data System (ADS)

Wen, Qiuzi H.; Wang, Xiaolan L.; Wong, Augustine

2011-09-01

In order to model climate data time series that often contain periodic variations, trends, and sudden changes in mean (mean shifts, mostly artificial), this study proposes a hybrid-domain (HD) algorithm, which incorporates a time domain test and a newly developed frequency domain test through an iterative procedure that is analogue to the well known backfitting algorithm. A two-phase competition procedure is developed to address the confounding issue between modeling periodic variations and mean shifts. A variety of distinctive features of climate data time series, including trends, periodic variations, mean shifts, and a dependent noise structure, can be modeled in tandem using the HD algorithm. This is particularly important for homogenization of climate data from a low density observing network in which reference series are not available to help preserve climatic trends and long-term periodic variations, preventing them from being mistaken as artificial shifts. The HD algorithm is also powerful in estimating trend and periodicity in a homogeneous data time series (i.e., in the absence of any mean shift). The performance of the HD algorithm (in terms of false alarm rate and hit rate in detecting shifts/cycles, and estimation accuracy) is assessed via a simulation study. Its power is further illustrated through its application to a few climate data time series.

Analyses of Inhomogeneities in Radiosonde Temperature and Humidity Time Series.

NASA Astrophysics Data System (ADS)

Zhai, Panmao; Eskridge, Robert E.

1996-04-01

Twice daily radiosonde data from selected stations in the United States (period 1948 to 1990) and China (period 1958 to 1990) were sorted into time series. These stations have one sounding taken in darkness and the other in sunlight. The analysis shows that the 0000 and 1200 UTC time series are highly correlated. Therefore, the Easterling and Peterson technique was tested on the 0000 and 1200 time series to detect inhomogeneities and to estimate the size of the biases. Discontinuities were detected using the difference series created from the 0000 and 1200 UTC time series. To establish that the detected bias was significant, a t test was performed to confirm that the change occurs in the daytime series but not in the nighttime series.Both U.S. and Chinese radiosonde temperature and humidity data include inhomogeneities caused by changes in radiosonde sensors and observation times. The U.S. humidity data have inhomogeneities that were caused by instrument changes and the censoring of data. The practice of reporting relative humidity as 19% when it is lower than 20% or the temperature is below 40°C is called censoring. This combination of procedural and instrument changes makes the detection of biases and adjustment of the data very difficult. In the Chinese temperatures, them are inhomogeneities related to a change in the radiation correction procedure.Test results demonstrate that a modified Easterling and Peterson method is suitable for use in detecting and adjusting time series radiosonde data.Accurate stations histories are very desirable. Stations histories can confirm that detected inhomogeneities are related to instrument or procedural changes. Adjustments can then he made to the data with some confidence.

Your Paycheck. Cooperative Work Experience Learning Activity Packet: Series on Job Entry and Adjustment; Packet Five.

ERIC Educational Resources Information Center

Herschbach, Dennis R.; And Others

This student booklet is fifth in an illustrated series of eleven learning activity packets for use in teaching job hunting and application procedures and the management of wages to secondary students. Two units are included in this packet: the first describing the various ways of being paid: salary (including overtime and compensatory time),…

Mapping Cropland and Crop-type Distribution Using Time Series MODIS Data

NASA Astrophysics Data System (ADS)

Lu, D.; Chen, Y.; Moran, E. F.; Batistella, M.; Luo, L.; Pokhrel, Y.; Deb, K.

2016-12-01

Mapping regional and global cropland distribution has attracted great attention in the past decade, but the separation of crop types is challenging due to the spectral confusion and cloud cover problems during the growing season in Brazil. The objective of this study is to develop a new approach to identify crop types (including soybean, cotton, maize) and planting patterns (soybean-maize, soybean-cotton, and single crop) in Mato Grosso, Goias and Tocantins States, Brazil. The time series moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) (MOD13Q1) in 2015/2016 were used in this research and field survey data were collected in May 2016. The major steps include: (1) reconstruct time series NDVI data contaminated by noise and clouds using the temporal interpolation algorithm; (2) identify the best periods and develop temporal indices and phenology parameters to distinguish cropland from other land cover types based on time series NDVI data; (3) develop a crop temporal difference index (CTDI) to extract crop types and patterns using time series NDVI data. This research shows that (1) the cropland occupied approximately 16.85% of total land in these three states; (2) soybean-maize and soybean-cotton were two major crop patterns which occupied 54.80% and 19.30% of total cropland area. This research indicates that the proposed approach is promising for accurately and rapidly mapping cropland and crop-type distribution in these three states of Brazil.

Developing a comprehensive time series of GDP per capita for 210 countries from 1950 to 2015

PubMed Central

2012-01-01

Background Income has been extensively studied and utilized as a determinant of health. There are several sources of income expressed as gross domestic product (GDP) per capita, but there are no time series that are complete for the years between 1950 and 2015 for the 210 countries for which data exist. It is in the interest of population health research to establish a global time series that is complete from 1950 to 2015. Methods We collected GDP per capita estimates expressed in either constant US dollar terms or international dollar terms (corrected for purchasing power parity) from seven sources. We applied several stages of models, including ordinary least-squares regressions and mixed effects models, to complete each of the seven source series from 1950 to 2015. The three US dollar and four international dollar series were each averaged to produce two new GDP per capita series. Results and discussion Nine complete series from 1950 to 2015 for 210 countries are available for use. These series can serve various analytical purposes and can illustrate myriad economic trends and features. The derivation of the two new series allows for researchers to avoid any series-specific biases that may exist. The modeling approach used is flexible and will allow for yearly updating as new estimates are produced by the source series. Conclusion GDP per capita is a necessary tool in population health research, and our development and implementation of a new method has allowed for the most comprehensive known time series to date. PMID:22846561

'Botanic Man:' Education or Entertainment?

ERIC Educational Resources Information Center

Freeman, Richard

1979-01-01

The experience of Thames Television in presenting an educational series during prime time is described. "The Botanic Man," a series on ecology, is a rating success. Several difficulties encountered in collaboration efforts and follow-up activities, including courses and workbook publications, are identified. (JMF)

Weekly Solutions of Time-Variable Gravity from 1993 to 2010

NASA Technical Reports Server (NTRS)

Lemoine, F.; Chinn, D.; Le Bail, K.; Zelensky, N.; Melachroinos, S.; Beall, J.

2011-01-01

The GRACE mission has been highly successful in determining the time-variable gravity field of the Earth, producing monthly or even more frequent solutions (cf. 10-day) solutions using both spherical harmonics and mascons. However the GRACE time series only commences in 2002 - 2003 and a gap of several years may occur in the series before a GRACE follow-on satellite is launched. Satellites tracked by SLR and DORIS have also been used to study time variations in the Earth's gravitational field. These include (most recently) the solutions of Cox and Chao (2002), Cheng et al. (2004, 2007) and Lemoine et al. (2007). In this paper we discuss the development of a new time series of low degree spherical harmonic fields based on the available SLR, DORIS and GPS data. We develop simultaneous solutions for both the geocenter and the low degree harmonics up to 5x5. The solutions integrate data from SLR geodetic satellites (e.g., Lageos1, Lageos2, Starlette, Stella, Ajisai, Larets, Westpac), altimetry satellites (TOPEX/Poseidon, Envisat, Jason-1, Jason-2), and satellites tracked solely by DORIS (e.g. SPOT2-5). We discuss some pertinent aspects of the satellite-specific modeling. We include altimeter crossovers in the weekly solutions where feasible and time permits. The resulting geocenter time series is compared with geophysical model predictions and other independently-derived solutions. Over the GRACE time period the fidelity and consistency with the GRACE solutions are presented.

a Simple Spatially Weighted Measure of Temporal Stability for Data with Limited Temporal Observations

NASA Astrophysics Data System (ADS)

Piburn, J.; Stewart, R.; Morton, A.

2017-10-01

Identifying erratic or unstable time-series is an area of interest to many fields. Recently, there have been successful developments towards this goal. These new developed methodologies however come from domains where it is typical to have several thousand or more temporal observations. This creates a challenge when attempting to apply these methodologies to time-series with much fewer temporal observations such as for socio-cultural understanding, a domain where a typical time series of interest might only consist of 20-30 annual observations. Most existing methodologies simply cannot say anything interesting with so few data points, yet researchers are still tasked to work within in the confines of the data. Recently a method for characterizing instability in a time series with limitedtemporal observations was published. This method, Attribute Stability Index (ASI), uses an approximate entropy based method tocharacterize a time series' instability. In this paper we propose an explicitly spatially weighted extension of the Attribute StabilityIndex. By including a mechanism to account for spatial autocorrelation, this work represents a novel approach for the characterizationof space-time instability. As a case study we explore national youth male unemployment across the world from 1991-2014.

The Forest Types and Ages Cleared for Land Development in Puerto Rico.

Treesearch

Todd Kennaway; E. H. Helmer

2007-01-01

On the Caribbean island of Puerto Rico, forest, urban/built-up, and pasture lands have replaced most formerly cultivated lands. The extent and age distribution of each forest type that undergoes land development, however, is unknown. This study assembles a time series of four land cover maps for Puerto Rico. The time series includes two digitized paper maps of land...

Time lagged ordinal partition networks for capturing dynamics of continuous dynamical systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

McCullough, Michael; Iu, Herbert Ho-Ching; Small, Michael

2015-05-15

We investigate a generalised version of the recently proposed ordinal partition time series to network transformation algorithm. First, we introduce a fixed time lag for the elements of each partition that is selected using techniques from traditional time delay embedding. The resulting partitions define regions in the embedding phase space that are mapped to nodes in the network space. Edges are allocated between nodes based on temporal succession thus creating a Markov chain representation of the time series. We then apply this new transformation algorithm to time series generated by the Rössler system and find that periodic dynamics translate tomore » ring structures whereas chaotic time series translate to band or tube-like structures—thereby indicating that our algorithm generates networks whose structure is sensitive to system dynamics. Furthermore, we demonstrate that simple network measures including the mean out degree and variance of out degrees can track changes in the dynamical behaviour in a manner comparable to the largest Lyapunov exponent. We also apply the same analysis to experimental time series generated by a diode resonator circuit and show that the network size, mean shortest path length, and network diameter are highly sensitive to the interior crisis captured in this particular data set.« less

Frequency-phase analysis of resting-state functional MRI

PubMed Central

Goelman, Gadi; Dan, Rotem; Růžička, Filip; Bezdicek, Ondrej; Růžička, Evžen; Roth, Jan; Vymazal, Josef; Jech, Robert

2017-01-01

We describe an analysis method that characterizes the correlation between coupled time-series functions by their frequencies and phases. It provides a unified framework for simultaneous assessment of frequency and latency of a coupled time-series. The analysis is demonstrated on resting-state functional MRI data of 34 healthy subjects. Interactions between fMRI time-series are represented by cross-correlation (with time-lag) functions. A general linear model is used on the cross-correlation functions to obtain the frequencies and phase-differences of the original time-series. We define symmetric, antisymmetric and asymmetric cross-correlation functions that correspond respectively to in-phase, 90° out-of-phase and any phase difference between a pair of time-series, where the last two were never introduced before. Seed maps of the motor system were calculated to demonstrate the strength and capabilities of the analysis. Unique types of functional connections, their dominant frequencies and phase-differences have been identified. The relation between phase-differences and time-delays is shown. The phase-differences are speculated to inform transfer-time and/or to reflect a difference in the hemodynamic response between regions that are modulated by neurotransmitters concentration. The analysis can be used with any coupled functions in many disciplines including electrophysiology, EEG or MEG in neuroscience. PMID:28272522

Using Time Series Analysis to Predict Cardiac Arrest in a PICU.

PubMed

Kennedy, Curtis E; Aoki, Noriaki; Mariscalco, Michele; Turley, James P

2015-11-01

To build and test cardiac arrest prediction models in a PICU, using time series analysis as input, and to measure changes in prediction accuracy attributable to different classes of time series data. Retrospective cohort study. Thirty-one bed academic PICU that provides care for medical and general surgical (not congenital heart surgery) patients. Patients experiencing a cardiac arrest in the PICU and requiring external cardiac massage for at least 2 minutes. None. One hundred three cases of cardiac arrest and 109 control cases were used to prepare a baseline dataset that consisted of 1,025 variables in four data classes: multivariate, raw time series, clinical calculations, and time series trend analysis. We trained 20 arrest prediction models using a matrix of five feature sets (combinations of data classes) with four modeling algorithms: linear regression, decision tree, neural network, and support vector machine. The reference model (multivariate data with regression algorithm) had an accuracy of 78% and 87% area under the receiver operating characteristic curve. The best model (multivariate + trend analysis data with support vector machine algorithm) had an accuracy of 94% and 98% area under the receiver operating characteristic curve. Cardiac arrest predictions based on a traditional model built with multivariate data and a regression algorithm misclassified cases 3.7 times more frequently than predictions that included time series trend analysis and built with a support vector machine algorithm. Although the final model lacks the specificity necessary for clinical application, we have demonstrated how information from time series data can be used to increase the accuracy of clinical prediction models.

Characterizing and estimating noise in InSAR and InSAR time series with MODIS

USGS Publications Warehouse

Barnhart, William D.; Lohman, Rowena B.

2013-01-01

InSAR time series analysis is increasingly used to image subcentimeter displacement rates of the ground surface. The precision of InSAR observations is often affected by several noise sources, including spatially correlated noise from the turbulent atmosphere. Under ideal scenarios, InSAR time series techniques can substantially mitigate these effects; however, in practice the temporal distribution of InSAR acquisitions over much of the world exhibit seasonal biases, long temporal gaps, and insufficient acquisitions to confidently obtain the precisions desired for tectonic research. Here, we introduce a technique for constraining the magnitude of errors expected from atmospheric phase delays on the ground displacement rates inferred from an InSAR time series using independent observations of precipitable water vapor from MODIS. We implement a Monte Carlo error estimation technique based on multiple (100+) MODIS-based time series that sample date ranges close to the acquisitions times of the available SAR imagery. This stochastic approach allows evaluation of the significance of signals present in the final time series product, in particular their correlation with topography and seasonality. We find that topographically correlated noise in individual interferograms is not spatially stationary, even over short-spatial scales (<10 km). Overall, MODIS-inferred displacements and velocities exhibit errors of similar magnitude to the variability within an InSAR time series. We examine the MODIS-based confidence bounds in regions with a range of inferred displacement rates, and find we are capable of resolving velocities as low as 1.5 mm/yr with uncertainties increasing to ∼6 mm/yr in regions with higher topographic relief.

10 CFR 709.2 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-01-01

... test) during a specific series of interactions, including the pretest interview, the use of the polygraph instrument to collect physiological data from the examinee while presenting a series of tests, the... a CI evaluation each time the selection process occurs. Regular and routine means access by...

10 CFR 709.2 - Definitions.

Code of Federal Regulations, 2012 CFR

2012-01-01

... test) during a specific series of interactions, including the pretest interview, the use of the polygraph instrument to collect physiological data from the examinee while presenting a series of tests, the... a CI evaluation each time the selection process occurs. Regular and routine means access by...

Comparison of detrending methods for fluctuation analysis in hydrology

NASA Astrophysics Data System (ADS)

Zhang, Qiang; Zhou, Yu; Singh, Vijay P.; Chen, Yongqin David

2011-03-01

SummaryTrends within a hydrologic time series can significantly influence the scaling results of fluctuation analysis, such as rescaled range (RS) analysis and (multifractal) detrended fluctuation analysis (MF-DFA). Therefore, removal of trends is important in the study of scaling properties of the time series. In this study, three detrending methods, including adaptive detrending algorithm (ADA), Fourier-based method, and average removing technique, were evaluated by analyzing numerically generated series and observed streamflow series with obvious relative regular periodic trend. Results indicated that: (1) the Fourier-based detrending method and ADA were similar in detrending practices, and given proper parameters, these two methods can produce similarly satisfactory results; (2) detrended series by Fourier-based detrending method and ADA lose the fluctuation information at larger time scales, and the location of crossover points is heavily impacted by the chosen parameters of these two methods; and (3) the average removing method has an advantage over the other two methods, i.e., the fluctuation information at larger time scales is kept well-an indication of relatively reliable performance in detrending. In addition, the average removing method performed reasonably well in detrending a time series with regular periods or trends. In this sense, the average removing method should be preferred in the study of scaling properties of the hydrometeorolgical series with relative regular periodic trend using MF-DFA.

The "Chaos Theory" and nonlinear dynamics in heart rate variability analysis: does it work in short-time series in patients with coronary heart disease?

PubMed

Krstacic, Goran; Krstacic, Antonija; Smalcelj, Anton; Milicic, Davor; Jembrek-Gostovic, Mirjana

2007-04-01

Dynamic analysis techniques may quantify abnormalities in heart rate variability (HRV) based on nonlinear and fractal analysis (chaos theory). The article emphasizes clinical and prognostic significance of dynamic changes in short-time series applied on patients with coronary heart disease (CHD) during the exercise electrocardiograph (ECG) test. The subjects were included in the series after complete cardiovascular diagnostic data. Series of R-R and ST-T intervals were obtained from exercise ECG data after sampling digitally. The range rescaled analysis method determined the fractal dimension of the intervals. To quantify fractal long-range correlation's properties of heart rate variability, the detrended fluctuation analysis technique was used. Approximate entropy (ApEn) was applied to quantify the regularity and complexity of time series, as well as unpredictability of fluctuations in time series. It was found that the short-term fractal scaling exponent (alpha(1)) is significantly lower in patients with CHD (0.93 +/- 0.07 vs 1.09 +/- 0.04; P < 0.001). The patients with CHD had higher fractal dimension in each exercise test program separately, as well as in exercise program at all. ApEn was significant lower in CHD group in both RR and ST-T ECG intervals (P < 0.001). The nonlinear dynamic methods could have clinical and prognostic applicability also in short-time ECG series. Dynamic analysis based on chaos theory during the exercise ECG test point out the multifractal time series in CHD patients who loss normal fractal characteristics and regularity in HRV. Nonlinear analysis technique may complement traditional ECG analysis.

Feature extraction across individual time series observations with spikes using wavelet principal component analysis.

PubMed

Røislien, Jo; Winje, Brita

2013-09-20

Clinical studies frequently include repeated measurements of individuals, often for long periods. We present a methodology for extracting common temporal features across a set of individual time series observations. In particular, the methodology explores extreme observations within the time series, such as spikes, as a possible common temporal phenomenon. Wavelet basis functions are attractive in this sense, as they are localized in both time and frequency domains simultaneously, allowing for localized feature extraction from a time-varying signal. We apply wavelet basis function decomposition of individual time series, with corresponding wavelet shrinkage to remove noise. We then extract common temporal features using linear principal component analysis on the wavelet coefficients, before inverse transformation back to the time domain for clinical interpretation. We demonstrate the methodology on a subset of a large fetal activity study aiming to identify temporal patterns in fetal movement (FM) count data in order to explore formal FM counting as a screening tool for identifying fetal compromise and thus preventing adverse birth outcomes. Copyright © 2013 John Wiley & Sons, Ltd.

Bayesian dynamic modeling of time series of dengue disease case counts

PubMed Central

López-Quílez, Antonio; Torres-Prieto, Alexander

2017-01-01

The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model’s short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful models for decision-making in public health. PMID:28671941

A univariate model of river water nitrate time series

NASA Astrophysics Data System (ADS)

Worrall, F.; Burt, T. P.

1999-01-01

Four time series were taken from three catchments in the North and South of England. The sites chosen included two in predominantly agricultural catchments, one at the tidal limit and one downstream of a sewage treatment works. A time series model was constructed for each of these series as a means of decomposing the elements controlling river water nitrate concentrations and to assess whether this approach could provide a simple management tool for protecting water abstractions. Autoregressive (AR) modelling of the detrended and deseasoned time series showed a "memory effect". This memory effect expressed itself as an increase in the winter-summer difference in nitrate levels that was dependent upon the nitrate concentration 12 or 6 months previously. Autoregressive moving average (ARMA) modelling showed that one of the series contained seasonal, non-stationary elements that appeared as an increasing trend in the winter-summer difference. The ARMA model was used to predict nitrate levels and predictions were tested against data held back from the model construction process - predictions gave average percentage errors of less than 10%. Empirical modelling can therefore provide a simple, efficient method for constructing management models for downstream water abstraction.

Evaluation of recent GRACE monthly solution series with an ice sheet perspective

NASA Astrophysics Data System (ADS)

Horwath, Martin; Groh, Andreas

2016-04-01

GRACE monthly global gravity field solutions have undergone a remarkable evolution, leading to the latest (Release 5) series by CSR, GFZ, and JPL, to new series by other processing centers, such as ITSG and AIUB, as well as to efforts to derive combined solutions, particularly by the EGSIEM (European Gravity Service for Improved Emergency Management) project. For applications, such as GRACE inferences on ice sheet mass balance, the obvious question is on what GRACE solution series to base the assessment. Here we evaluate different GRACE solution series (including the ones listed above) in a unified framework. We concentrate on solutions expanded up to degree 90 or higher, since this is most appropriate for polar applications. We empirically assess the error levels in the spectral as well as in the spatial domain based on the month-to-month scatter in the high spherical harmonic degrees. We include empirical assessment of error correlations. We then apply all series to infer Antarctic and Greenland mass change time series and compare the results in terms of apparent signal content and noise level. We find that the ITSG solutions show lowest noise level in the high degrees (above 60). A preliminary combined solution from the EGSIEM project shows lowest noise in the degrees below 60. This virtue maps into the derived ice mass time series, where the EGSIEM-based results show the lowest noise in most cases. Meanwhile, there is no indication that any of the considered series systematically dampens actual geophysical signals.

Hybrid Wavelet De-noising and Rank-Set Pair Analysis approach for forecasting hydro-meteorological time series

NASA Astrophysics Data System (ADS)

WANG, D.; Wang, Y.; Zeng, X.

2017-12-01

Accurate, fast forecasting of hydro-meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, Wavelet De-noising (WD) and Rank-Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro-meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD-RSPA approach. Two types of hydro-meteorological data sets with different characteristics and different levels of human influences at some representative stations are used to illustrate the WD-RSPA approach. The approach is also compared to three other generic methods: the conventional Auto Regressive Integrated Moving Average (ARIMA) method, Artificial Neural Networks (ANNs) (BP-error Back Propagation, MLP-Multilayer Perceptron and RBF-Radial Basis Function), and RSPA alone. Nine error metrics are used to evaluate the model performance. The results show that WD-RSPA is accurate, feasible, and effective. In particular, WD-RSPA is found to be the best among the various generic methods compared in this paper, even when the extreme events are included within a time series.

10 CFR 905.11 - What must an IRP include?

Code of Federal Regulations, 2010 CFR

2010-01-01

... forecasting method, including but not limited to the time series, end-use, and econometric methods. The... projected durability of such savings measured over time; and must treat demand and supply resources on a... implement its IRP. (i) The IRP must state the time period that the action plan covers, and the action plan...

10 CFR 905.11 - What must an IRP include?

Code of Federal Regulations, 2011 CFR

2011-01-01

... forecasting method, including but not limited to the time series, end-use, and econometric methods. The... projected durability of such savings measured over time; and must treat demand and supply resources on a... implement its IRP. (i) The IRP must state the time period that the action plan covers, and the action plan...

10 CFR 905.11 - What must an IRP include?

Code of Federal Regulations, 2012 CFR

2012-01-01

... forecasting method, including but not limited to the time series, end-use, and econometric methods. The... projected durability of such savings measured over time; and must treat demand and supply resources on a... implement its IRP. (i) The IRP must state the time period that the action plan covers, and the action plan...

10 CFR 905.11 - What must an IRP include?

Code of Federal Regulations, 2014 CFR

2014-01-01

... forecasting method, including but not limited to the time series, end-use, and econometric methods. The... projected durability of such savings measured over time; and must treat demand and supply resources on a... implement its IRP. (i) The IRP must state the time period that the action plan covers, and the action plan...

10 CFR 905.11 - What must an IRP include?

Code of Federal Regulations, 2013 CFR

2013-01-01

... forecasting method, including but not limited to the time series, end-use, and econometric methods. The... projected durability of such savings measured over time; and must treat demand and supply resources on a... implement its IRP. (i) The IRP must state the time period that the action plan covers, and the action plan...

Comparison of Nomothetic versus Idiographic-Oriented Methods for Making Predictions about Distal Outcomes from Time Series Data

ERIC Educational Resources Information Center

Castro-Schilo, Laura; Ferrer, Emilio

2013-01-01

We illustrate the idiographic/nomothetic debate by comparing 3 approaches to using daily self-report data on affect for predicting relationship quality and breakup. The 3 approaches included (a) the first day in the series of daily data; (b) the mean and variability of the daily series; and (c) parameters from dynamic factor analysis, a…

Automated Bayesian model development for frequency detection in biological time series.

PubMed

Granqvist, Emma; Oldroyd, Giles E D; Morris, Richard J

2011-06-24

A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and the requirement for uniformly sampled data. Biological time series often deviate significantly from the requirements of optimality for Fourier transformation. In this paper we present an alternative approach based on Bayesian inference. We show the value of placing spectral analysis in the framework of Bayesian inference and demonstrate how model comparison can automate this procedure.

Automated Bayesian model development for frequency detection in biological time series

PubMed Central

2011-01-01

Background A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. Results In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Conclusions Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and the requirement for uniformly sampled data. Biological time series often deviate significantly from the requirements of optimality for Fourier transformation. In this paper we present an alternative approach based on Bayesian inference. We show the value of placing spectral analysis in the framework of Bayesian inference and demonstrate how model comparison can automate this procedure. PMID:21702910

Using machine learning to identify structural breaks in single-group interrupted time series designs.

PubMed

Linden, Ariel; Yarnold, Paul R

2016-12-01

Single-group interrupted time series analysis (ITSA) is a popular evaluation methodology in which a single unit of observation is being studied, the outcome variable is serially ordered as a time series and the intervention is expected to 'interrupt' the level and/or trend of the time series, subsequent to its introduction. Given that the internal validity of the design rests on the premise that the interruption in the time series is associated with the introduction of the treatment, treatment effects may seem less plausible if a parallel trend already exists in the time series prior to the actual intervention. Thus, sensitivity analyses should focus on detecting structural breaks in the time series before the intervention. In this paper, we introduce a machine-learning algorithm called optimal discriminant analysis (ODA) as an approach to determine if structural breaks can be identified in years prior to the initiation of the intervention, using data from California's 1988 voter-initiated Proposition 99 to reduce smoking rates. The ODA analysis indicates that numerous structural breaks occurred prior to the actual initiation of Proposition 99 in 1989, including perfect structural breaks in 1983 and 1985, thereby casting doubt on the validity of treatment effects estimated for the actual intervention when using a single-group ITSA design. Given the widespread use of ITSA for evaluating observational data and the increasing use of machine-learning techniques in traditional research, we recommend that structural break sensitivity analysis is routinely incorporated in all research using the single-group ITSA design. © 2016 John Wiley & Sons, Ltd.

KALREF—A Kalman filter and time series approach to the International Terrestrial Reference Frame realization

NASA Astrophysics Data System (ADS)

Wu, Xiaoping; Abbondanza, Claudio; Altamimi, Zuheir; Chin, T. Mike; Collilieux, Xavier; Gross, Richard S.; Heflin, Michael B.; Jiang, Yan; Parker, Jay W.

2015-05-01

The current International Terrestrial Reference Frame is based on a piecewise linear site motion model and realized by reference epoch coordinates and velocities for a global set of stations. Although linear motions due to tectonic plates and glacial isostatic adjustment dominate geodetic signals, at today's millimeter precisions, nonlinear motions due to earthquakes, volcanic activities, ice mass losses, sea level rise, hydrological changes, and other processes become significant. Monitoring these (sometimes rapid) changes desires consistent and precise realization of the terrestrial reference frame (TRF) quasi-instantaneously. Here, we use a Kalman filter and smoother approach to combine time series from four space geodetic techniques to realize an experimental TRF through weekly time series of geocentric coordinates. In addition to secular, periodic, and stochastic components for station coordinates, the Kalman filter state variables also include daily Earth orientation parameters and transformation parameters from input data frames to the combined TRF. Local tie measurements among colocated stations are used at their known or nominal epochs of observation, with comotion constraints applied to almost all colocated stations. The filter/smoother approach unifies different geodetic time series in a single geocentric frame. Fragmented and multitechnique tracking records at colocation sites are bridged together to form longer and coherent motion time series. While the time series approach to TRF reflects the reality of a changing Earth more closely than the linear approximation model, the filter/smoother is computationally powerful and flexible to facilitate incorporation of other data types and more advanced characterization of stochastic behavior of geodetic time series.

Time series regression model for infectious disease and weather.

PubMed

Imai, Chisato; Armstrong, Ben; Chalabi, Zaid; Mangtani, Punam; Hashizume, Masahiro

2015-10-01

Time series regression has been developed and long used to evaluate the short-term associations of air pollution and weather with mortality or morbidity of non-infectious diseases. The application of the regression approaches from this tradition to infectious diseases, however, is less well explored and raises some new issues. We discuss and present potential solutions for five issues often arising in such analyses: changes in immune population, strong autocorrelations, a wide range of plausible lag structures and association patterns, seasonality adjustments, and large overdispersion. The potential approaches are illustrated with datasets of cholera cases and rainfall from Bangladesh and influenza and temperature in Tokyo. Though this article focuses on the application of the traditional time series regression to infectious diseases and weather factors, we also briefly introduce alternative approaches, including mathematical modeling, wavelet analysis, and autoregressive integrated moving average (ARIMA) models. Modifications proposed to standard time series regression practice include using sums of past cases as proxies for the immune population, and using the logarithm of lagged disease counts to control autocorrelation due to true contagion, both of which are motivated from "susceptible-infectious-recovered" (SIR) models. The complexity of lag structures and association patterns can often be informed by biological mechanisms and explored by using distributed lag non-linear models. For overdispersed models, alternative distribution models such as quasi-Poisson and negative binomial should be considered. Time series regression can be used to investigate dependence of infectious diseases on weather, but may need modifying to allow for features specific to this context. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Temporal evolution of total ozone and circulation patterns over European mid-latitudes

NASA Astrophysics Data System (ADS)

Monge Sanz, B. M.; Casale, G. R.; Palmieri, S.; Siani, A. M.

2003-04-01

Linear correlation analysis and the running correlation technique are used to investigate the interannual and interdecadal variations of total ozone (TO) over several mid-latitude European locations. The study includes the longest series of ozone data, that of the Swiss station of Arosa. TO series have been related to time series of two circulation indices, the North Atlantic Oscillation Index (NAOI) and the Arctic Oscillation Index (AOI). The analysis has been performed with monthly data, and both series containing all the months of the year and winter (DJFM) series have been used. Special attention has been given to winter series, which exhibit very high correlation coefficients with NAOI and AOI; interannual variations of this relationship are studied by applying the running correlation technique. TO and circulation indices data series have been also partitioned into their different time-scale components with the Kolmogorov-Zurbenko method. Long-term components indicate the existence of strong opposite connection between total ozone and circulation patterns over the studied region during the last three decades. However, it is also observed that this relation has not always been so, and in previous times differences in the correlation amplitude and sign have been detected.

Seasonal variability and degradation investigation of iodocarbons in a coastal fjord

NASA Astrophysics Data System (ADS)

Shi, Qiang; Wallace, Douglas

2016-04-01

Methyl iodide (CH3I) is considered an important carrier of iodine atoms from sea to air. The importance of other volatile iodinated compounds, such as very short-lived iodocarbons (e.g. CH2ClI, CH2I2), has also been demonstrated [McFiggans, 2005; O'Dowd and Hoffmann, 2005; Carpenter et al., 2013]. The production pathways of iodocarbons, and controls on their sea-to-air flux can be investigated by in-situ studies (e.g. surface layer mass balance from time-series studies) and by incubation experiments. Shi et al., [2014] reported previously unrecognised large, night-time losses of CH3I observed during incubation experiments with coastal waters. These losses were significant for controlling the sea-to-air flux but are not yet understood. As part of a study to further investigate sources and sinks of CH3I and other iodocarbons in coastal waters, samples have been analysed weekly since April 2015 at 4 depths (5 to 60 m) in the Bedford Basin, Halifax, Canada. The time-series study was part of a broader study that included measurement of other, potentially related parameters (temperature, salinity, Chlorophyll a etc.). A set of repeated degradation experiments was conducted, in the context of this time-series, including incubations within a solar simulator using 13C labelled CH3I. Results of the time-series sampling and incubation experiments will be presented.

Examining deterrence of adult sex crimes: A semi-parametric intervention time series approach.

PubMed

Park, Jin-Hong; Bandyopadhyay, Dipankar; Letourneau, Elizabeth

2014-01-01

Motivated by recent developments on dimension reduction (DR) techniques for time series data, the association of a general deterrent effect towards South Carolina (SC)'s registration and notification (SORN) policy for preventing sex crimes was examined. Using adult sex crime arrestee data from 1990 to 2005, the the idea of Central Mean Subspace (CMS) is extended to intervention time series analysis (CMS-ITS) to model the sequential intervention effects of 1995 (the year SC's SORN policy was initially implemented) and 1999 (the year the policy was revised to include online notification) on the time series spectrum. The CMS-ITS model estimation was achieved via kernel smoothing techniques, and compared to interrupted auto-regressive integrated time series (ARIMA) models. Simulation studies and application to the real data underscores our model's ability towards achieving parsimony, and to detect intervention effects not earlier determined via traditional ARIMA models. From a public health perspective, findings from this study draw attention to the potential general deterrent effects of SC's SORN policy. These findings are considered in light of the overall body of research on sex crime arrestee registration and notification policies, which remain controversial.

High voltage pulse generator

DOEpatents

Fasching, George E.

1977-03-08

An improved high-voltage pulse generator has been provided which is especially useful in ultrasonic testing of rock core samples. An N number of capacitors are charged in parallel to V volts and at the proper instance are coupled in series to produce a high-voltage pulse of N times V volts. Rapid switching of the capacitors from the paralleled charging configuration to the series discharging configuration is accomplished by using silicon-controlled rectifiers which are chain self-triggered following the initial triggering of a first one of the rectifiers connected between the first and second of the plurality of charging capacitors. A timing and triggering circuit is provided to properly synchronize triggering pulses to the first SCR at a time when the charging voltage is not being applied to the parallel-connected charging capacitors. Alternate circuits are provided for controlling the application of the charging voltage from a charging circuit to be applied to the parallel capacitors which provides a selection of at least two different intervals in which the charging voltage is turned "off" to allow the SCR's connecting the capacitors in series to turn "off" before recharging begins. The high-voltage pulse-generating circuit including the N capacitors and corresponding SCR's which connect the capacitors in series when triggered "on" further includes diodes and series-connected inductors between the parallel-connected charging capacitors which allow sufficiently fast charging of the capacitors for a high pulse repetition rate and yet allow considerable control of the decay time of the high-voltage pulses from the pulse-generating circuit.

Methods for developing time-series climate surfaces to drive topographically distributed energy- and water-balance models

USGS Publications Warehouse

Susong, D.; Marks, D.; Garen, D.

1999-01-01

Topographically distributed energy- and water-balance models can accurately simulate both the development and melting of a seasonal snowcover in the mountain basins. To do this they require time-series climate surfaces of air temperature, humidity, wind speed, precipitation, and solar and thermal radiation. If data are available, these parameters can be adequately estimated at time steps of one to three hours. Unfortunately, climate monitoring in mountain basins is very limited, and the full range of elevations and exposures that affect climate conditions, snow deposition, and melt is seldom sampled. Detailed time-series climate surfaces have been successfully developed using limited data and relatively simple methods. We present a synopsis of the tools and methods used to combine limited data with simple corrections for the topographic controls to generate high temporal resolution time-series images of these climate parameters. Methods used include simulations, elevational gradients, and detrended kriging. The generated climate surfaces are evaluated at points and spatially to determine if they are reasonable approximations of actual conditions. Recommendations are made for the addition of critical parameters and measurement sites into routine monitoring systems in mountain basins.Topographically distributed energy- and water-balance models can accurately simulate both the development and melting of a seasonal snowcover in the mountain basins. To do this they require time-series climate surfaces of air temperature, humidity, wind speed, precipitation, and solar and thermal radiation. If data are available, these parameters can be adequately estimated at time steps of one to three hours. Unfortunately, climate monitoring in mountain basins is very limited, and the full range of elevations and exposures that affect climate conditions, snow deposition, and melt is seldom sampled. Detailed time-series climate surfaces have been successfully developed using limited data and relatively simple methods. We present a synopsis of the tools and methods used to combine limited data with simple corrections for the topographic controls to generate high temporal resolution time-series images of these climate parameters. Methods used include simulations, elevational gradients, and detrended kriging. The generated climate surfaces are evaluated at points and spatially to determine if they are reasonable approximations of actual conditions. Recommendations are made for the addition of critical parameters and measurement sites into routine monitoring systems in mountain basins.

Visibility graph analysis on quarterly macroeconomic series of China based on complex network theory

NASA Astrophysics Data System (ADS)

Wang, Na; Li, Dong; Wang, Qiwen

2012-12-01

The visibility graph approach and complex network theory provide a new insight into time series analysis. The inheritance of the visibility graph from the original time series was further explored in the paper. We found that degree distributions of visibility graphs extracted from Pseudo Brownian Motion series obtained by the Frequency Domain algorithm exhibit exponential behaviors, in which the exponential exponent is a binomial function of the Hurst index inherited in the time series. Our simulations presented that the quantitative relations between the Hurst indexes and the exponents of degree distribution function are different for different series and the visibility graph inherits some important features of the original time series. Further, we convert some quarterly macroeconomic series including the growth rates of value-added of three industry series and the growth rates of Gross Domestic Product series of China to graphs by the visibility algorithm and explore the topological properties of graphs associated from the four macroeconomic series, namely, the degree distribution and correlations, the clustering coefficient, the average path length, and community structure. Based on complex network analysis we find degree distributions of associated networks from the growth rates of value-added of three industry series are almost exponential and the degree distributions of associated networks from the growth rates of GDP series are scale free. We also discussed the assortativity and disassortativity of the four associated networks as they are related to the evolutionary process of the original macroeconomic series. All the constructed networks have “small-world” features. The community structures of associated networks suggest dynamic changes of the original macroeconomic series. We also detected the relationship among government policy changes, community structures of associated networks and macroeconomic dynamics. We find great influences of government policies in China on the changes of dynamics of GDP and the three industries adjustment. The work in our paper provides a new way to understand the dynamics of economic development.

A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Ning; Diao, Ruisheng; Hafen, Ryan P.

2013-12-18

This paper presents four algorithms to generate random forecast error time series, including a truncated-normal distribution model, a state-space based Markov model, a seasonal autoregressive moving average (ARMA) model, and a stochastic-optimization based model. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets, used for variable generation integration studies. A comparison is made using historical DA load forecast and actual load values to generate new sets of DA forecasts with similar stoical forecast error characteristics. This paper discusses and comparesmore » the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.« less

Beyond linear methods of data analysis: time series analysis and its applications in renal research.

PubMed

Gupta, Ashwani K; Udrea, Andreea

2013-01-01

Analysis of temporal trends in medicine is needed to understand normal physiology and to study the evolution of disease processes. It is also useful for monitoring response to drugs and interventions, and for accountability and tracking of health care resources. In this review, we discuss what makes time series analysis unique for the purposes of renal research and its limitations. We also introduce nonlinear time series analysis methods and provide examples where these have advantages over linear methods. We review areas where these computational methods have found applications in nephrology ranging from basic physiology to health services research. Some examples include noninvasive assessment of autonomic function in patients with chronic kidney disease, dialysis-dependent renal failure and renal transplantation. Time series models and analysis methods have been utilized in the characterization of mechanisms of renal autoregulation and to identify the interaction between different rhythms of nephron pressure flow regulation. They have also been used in the study of trends in health care delivery. Time series are everywhere in nephrology and analyzing them can lead to valuable knowledge discovery. The study of time trends of vital signs, laboratory parameters and the health status of patients is inherent to our everyday clinical practice, yet formal models and methods for time series analysis are not fully utilized. With this review, we hope to familiarize the reader with these techniques in order to assist in their proper use where appropriate.

Time-series analysis in imatinib-resistant chronic myeloid leukemia K562-cells under different drug treatments.

PubMed

Zhao, Yan-Hong; Zhang, Xue-Fang; Zhao, Yan-Qiu; Bai, Fan; Qin, Fan; Sun, Jing; Dong, Ying

2017-08-01

Chronic myeloid leukemia (CML) is characterized by the accumulation of active BCR-ABL protein. Imatinib is the first-line treatment of CML; however, many patients are resistant to this drug. In this study, we aimed to compare the differences in expression patterns and functions of time-series genes in imatinib-resistant CML cells under different drug treatments. GSE24946 was downloaded from the GEO database, which included 17 samples of K562-r cells with (n=12) or without drug administration (n=5). Three drug treatment groups were considered for this study: arsenic trioxide (ATO), AMN107, and ATO+AMN107. Each group had one sample at each time point (3, 12, 24, and 48 h). Time-series genes with a ratio of standard deviation/average (coefficient of variation) >0.15 were screened, and their expression patterns were revealed based on Short Time-series Expression Miner (STEM). Then, the functional enrichment analysis of time-series genes in each group was performed using DAVID, and the genes enriched in the top ten functional categories were extracted to detect their expression patterns. Different time-series genes were identified in the three groups, and most of them were enriched in the ribosome and oxidative phosphorylation pathways. Time-series genes in the three treatment groups had different expression patterns and functions. Time-series genes in the ATO group (e.g. CCNA2 and DAB2) were significantly associated with cell adhesion, those in the AMN107 group were related to cellular carbohydrate metabolic process, while those in the ATO+AMN107 group (e.g. AP2M1) were significantly related to cell proliferation and antigen processing. In imatinib-resistant CML cells, ATO could influence genes related to cell adhesion, AMN107 might affect genes involved in cellular carbohydrate metabolism, and the combination therapy might regulate genes involved in cell proliferation.

Array magnetics modal analysis for the DIII-D tokamak based on localized time-series modelling

DOE PAGES

Olofsson, K. Erik J.; Hanson, Jeremy M.; Shiraki, Daisuke; ...

2014-07-14

Here, time-series analysis of magnetics data in tokamaks is typically done using block-based fast Fourier transform methods. This work presents the development and deployment of a new set of algorithms for magnetic probe array analysis. The method is based on an estimation technique known as stochastic subspace identification (SSI). Compared with the standard coherence approach or the direct singular value decomposition approach, the new technique exhibits several beneficial properties. For example, the SSI method does not require that frequencies are orthogonal with respect to the timeframe used in the analysis. Frequencies are obtained directly as parameters of localized time-series models.more » The parameters are extracted by solving small-scale eigenvalue problems. Applications include maximum-likelihood regularized eigenmode pattern estimation, detection of neoclassical tearing modes, including locked mode precursors, and automatic clustering of modes, and magnetics-pattern characterization of sawtooth pre- and postcursors, edge harmonic oscillations and fishbones.« less

Computing Gross Pay from Punched Time Cards. Student Manual and Instructor's Manual.

ERIC Educational Resources Information Center

McElveen, Peggy C.

Supporting performance objective 30 of the V-TECS (Vocational-Technical Education Consortium of States) Secretarial Catalog, both a set of student materials and an instructor's manual on computing gross pay from punched time cards are included in this packet, which is part of a series. The student materials include a group of time cards,…

High Speed Solution of Spacecraft Trajectory Problems Using Taylor Series Integration

NASA Technical Reports Server (NTRS)

Scott, James R.; Martini, Michael C.

2008-01-01

Taylor series integration is implemented in a spacecraft trajectory analysis code-the Spacecraft N-body Analysis Program (SNAP) - and compared with the code s existing eighth-order Runge-Kutta Fehlberg time integration scheme. Nine trajectory problems, including near Earth, lunar, Mars and Europa missions, are analyzed. Head-to-head comparison at five different error tolerances shows that, on average, Taylor series is faster than Runge-Kutta Fehlberg by a factor of 15.8. Results further show that Taylor series has superior convergence properties. Taylor series integration proves that it can provide rapid, highly accurate solutions to spacecraft trajectory problems.

Sequential visibility-graph motifs

NASA Astrophysics Data System (ADS)

Iacovacci, Jacopo; Lacasa, Lucas

2016-04-01

Visibility algorithms transform time series into graphs and encode dynamical information in their topology, paving the way for graph-theoretical time series analysis as well as building a bridge between nonlinear dynamics and network science. In this work we introduce and study the concept of sequential visibility-graph motifs, smaller substructures of n consecutive nodes that appear with characteristic frequencies. We develop a theory to compute in an exact way the motif profiles associated with general classes of deterministic and stochastic dynamics. We find that this simple property is indeed a highly informative and computationally efficient feature capable of distinguishing among different dynamics and robust against noise contamination. We finally confirm that it can be used in practice to perform unsupervised learning, by extracting motif profiles from experimental heart-rate series and being able, accordingly, to disentangle meditative from other relaxation states. Applications of this general theory include the automatic classification and description of physical, biological, and financial time series.

Development and evaluation of a data-adaptive alerting algorithm for univariate temporal biosurveillance data.

PubMed

Elbert, Yevgeniy; Burkom, Howard S

2009-11-20

This paper discusses further advances in making robust predictions with the Holt-Winters forecasts for a variety of syndromic time series behaviors and introduces a control-chart detection approach based on these forecasts. Using three collections of time series data, we compare biosurveillance alerting methods with quantified measures of forecast agreement, signal sensitivity, and time-to-detect. The study presents practical rules for initialization and parameterization of biosurveillance time series. Several outbreak scenarios are used for detection comparison. We derive an alerting algorithm from forecasts using Holt-Winters-generalized smoothing for prospective application to daily syndromic time series. The derived algorithm is compared with simple control-chart adaptations and to more computationally intensive regression modeling methods. The comparisons are conducted on background data from both authentic and simulated data streams. Both types of background data include time series that vary widely by both mean value and cyclic or seasonal behavior. Plausible, simulated signals are added to the background data for detection performance testing at signal strengths calculated to be neither too easy nor too hard to separate the compared methods. Results show that both the sensitivity and the timeliness of the Holt-Winters-based algorithm proved to be comparable or superior to that of the more traditional prediction methods used for syndromic surveillance.

Signatures of ecological processes in microbial community time series.

PubMed

Faust, Karoline; Bauchinger, Franziska; Laroche, Béatrice; de Buyl, Sophie; Lahti, Leo; Washburne, Alex D; Gonze, Didier; Widder, Stefanie

2018-06-28

Growth rates, interactions between community members, stochasticity, and immigration are important drivers of microbial community dynamics. In sequencing data analysis, such as network construction and community model parameterization, we make implicit assumptions about the nature of these drivers and thereby restrict model outcome. Despite apparent risk of methodological bias, the validity of the assumptions is rarely tested, as comprehensive procedures are lacking. Here, we propose a classification scheme to determine the processes that gave rise to the observed time series and to enable better model selection. We implemented a three-step classification scheme in R that first determines whether dependence between successive time steps (temporal structure) is present in the time series and then assesses with a recently developed neutrality test whether interactions between species are required for the dynamics. If the first and second tests confirm the presence of temporal structure and interactions, then parameters for interaction models are estimated. To quantify the importance of temporal structure, we compute the noise-type profile of the community, which ranges from black in case of strong dependency to white in the absence of any dependency. We applied this scheme to simulated time series generated with the Dirichlet-multinomial (DM) distribution, Hubbell's neutral model, the generalized Lotka-Volterra model and its discrete variant (the Ricker model), and a self-organized instability model, as well as to human stool microbiota time series. The noise-type profiles for all but DM data clearly indicated distinctive structures. The neutrality test correctly classified all but DM and neutral time series as non-neutral. The procedure reliably identified time series for which interaction inference was suitable. Both tests were required, as we demonstrated that all structured time series, including those generated with the neutral model, achieved a moderate to high goodness of fit to the Ricker model. We present a fast and robust scheme to classify community structure and to assess the prevalence of interactions directly from microbial time series data. The procedure not only serves to determine ecological drivers of microbial dynamics, but also to guide selection of appropriate community models for prediction and follow-up analysis.

A 16-year time series of 1 km AVHRR satellite data of the conterminous United States and Alaska

USGS Publications Warehouse

Eidenshink, Jeff

2006-01-01

The U.S. Geological Survey (USGS) has developed a 16-year time series of vegetation condition information for the conterminous United States and Alaska using 1 km Advanced Very High Resolution Radiometer (AVHRR) data. The AVHRR data have been processed using consistent methods that account for radiometric variability due to calibration uncertainty, the effects of the atmosphere on surface radiometric measurements obtained from wide field-of-view observations, and the geometric registration accuracy. The conterminous United States and Alaska data sets have an atmospheric correction for water vapor, ozone, and Rayleigh scattering and include a cloud mask derived using the Clouds from AVHRR (CLAVR) algorithm. In comparison with other AVHRR time series data sets, the conterminous United States and Alaska data are processed using similar techniques. The primary difference is that the conterminous United States and Alaska data are at 1 km resolution, while others are at 8 km resolution. The time series consists of weekly and biweekly maximum normalized difference vegetation index (NDVI) composites.

Time series analysis for psychological research: examining and forecasting change

PubMed Central

Jebb, Andrew T.; Tay, Louis; Wang, Wei; Huang, Qiming

2015-01-01

Psychological research has increasingly recognized the importance of integrating temporal dynamics into its theories, and innovations in longitudinal designs and analyses have allowed such theories to be formalized and tested. However, psychological researchers may be relatively unequipped to analyze such data, given its many characteristics and the general complexities involved in longitudinal modeling. The current paper introduces time series analysis to psychological research, an analytic domain that has been essential for understanding and predicting the behavior of variables across many diverse fields. First, the characteristics of time series data are discussed. Second, different time series modeling techniques are surveyed that can address various topics of interest to psychological researchers, including describing the pattern of change in a variable, modeling seasonal effects, assessing the immediate and long-term impact of a salient event, and forecasting future values. To illustrate these methods, an illustrative example based on online job search behavior is used throughout the paper, and a software tutorial in R for these analyses is provided in the Supplementary Materials. PMID:26106341

What InSAR time-series methods are best suited for the Ecuadorian volcanoes

NASA Astrophysics Data System (ADS)

Mirzaee, S.; Amelung, F.

2017-12-01

Ground displacement measurements from stacks of SAR images obtained using interferometric time-series approaches play an increasingly important role for volcanic hazard assessment. The inflation of the ground surface can indicate that magma ascends to shallower levels and that a volcano gets ready for an eruption. Commonly used InSAR time-series approaches include Small Baseline (SB), Persistent Scatter InSAR (PSI) and SqueeSAR methods but it remains unclear which approach is best suited for volcanic environments. On this poster we present InSAR deformation measurements for the active volcanoes of Ecuador (Cotopaxi, Tungurahua and Pichincha) using a variety of INSAR time-series methods. We discuss the pros and cons of each method given the available data stacks (TerraSAR-X, Cosmo-Skymed and Sentinel-1) in an effort to design a comprehensive observation strategy for the Ecuadorian volcanoes. SAR data are provided in the framework of the Group on Earth Observation's Ecuadorian Volcano Geohazard Supersite.

Time series analysis for psychological research: examining and forecasting change.

PubMed

Jebb, Andrew T; Tay, Louis; Wang, Wei; Huang, Qiming

2015-01-01

Psychological research has increasingly recognized the importance of integrating temporal dynamics into its theories, and innovations in longitudinal designs and analyses have allowed such theories to be formalized and tested. However, psychological researchers may be relatively unequipped to analyze such data, given its many characteristics and the general complexities involved in longitudinal modeling. The current paper introduces time series analysis to psychological research, an analytic domain that has been essential for understanding and predicting the behavior of variables across many diverse fields. First, the characteristics of time series data are discussed. Second, different time series modeling techniques are surveyed that can address various topics of interest to psychological researchers, including describing the pattern of change in a variable, modeling seasonal effects, assessing the immediate and long-term impact of a salient event, and forecasting future values. To illustrate these methods, an illustrative example based on online job search behavior is used throughout the paper, and a software tutorial in R for these analyses is provided in the Supplementary Materials.

Correlation of physical properties with molecular structure for some dicyclic hydrocarbons having high thermal-energy release per unit volume -- 2-alkylbiphenyl and the two isomeric 2-alkylbicyclohexyl series

NASA Technical Reports Server (NTRS)

Goodman, Irving A; Wise, Paul H

1952-01-01

Three homologous series of related dicyclic hydrocarbons are presented for comparison on the basis of their physical properties, which include net heat of combustion, density, melting point, boiling point, and kinematic viscosity. The three series investigated include the 2-n-alkylbiphenyl, 2-n-alkylbicyclohexyl (high boiling), and 2-n-alkylbiphenyls (low boiling) series through c sub 16, in addition to three branched-chain (isopropyl, sec-butyl, and isobutyl) 2-alkylbiphenyls and their corresponding 2-alkylbicyclohexyls. The physical properties of the low-boiling and high-boiling isomers of 2-sec-butylbicyclohexyl and 2-isobutylbicyclohexyl are reported herein for the first time.

5 CFR 551.215 - Fire protection activities and 7(k) coverage for FLSA pay and exemption determinations.

Code of Federal Regulations, 2013 CFR

2013-01-01

... Protection and Prevention series, including any qualified firefighter who is assigned to perform support... considerations; (ii) Employees in positions properly classified in other series, such as Forestry Technician, for whom fire protection functions constitute substantially full-time assignments throughout the year, or...

5 CFR 551.215 - Fire protection activities and 7(k) coverage for FLSA pay and exemption determinations.

Code of Federal Regulations, 2011 CFR

2011-01-01

... Protection and Prevention series, including any qualified firefighter who is assigned to perform support... considerations; (ii) Employees in positions properly classified in other series, such as Forestry Technician, for whom fire protection functions constitute substantially full-time assignments throughout the year, or...

Interactive digital signal processor

NASA Technical Reports Server (NTRS)

Mish, W. H.; Wenger, R. M.; Behannon, K. W.; Byrnes, J. B.

1982-01-01

The Interactive Digital Signal Processor (IDSP) is examined. It consists of a set of time series analysis Operators each of which operates on an input file to produce an output file. The operators can be executed in any order that makes sense and recursively, if desired. The operators are the various algorithms used in digital time series analysis work. User written operators can be easily interfaced to the sysatem. The system can be operated both interactively and in batch mode. In IDSP a file can consist of up to n (currently n=8) simultaneous time series. IDSP currently includes over thirty standard operators that range from Fourier transform operations, design and application of digital filters, eigenvalue analysis, to operators that provide graphical output, allow batch operation, editing and display information.

Programmable Logic Application Notes

NASA Technical Reports Server (NTRS)

Katz, Richard

2000-01-01

This column will be provided each quarter as a source for reliability, radiation results, NASA capabilities, and other information on programmable logic devices and related applications. This quarter will continue a series of notes concentrating on analysis techniques with this issue's section discussing: Digital Timing Analysis Tools and Techniques. Articles in this issue include: SX and SX-A Series Devices Power Sequencing; JTAG and SXISX-AISX-S Series Devices; Analysis Techniques (i.e., notes on digital timing analysis tools and techniques); Status of the Radiation Hard reconfigurable Field Programmable Gate Array Program, Input Transition Times; Apollo Guidance Computer Logic Study; RT54SX32S Prototype Data Sets; A54SX32A - 0.22 micron/UMC Test Results; Ramtron FM1608 FRAM; and Analysis of VHDL Code and Synthesizer Output.

Quantitative Microbial Risk Assessment Tutorial: Publishing a Microbial Density Time Series as a Txt File

EPA Science Inventory

A SARA Timeseries Utility supports analysis and management of time-varying environmental data including listing, graphing, computing statistics, computing meteorological data and saving in a WDM or text file. File formats supported include WDM, HSPF Binary (.hbn), USGS RDB, and T...

Production and Uses of Multi-Decade Geodetic Earth Science Data Records

NASA Astrophysics Data System (ADS)

Bock, Y.; Kedar, S.; Moore, A. W.; Fang, P.; Liu, Z.; Sullivan, A.; Argus, D. F.; Jiang, S.; Marshall, S. T.

2017-12-01

The Solid Earth Science ESDR System (SESES) project funded under the NASA MEaSUREs program produces and disseminates mature, long-term, calibrated and validated, GNSS based Earth Science Data Records (ESDRs) that encompass multiple diverse areas of interest in Earth Science, such as tectonic motion, transient slip and earthquake dynamics, as well as meteorology, climate, and hydrology. The ESDRs now span twenty-five years for the earliest stations and today are available for thousands of global and regional stations. Using a unified metadata database and a combination of GNSS solutions generated by two independent analysis centers, the project currently produces four long-term ESDR's: Geodetic Displacement Time Series: Daily, combined, cleaned and filtered, GIPSY and GAMIT long-term time series of continuous GPS station positions (global and regional) in the latest version of ITRF, automatically updated weekly. Geodetic Velocities: Weekly updated velocity field + velocity field histories in various reference frames; compendium of all model parameters including earthquake catalog, coseismic offsets, and postseismic model parameters (exponential or logarithmic). Troposphere Delay Time Series: Long-term time series of troposphere delay (30-min resolution) at geodetic stations, necessarily estimated during position time series production and automatically updated weekly. Seismogeodetic records for historic earthquakes: High-rate broadband displacement and seismic velocity time series combining 1 Hz GPS displacements and 100 Hz accelerometer data for select large earthquakes and collocated cGPS and seismic instruments from regional networks. We present several recent notable examples of the ESDR's usage: A transient slip study that uses the combined position time series to unravel "tremor-less" slow tectonic transient events. Fault geometry determination from geodetic slip rates. Changes in water resources across California's physiographic provinces at a spatial resolution of 75 km. Retrospective study of a southern California summer monsoon event.

Noise analysis of GPS time series in Taiwan

NASA Astrophysics Data System (ADS)

Lee, You-Chia; Chang, Wu-Lung

2017-04-01

Global positioning system (GPS) usually used for researches of plate tectonics and crustal deformation. In most studies, GPS time series considered only time-independent noises (white noise), but time-dependent noises (flicker noise, random walk noise) which were found by nearly twenty years are also important to the precision of data. The rate uncertainties of stations will be underestimated if the GPS time series are assumed only time-independent noise. Therefore studying the noise properties of GPS time series is necessary in order to realize the precision and reliability of velocity estimates. The lengths of our GPS time series are from over 500 stations around Taiwan with time spans longer than 2.5 years up to 20 years. The GPS stations include different monument types such as deep drill braced, roof, metal tripod, and concrete pier, and the most common type in Taiwan is the metal tripod. We investigated the noise properties of continuous GPS time series by using the spectral index and amplitude of the power law noise. During the process we first remove the data outliers, and then estimate linear trend, size of offsets, and seasonal signals, and finally the amplitudes of the power-law and white noise are estimated simultaneously. Our preliminary results show that the noise amplitudes of the north component are smaller than that of the other two components, and the largest amplitudes are in the vertical. We also find that the amplitudes of white noise and power-law noises are positively correlated in three components. Comparisons of noise amplitudes of different monument types in Taiwan reveal that the deep drill braced monuments have smaller data uncertainties and therefore are more stable than other monuments.

A tool for NDVI time series extraction from wide-swath remotely sensed images

NASA Astrophysics Data System (ADS)

Li, Zhishan; Shi, Runhe; Zhou, Cong

2015-09-01

Normalized Difference Vegetation Index (NDVI) is one of the most widely used indicators for monitoring the vegetation coverage in land surface. The time series features of NDVI are capable of reflecting dynamic changes of various ecosystems. Calculating NDVI via Moderate Resolution Imaging Spectrometer (MODIS) and other wide-swath remotely sensed images provides an important way to monitor the spatial and temporal characteristics of large-scale NDVI. However, difficulties are still existed for ecologists to extract such information correctly and efficiently because of the problems in several professional processes on the original remote sensing images including radiometric calibration, geometric correction, multiple data composition and curve smoothing. In this study, we developed an efficient and convenient online toolbox for non-remote sensing professionals who want to extract NDVI time series with a friendly graphic user interface. It is based on Java Web and Web GIS technically. Moreover, Struts, Spring and Hibernate frameworks (SSH) are integrated in the system for the purpose of easy maintenance and expansion. Latitude, longitude and time period are the key inputs that users need to provide, and the NDVI time series are calculated automatically.

Spectral-decomposition techniques for the identification of radon anomalies temporally associated with earthquakes occurring in the UK in 2002 and 2008.

NASA Astrophysics Data System (ADS)

Crockett, R. G. M.; Gillmore, G. K.

2009-04-01

During the second half of 2002, the University of Northampton Radon Research Group operated two continuous hourly-sampling radon detectors 2.25 km apart in Northampton, in the (English) East Midlands. This period included the Dudley earthquake (22/09/2002) which was widely noticed by members of the public in the Northampton area. Also, at various periods during 2008 the Group has operated another pair of continuous hourly-sampling radon detectors similar distances apart in Northampton. One such period included the Market Rasen earthquake (27/02/2008) which was also widely noticed by members of the public in the Northampton area. During each period of monitoring, two time-series of radon readings were obtained, one from each detector. These have been analysed for evidence of simultaneous similar anomalies: the premise being that big disturbances occurring at big distances (in relation to the detector separation) should produce simultaneous similar anomalies but that simultaneous anomalies occurring by chance will be dissimilar. As previously reported, cross-correlating the two 2002 time-series over periods of 1-30 days duration, rolled forwards through the time-series at one-hour intervals produced two periods of significant correlation, i.e. two periods of simultaneous similar behaviour in the radon concentrations. One of these periods corresponded in time to the Dudley earthquake, the other corresponded in time to a smaller earthquake which occurred in the English Channel (26/08/2002). We here report subsequent investigation of the 2002 time-series and the 2008 time-series using spectral-decomposition techniques. These techniques have revealed additional simultaneous similar behaviour in the two radon concentrations, not revealed by the rolling correlation on the raw data. These correspond in time to the Manchester earthquake swarm of October 2002 and the Market Rasen earthquake of February 2008. The spectral-decomposition techniques effectively ‘de-noise' the data, and also remove lower-frequency variations (e.g. tidal variations), revealing the simultaneous similarities. Whilst this is very much work in progress, there is the potential that such techniques enhance the possibility that simultaneous real-time monitoring of radon levels - for short-term simultaneous anomalies - at several locations in earthquake areas might provide the core of an earthquake prediction method. Keywords: Radon; earthquakes; time series; cross-correlation; spectral-decomposition; real-time simultaneous monitoring.

A Markovian Entropy Measure for the Analysis of Calcium Activity Time Series.

PubMed

Marken, John P; Halleran, Andrew D; Rahman, Atiqur; Odorizzi, Laura; LeFew, Michael C; Golino, Caroline A; Kemper, Peter; Saha, Margaret S

2016-01-01

Methods to analyze the dynamics of calcium activity often rely on visually distinguishable features in time series data such as spikes, waves, or oscillations. However, systems such as the developing nervous system display a complex, irregular type of calcium activity which makes the use of such methods less appropriate. Instead, for such systems there exists a class of methods (including information theoretic, power spectral, and fractal analysis approaches) which use more fundamental properties of the time series to analyze the observed calcium dynamics. We present a new analysis method in this class, the Markovian Entropy measure, which is an easily implementable calcium time series analysis method which represents the observed calcium activity as a realization of a Markov Process and describes its dynamics in terms of the level of predictability underlying the transitions between the states of the process. We applied our and other commonly used calcium analysis methods on a dataset from Xenopus laevis neural progenitors which displays irregular calcium activity and a dataset from murine synaptic neurons which displays activity time series that are well-described by visually-distinguishable features. We find that the Markovian Entropy measure is able to distinguish between biologically distinct populations in both datasets, and that it can separate biologically distinct populations to a greater extent than other methods in the dataset exhibiting irregular calcium activity. These results support the benefit of using the Markovian Entropy measure to analyze calcium dynamics, particularly for studies using time series data which do not exhibit easily distinguishable features.

Stochastic model stationarization by eliminating the periodic term and its effect on time series prediction

NASA Astrophysics Data System (ADS)

Moeeni, Hamid; Bonakdari, Hossein; Fatemi, Seyed Ehsan

2017-04-01

Because time series stationarization has a key role in stochastic modeling results, three methods are analyzed in this study. The methods are seasonal differencing, seasonal standardization and spectral analysis to eliminate the periodic effect on time series stationarity. First, six time series including 4 streamflow series and 2 water temperature series are stationarized. The stochastic term for these series obtained with ARIMA is subsequently modeled. For the analysis, 9228 models are introduced. It is observed that seasonal standardization and spectral analysis eliminate the periodic term completely, while seasonal differencing maintains seasonal correlation structures. The obtained results indicate that all three methods present acceptable performance overall. However, model accuracy in monthly streamflow prediction is higher with seasonal differencing than with the other two methods. Another advantage of seasonal differencing over the other methods is that the monthly streamflow is never estimated as negative. Standardization is the best method for predicting monthly water temperature although it is quite similar to seasonal differencing, while spectral analysis performed the weakest in all cases. It is concluded that for each monthly seasonal series, seasonal differencing is the best stationarization method in terms of periodic effect elimination. Moreover, the monthly water temperature is predicted with more accuracy than monthly streamflow. The criteria of the average stochastic term divided by the amplitude of the periodic term obtained for monthly streamflow and monthly water temperature were 0.19 and 0.30, 0.21 and 0.13, and 0.07 and 0.04 respectively. As a result, the periodic term is more dominant than the stochastic term for water temperature in the monthly water temperature series compared to streamflow series.

Surface Area Analysis Using the Brunauer-Emmett-Teller (BET) Method: Standard Operating Procedure Series: SOP-C

DTIC Science & Technology

2016-09-01

Method Scientific Operating Procedure Series : SOP-C En vi ro nm en ta l L ab or at or y Jonathon Brame and Chris Griggs September 2016...BET) Method Scientific Operating Procedure Series : SOP-C Jonathon Brame and Chris Griggs Environmental Laboratory U.S. Army Engineer Research and...response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing

Characterization of Vertical Impact Device Acceleration Pulses Using Parametric Assessment: Phase IV Dual Impact Pulses

DTIC Science & Technology

2017-01-04

response, including the time for reviewing instructions, searching existing data sources, searching existing data sources, gathering and maintaining...configurations with a restrained manikin, was evaluated in four different test series . Test Series 1 was conducted to determine the materials and...5 ms TTP. Test Series 2 was conducted to determine the materials and drop heights required for energy attenuation of the seat pan to generate a 4 m

Orogen-Wide InSAR Time Series for Detecting Deformation Sources: The Zagros and Makran of Southern Iran

NASA Astrophysics Data System (ADS)

Lohman, R. B.; Barnhart, W. D.

2011-12-01

We present interferometric synthetic aperture radar (InSAR) time series maps that span the eastern Zagros (Fars Arc) collisional belt and western Makran accretionary prism of Southern Iran. Given the upcoming availability of large volumes of SAR data from new platforms, such as Sentinel 1 and potentially DESDynI, we explore computationally efficient approaches for extracting deformation time series when the signal of interest is small compared to the level of noise in individual interferograms. We use 12 descending and 2 ascending multi-frame (2-4 frames) Envisat tracks and 2 ascending ALOS tracks spanning 2003-2010 and 2006-2010. We implement a linear inversion, similar to the Small Baseline Subset (SBaS) technique, to derive surface displacements at individual acquisition dates from trees of interferograms with perpendicular baselines less than 350m for Envisat and 1500m for ALOS pairs. This spatially extensive dataset allows us to investigate several attributes of interferometry that vary spatially and temporally over large distances, including changes in phase coherence relative to elevation and relief as well as land use. Through synthetic tests and observed data, we explore various sources of potential error in calculation of time series, including variable coherence of pixels between interferograms in a single track, ambiguities in phase unwrapping, and orbital ramp estimation over scenes with variable correlated noise structure. We present examples of detected signals with both temporally variable characteristics and small magnitudes, including surface/subsurface salt deformation, aseismic deformation across Minab-Zendan-Palami strike-slip zone, and subsidence due to hydrocarbon extraction.

Timescales for determining temperature and dissolved oxygen trends in the Long Island Sound (LIS) estuary

NASA Astrophysics Data System (ADS)

Staniec, Allison; Vlahos, Penny

2017-12-01

Long-term time series represent a critical part of the oceanographic community's efforts to discern natural and anthropogenically forced variations in the environment. They provide regular measurements of climate relevant indicators including temperature, oxygen concentrations, and salinity. When evaluating time series, it is essential to isolate long-term trends from autocorrelation in data and noise due to natural variability. Herein we apply a statistical approach, well-established in atmospheric time series, to key parameters in the U.S. east coast's Long Island Sound estuary (LIS). Analysis shows that the LIS time series (established in the early 1990s) is sufficiently long to detect significant trends in physical-chemical parameters including temperature (T) and dissolved oxygen (DO). Over the last two decades, overall (combined surface and deep) LIS T has increased at an average rate of 0.08 ± 0.03 °C yr-1 while overall DO has dropped at an average rate of 0.03 ± 0.01 mg L-1yr-1 since 1994 at the 95% confidence level. This trend is notably faster than the global open ocean T trend (0.01 °C yr-1), as might be expected for a shallower estuarine system. T and DO trends were always significant for the existing time series using four month data increments. Rates of change of DO and T in LIS are strongly correlated and the rate of decrease of DO concentrations is consistent with the expected reduced solubility of DO at these higher temperatures. Thus, changes in T alone, across decadal timescales can account for between 33 and 100% of the observed decrease in DO. This has significant implications for other dissolved gases and the long-term management of LIS hypoxia.

Perl Modules for Constructing Iterators

NASA Technical Reports Server (NTRS)

Tilmes, Curt

2009-01-01

The Iterator Perl Module provides a general-purpose framework for constructing iterator objects within Perl, and a standard API for interacting with those objects. Iterators are an object-oriented design pattern where a description of a series of values is used in a constructor. Subsequent queries can request values in that series. These Perl modules build on the standard Iterator framework and provide iterators for some other types of values. Iterator::DateTime constructs iterators from DateTime objects or Date::Parse descriptions and ICal/RFC 2445 style re-currence descriptions. It supports a variety of input parameters, including a start to the sequence, an end to the sequence, an Ical/RFC 2445 recurrence describing the frequency of the values in the series, and a format description that can refine the presentation manner of the DateTime. Iterator::String constructs iterators from string representations. This module is useful in contexts where the API consists of supplying a string and getting back an iterator where the specific iteration desired is opaque to the caller. It is of particular value to the Iterator::Hash module which provides nested iterations. Iterator::Hash constructs iterators from Perl hashes that can include multiple iterators. The constructed iterators will return all the permutations of the iterations of the hash by nested iteration of embedded iterators. A hash simply includes a set of keys mapped to values. It is a very common data structure used throughout Perl programming. The Iterator:: Hash module allows a hash to include strings defining iterators (parsed and dispatched with Iterator::String) that are used to construct an overall series of hash values.

Analysis and Forecasting of Shoreline Position

NASA Astrophysics Data System (ADS)

Barton, C. C.; Tebbens, S. F.

2007-12-01

Analysis of historical shoreline positions on sandy coasts, in the geologic record, and study of sea-level rise curves reveals that the dynamics of the underlying processes produce temporal/spatial signals that exhibit power scaling and are therefore self-affine fractals. Self-affine time series signals can be quantified over many orders of magnitude in time and space in terms of persistence, a measure of the degree of correlation between adjacent values in the stochastic portion of a time series. Fractal statistics developed for self-affine time series are used to forecast a probability envelope bounding future shoreline positions. The envelope provides the standard deviation as a function of three variables: persistence, a constant equal to the value of the power spectral density when 1/period equals 1, and the number of time increments. The persistence of a twenty-year time series of the mean-high-water (MHW) shoreline positions was measured for four profiles surveyed at Duck, NC at the Field Research Facility (FRF) by the U.S. Army Corps of Engineers. The four MHW shoreline time series signals are self-affine with persistence ranging between 0.8 and 0.9, which indicates that the shoreline position time series is weakly persistent (where zero is uncorrelated), and has highly varying trends for all time intervals sampled. Forecasts of a probability envelope for future MHW positions are made for the 20 years of record and beyond to 50 years from the start of the data records. The forecasts describe the twenty-year data sets well and indicate that within a 96% confidence envelope, future decadal MHW shoreline excursions should be within 14.6 m of the position at the start of data collection. This is a stable-oscillatory shoreline. The forecasting method introduced here includes the stochastic portion of the time series while the traditional method of predicting shoreline change reduces the time series to a linear trend line fit to historic shoreline positions and extrapolated linearly to forecast future positions with a linearly increasing mean that breaks the confidence envelope eight years into the future and continues to increase. The traditional method is a poor representation of the observed shoreline position time series and is a poor basis for extrapolating future shoreline positions.

Creating high-resolution time series land-cover classifications in rapidly changing forested areas with BULC-U in Google Earth Engine

NASA Astrophysics Data System (ADS)

Cardille, J. A.; Lee, J.

2017-12-01

With the opening of the Landsat archive, there is a dramatically increased potential for creating high-quality time series of land use/land-cover (LULC) classifications derived from remote sensing. Although LULC time series are appealing, their creation is typically challenging in two fundamental ways. First, there is a need to create maximally correct LULC maps for consideration at each time step; and second, there is a need to have the elements of the time series be consistent with each other, without pixels that flip improbably between covers due only to unavoidable, stray classification errors. We have developed the Bayesian Updating of Land Cover - Unsupervised (BULC-U) algorithm to address these challenges simultaneously, and introduce and apply it here for two related but distinct purposes. First, with minimal human intervention, we produced an internally consistent, high-accuracy LULC time series in rapidly changing Mato Grosso, Brazil for a time interval (1986-2000) in which cropland area more than doubled. The spatial and temporal resolution of the 59 LULC snapshots allows users to witness the establishment of towns and farms at the expense of forest. The new time series could be used by policy-makers and analysts to unravel important considerations for conservation and management, including the timing and location of past development, the rate and nature of changes in forest connectivity, the connection with road infrastructure, and more. The second application of BULC-U is to sharpen the well-known GlobCover 2009 classification from 300m to 30m, while improving accuracy measures for every class. The greatly improved resolution and accuracy permits a better representation of the true LULC proportions, the use of this map in models, and quantification of the potential impacts of changes. Given that there may easily be thousands and potentially millions of images available to harvest for an LULC time series, it is imperative to build useful algorithms requiring minimal human intervention. Through image segmentation and classification, BULC-U allows us to use both the spectral and spatial characteristics of imagery to sharpen classifications and create time series. It is hoped that this study may allow us and other users of this new method to consider time series across ever larger areas.

Time series analysis of patients seeking orthodontic treatment at Seoul National University Dental Hospital over the past decade

PubMed Central

Lim, Hyun-Woo; Park, Ji-Hoon; Park, Hyun-Hee

2017-01-01

Objective This paper describes changes in the characteristics of patients seeking orthodontic treatment over the past decade and the treatment they received, to identify any seasonal variations or trends. Methods This single-center retrospective cohort study included all patients who presented to Seoul National University Dental Hospital for orthodontic diagnosis and treatment between January 1, 2005 and December 31, 2015. The study analyzed a set of heterogeneous variables grouped into the following categories: demographic (age, gender, and address), clinical (Angle Classification, anomaly, mode of orthodontic treatment, removable appliances for Phase 1 treatment, fixed appliances for Phase 2 treatment, orthognathic surgery, extraction, mini-plate, mini-implant, and patient transfer) and time-related variables (date of first visit and orthodontic treatment time). Time series analysis was applied to each variable. Results The sample included 14,510 patients with a median age of 19.5 years. The number of patients and their ages demonstrated a clear seasonal variation, which peaked in the summer and winter. Increasing trends were observed for the proportion of male patients, use of non-extraction treatment modality, use of ceramic brackets, patients from provinces outside the Seoul region at large, patients transferred from private practitioners, and patients who underwent orthognathic surgery performed by university surgeons. Decreasing trends included the use of metal brackets and orthodontic treatment time. Conclusions Time series analysis revealed a seasonal variation in some characteristics, and several variables showed changing trends over the past decade. PMID:28861391

Taylor Series Trajectory Calculations Including Oblateness Effects and Variable Atmospheric Density

NASA Technical Reports Server (NTRS)

Scott, James R.

2011-01-01

Taylor series integration is implemented in NASA Glenn's Spacecraft N-body Analysis Program, and compared head-to-head with the code's existing 8th- order Runge-Kutta Fehlberg time integration scheme. This paper focuses on trajectory problems that include oblateness and/or variable atmospheric density. Taylor series is shown to be significantly faster and more accurate for oblateness problems up through a 4x4 field, with speedups ranging from a factor of 2 to 13. For problems with variable atmospheric density, speedups average 24 for atmospheric density alone, and average 1.6 to 8.2 when density and oblateness are combined.

Studies in Astronomical Time Series Analysis. VI. Bayesian Block Representations

NASA Technical Reports Server (NTRS)

Scargle, Jeffrey D.; Norris, Jay P.; Jackson, Brad; Chiang, James

2013-01-01

This paper addresses the problem of detecting and characterizing local variability in time series and other forms of sequential data. The goal is to identify and characterize statistically significant variations, at the same time suppressing the inevitable corrupting observational errors. We present a simple nonparametric modeling technique and an algorithm implementing it-an improved and generalized version of Bayesian Blocks [Scargle 1998]-that finds the optimal segmentation of the data in the observation interval. The structure of the algorithm allows it to be used in either a real-time trigger mode, or a retrospective mode. Maximum likelihood or marginal posterior functions to measure model fitness are presented for events, binned counts, and measurements at arbitrary times with known error distributions. Problems addressed include those connected with data gaps, variable exposure, extension to piece- wise linear and piecewise exponential representations, multivariate time series data, analysis of variance, data on the circle, other data modes, and dispersed data. Simulations provide evidence that the detection efficiency for weak signals is close to a theoretical asymptotic limit derived by [Arias-Castro, Donoho and Huo 2003]. In the spirit of Reproducible Research [Donoho et al. (2008)] all of the code and data necessary to reproduce all of the figures in this paper are included as auxiliary material.

Development and Testing of Data Mining Algorithms for Earth Observation

NASA Technical Reports Server (NTRS)

Glymour, Clark

2005-01-01

The new algorithms developed under this project included a principled procedure for classification of objects, events or circumstances according to a target variable when a very large number of potential predictor variables is available but the number of cases that can be used for training a classifier is relatively small. These "high dimensional" problems require finding a minimal set of variables -called the Markov Blanket-- sufficient for predicting the value of the target variable. An algorithm, the Markov Blanket Fan Search, was developed, implemented and tested on both simulated and real data in conjunction with a graphical model classifier, which was also implemented. Another algorithm developed and implemented in TETRAD IV for time series elaborated on work by C. Granger and N. Swanson, which in turn exploited some of our earlier work. The algorithms in question learn a linear time series model from data. Given such a time series, the simultaneous residual covariances, after factoring out time dependencies, may provide information about causal processes that occur more rapidly than the time series representation allow, so called simultaneous or contemporaneous causal processes. Working with A. Monetta, a graduate student from Italy, we produced the correct statistics for estimating the contemporaneous causal structure from time series data using the TETRAD IV suite of algorithms. Two economists, David Bessler and Kevin Hoover, have independently published applications using TETRAD style algorithms to the same purpose. These implementations and algorithmic developments were separately used in two kinds of studies of climate data: Short time series of geographically proximate climate variables predicting agricultural effects in California, and longer duration climate measurements of temperature teleconnections.

Constructing the reduced dynamical models of interannual climate variability from spatial-distributed time series

NASA Astrophysics Data System (ADS)

Mukhin, Dmitry; Gavrilov, Andrey; Loskutov, Evgeny; Feigin, Alexander

2016-04-01

We suggest a method for empirical forecast of climate dynamics basing on the reconstruction of reduced dynamical models in a form of random dynamical systems [1,2] derived from observational time series. The construction of proper embedding - the set of variables determining the phase space the model works in - is no doubt the most important step in such a modeling, but this task is non-trivial due to huge dimension of time series of typical climatic fields. Actually, an appropriate expansion of observational time series is needed yielding the number of principal components considered as phase variables, which are to be efficient for the construction of low-dimensional evolution operator. We emphasize two main features the reduced models should have for capturing the main dynamical properties of the system: (i) taking into account time-lagged teleconnections in the atmosphere-ocean system and (ii) reflecting the nonlinear nature of these teleconnections. In accordance to these principles, in this report we present the methodology which includes the combination of a new way for the construction of an embedding by the spatio-temporal data expansion and nonlinear model construction on the basis of artificial neural networks. The methodology is aplied to NCEP/NCAR reanalysis data including fields of sea level pressure, geopotential height, and wind speed, covering Northern Hemisphere. Its efficiency for the interannual forecast of various climate phenomena including ENSO, PDO, NAO and strong blocking event condition over the mid latitudes, is demonstrated. Also, we investigate the ability of the models to reproduce and predict the evolution of qualitative features of the dynamics, such as spectral peaks, critical transitions and statistics of extremes. This research was supported by the Government of the Russian Federation (Agreement No. 14.Z50.31.0033 with the Institute of Applied Physics RAS) [1] Y. I. Molkov, E. M. Loskutov, D. N. Mukhin, and A. M. Feigin, "Random dynamical models from time series," Phys. Rev. E, vol. 85, no. 3, p. 036216, 2012. [2] D. Mukhin, D. Kondrashov, E. Loskutov, A. Gavrilov, A. Feigin, and M. Ghil, "Predicting Critical Transitions in ENSO models. Part II: Spatially Dependent Models," J. Clim., vol. 28, no. 5, pp. 1962-1976, 2015.

Updating stand-level forest inventories using airborne laser scanning and Landsat time series data

NASA Astrophysics Data System (ADS)

Bolton, Douglas K.; White, Joanne C.; Wulder, Michael A.; Coops, Nicholas C.; Hermosilla, Txomin; Yuan, Xiaoping

2018-04-01

Vertical forest structure can be mapped over large areas by combining samples of airborne laser scanning (ALS) data with wall-to-wall spatial data, such as Landsat imagery. Here, we use samples of ALS data and Landsat time-series metrics to produce estimates of top height, basal area, and net stem volume for two timber supply areas near Kamloops, British Columbia, Canada, using an imputation approach. Both single-year and time series metrics were calculated from annual, gap-free Landsat reflectance composites representing 1984-2014. Metrics included long-term means of vegetation indices, as well as measures of the variance and slope of the indices through time. Terrain metrics, generated from a 30 m digital elevation model, were also included as predictors. We found that imputation models improved with the inclusion of Landsat time series metrics when compared to single-year Landsat metrics (relative RMSE decreased from 22.8% to 16.5% for top height, from 32.1% to 23.3% for basal area, and from 45.6% to 34.1% for net stem volume). Landsat metrics that characterized 30-years of stand history resulted in more accurate models (for all three structural attributes) than Landsat metrics that characterized only the most recent 10 or 20 years of stand history. To test model transferability, we compared imputed attributes against ALS-based estimates in nearby forest blocks (>150,000 ha) that were not included in model training or testing. Landsat-imputed attributes correlated strongly to ALS-based estimates in these blocks (R2 = 0.62 and relative RMSE = 13.1% for top height, R2 = 0.75 and relative RMSE = 17.8% for basal area, and R2 = 0.67 and relative RMSE = 26.5% for net stem volume), indicating model transferability. These findings suggest that in areas containing spatially-limited ALS data acquisitions, imputation models, and Landsat time series and terrain metrics can be effectively used to produce wall-to-wall estimates of key inventory attributes, providing an opportunity to update estimates of forest attributes in areas where inventory information is either out of date or non-existent.

Monitoring the Effects of Forest Restoration Treatments on Post-Fire Vegetation Recovery with MODIS Multitemporal Data

PubMed Central

van Leeuwen, Willem J. D.

2008-01-01

This study examines how satellite based time-series vegetation greenness data and phenological measurements can be used to monitor and quantify vegetation recovery after wildfire disturbances and examine how pre-fire fuel reduction restoration treatments impact fire severity and impact vegetation recovery trajectories. Pairs of wildfire affected sites and a nearby unburned reference site were chosen to measure the post-disturbance recovery in relation to climate variation. All site pairs were chosen in forested uplands in Arizona and were restricted to the area of the Rodeo-Chediski fire that occurred in 2002. Fuel reduction treatments were performed in 1999 and 2001. The inter-annual and seasonal vegetation dynamics before, during, and after wildfire events can be monitored using a time series of biweekly composited MODIS NDVI (Moderate Resolution Imaging Spectroradiometer - Normalized Difference Vegetation Index) data. Time series analysis methods included difference metrics, smoothing filters, and fitting functions that were applied to extract seasonal and inter-annual change and phenological metrics from the NDVI time series data from 2000 to 2007. Pre- and post-fire Landsat data were used to compute the Normalized Burn Ratio (NBR) and examine burn severity at the selected sites. The phenological metrics (pheno-metrics) included the timing and greenness (i.e. NDVI) for the start, peak and end of the growing season as well as proxy measures for the rate of green-up and senescence and the annual vegetation productivity. Pre-fire fuel reduction treatments resulted in lower fire severity, which reduced annual productivity much less than untreated areas within the Rodeo-Chediski fire perimeter. The seasonal metrics were shown to be useful for estimating the rate of post-fire disturbance recovery and the timing of phenological greenness phases. The use of satellite time series NDVI data and derived pheno-metrics show potential for tracking vegetation cover dynamics and successional changes in response to drought, wildfire disturbances, and forest restoration treatments in fire-suppressed forests. PMID:27879809

31 CFR 353.42 - Nonreceipt or loss of check issued in payment.

Code of Federal Regulations, 2012 CFR

2012-07-01

... DEFINITIVE UNITED STATES SAVINGS BONDS, SERIES EE AND HH General Provisions for Payment § 353.42 Nonreceipt... received within a reasonable time or is lost after receipt, notice should be given to the same agency to... payment and describe the bond by series, denomination, serial number, and registration, including the...

31 CFR 353.42 - Nonreceipt or loss of check issued in payment.

Code of Federal Regulations, 2013 CFR

2013-07-01

... DEFINITIVE UNITED STATES SAVINGS BONDS, SERIES EE AND HH General Provisions for Payment § 353.42 Nonreceipt... received within a reasonable time or is lost after receipt, notice should be given to the same agency to... payment and describe the bond by series, denomination, serial number, and registration, including the...

31 CFR 353.42 - Nonreceipt or loss of check issued in payment.

Code of Federal Regulations, 2011 CFR

2011-07-01

... DEFINITIVE UNITED STATES SAVINGS BONDS, SERIES EE AND HH General Provisions for Payment § 353.42 Nonreceipt... received within a reasonable time or is lost after receipt, notice should be given to the same agency to... payment and describe the bond by series, denomination, serial number, and registration, including the...

31 CFR 353.42 - Nonreceipt or loss of check issued in payment.

Code of Federal Regulations, 2010 CFR

2010-07-01

... DEFINITIVE UNITED STATES SAVINGS BONDS, SERIES EE AND HH General Provisions for Payment § 353.42 Nonreceipt... received within a reasonable time or is lost after receipt, notice should be given to the same agency to... payment and describe the bond by series, denomination, serial number, and registration, including the...

31 CFR 353.42 - Nonreceipt or loss of check issued in payment.

Code of Federal Regulations, 2014 CFR

2014-07-01

... DEFINITIVE UNITED STATES SAVINGS BONDS, SERIES EE AND HH General Provisions for Payment § 353.42 Nonreceipt... received within a reasonable time or is lost after receipt, notice should be given to the same agency to... payment and describe the bond by series, denomination, serial number, and registration, including the...

26 CFR 1.761-1 - Terms defined.

Code of Federal Regulations, 2013 CFR

2013-04-01

... including any extension of time) prescribed by law for the filing of the partnership return. As to any... in a partnership by means of a distribution, or a series of distributions, to the partner by the partnership. A series of distributions will come within the meaning of this term whether they are made in one...

26 CFR 1.761-1 - Terms defined.

Code of Federal Regulations, 2012 CFR

2012-04-01

... including any extension of time) prescribed by law for the filing of the partnership return. As to any... in a partnership by means of a distribution, or a series of distributions, to the partner by the partnership. A series of distributions will come within the meaning of this term whether they are made in one...

Daily water and sediment discharges from selected rivers of the eastern United States; a time-series modeling approach

USGS Publications Warehouse

Fitzgerald, Michael G.; Karlinger, Michael R.

1983-01-01

Time-series models were constructed for analysis of daily runoff and sediment discharge data from selected rivers of the Eastern United States. Logarithmic transformation and first-order differencing of the data sets were necessary to produce second-order, stationary time series and remove seasonal trends. Cyclic models accounted for less than 42 percent of the variance in the water series and 31 percent in the sediment series. Analysis of the apparent oscillations of given frequencies occurring in the data indicates that frequently occurring storms can account for as much as 50 percent of the variation in sediment discharge. Components of the frequency analysis indicate that a linear representation is reasonable for the water-sediment system. Models that incorporate lagged water discharge as input prove superior to univariate techniques in modeling and prediction of sediment discharges. The random component of the models includes errors in measurement and model hypothesis and indicates no serial correlation. An index of sediment production within or between drain-gage basins can be calculated from model parameters.

Interrupted time series regression for the evaluation of public health interventions: a tutorial.

PubMed

Bernal, James Lopez; Cummins, Steven; Gasparrini, Antonio

2017-02-01

Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time. It is increasingly being used to evaluate the effectiveness of interventions ranging from clinical therapy to national public health legislation. Whereas the design shares many properties of regression-based approaches in other epidemiological studies, there are a range of unique features of time series data that require additional methodological considerations. In this tutorial we use a worked example to demonstrate a robust approach to ITS analysis using segmented regression. We begin by describing the design and considering when ITS is an appropriate design choice. We then discuss the essential, yet often omitted, step of proposing the impact model a priori. Subsequently, we demonstrate the approach to statistical analysis including the main segmented regression model. Finally we describe the main methodological issues associated with ITS analysis: over-dispersion of time series data, autocorrelation, adjusting for seasonal trends and controlling for time-varying confounders, and we also outline some of the more complex design adaptations that can be used to strengthen the basic ITS design.

Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data.

PubMed

Grootswagers, Tijl; Wardle, Susan G; Carlson, Thomas A

2017-04-01

Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analyzing fMRI data. Although decoding methods have been extensively applied in brain-computer interfaces, these methods have only recently been applied to time series neuroimaging data such as MEG and EEG to address experimental questions in cognitive neuroscience. In a tutorial style review, we describe a broad set of options to inform future time series decoding studies from a cognitive neuroscience perspective. Using example MEG data, we illustrate the effects that different options in the decoding analysis pipeline can have on experimental results where the aim is to "decode" different perceptual stimuli or cognitive states over time from dynamic brain activation patterns. We show that decisions made at both preprocessing (e.g., dimensionality reduction, subsampling, trial averaging) and decoding (e.g., classifier selection, cross-validation design) stages of the analysis can significantly affect the results. In addition to standard decoding, we describe extensions to MVPA for time-varying neuroimaging data including representational similarity analysis, temporal generalization, and the interpretation of classifier weight maps. Finally, we outline important caveats in the design and interpretation of time series decoding experiments.

Interrupted time series regression for the evaluation of public health interventions: a tutorial

PubMed Central

Bernal, James Lopez; Cummins, Steven; Gasparrini, Antonio

2017-01-01

Abstract Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time. It is increasingly being used to evaluate the effectiveness of interventions ranging from clinical therapy to national public health legislation. Whereas the design shares many properties of regression-based approaches in other epidemiological studies, there are a range of unique features of time series data that require additional methodological considerations. In this tutorial we use a worked example to demonstrate a robust approach to ITS analysis using segmented regression. We begin by describing the design and considering when ITS is an appropriate design choice. We then discuss the essential, yet often omitted, step of proposing the impact model a priori. Subsequently, we demonstrate the approach to statistical analysis including the main segmented regression model. Finally we describe the main methodological issues associated with ITS analysis: over-dispersion of time series data, autocorrelation, adjusting for seasonal trends and controlling for time-varying confounders, and we also outline some of the more complex design adaptations that can be used to strengthen the basic ITS design. PMID:27283160

Temporal relationships between awakening cortisol and psychosocial variables in inpatients with anorexia nervosa - A time series approach.

PubMed

Wild, Beate; Stadnitski, Tatjana; Wesche, Daniela; Stroe-Kunold, Esther; Schultz, Jobst-Hendrik; Rudofsky, Gottfried; Maser-Gluth, Christiane; Herzog, Wolfgang; Friederich, Hans-Christoph

2016-04-01

The aim of the study was to investigate the characteristics of the awakening salivary cortisol in patients with anorexia nervosa (AN) using a time series design. We included ten AN inpatients, six with a very low BMI (high symptom severity, HSS group) and four patients with less severe symptoms (low symptom severity, LSS group). Patients collected salivary cortisol daily upon awakening. The number of collected saliva samples varied across patients between n=65 and n=229 (due to the different lengths of their inpatient stay). In addition, before retiring, the patients answered questions daily on the handheld regarding disorder-related psychosocial variables. The analysis of cortisol and diary data was conducted by using a time series approach. Time series showed that the awakening cortisol of the AN patients was elevated as compared to a control group. Cortisol measurements of patients with LSS essentially fluctuated in a stationary manner around a constant mean. The series of patients with HSS were generally less stable; four HSS patients showed a non-stationary cortisol awakening series. Antipsychotic medication did not change awakening cortisol in a specific way. The lagged dependencies between cortisol and depressive feelings became significant for four patients. Here, higher cortisol values were temporally associated with higher values of depressive feelings. Upon awakening, the cortisol of all AN patients was in the standard range but elevated as compared to healthy controls. Patients with HSS appeared to show less stable awakening cortisol time series compared to patients with LSS. Copyright © 2016 Elsevier B.V. All rights reserved.

Comparison of missing value imputation methods in time series: the case of Turkish meteorological data

NASA Astrophysics Data System (ADS)

Yozgatligil, Ceylan; Aslan, Sipan; Iyigun, Cem; Batmaz, Inci

2013-04-01

This study aims to compare several imputation methods to complete the missing values of spatio-temporal meteorological time series. To this end, six imputation methods are assessed with respect to various criteria including accuracy, robustness, precision, and efficiency for artificially created missing data in monthly total precipitation and mean temperature series obtained from the Turkish State Meteorological Service. Of these methods, simple arithmetic average, normal ratio (NR), and NR weighted with correlations comprise the simple ones, whereas multilayer perceptron type neural network and multiple imputation strategy adopted by Monte Carlo Markov Chain based on expectation-maximization (EM-MCMC) are computationally intensive ones. In addition, we propose a modification on the EM-MCMC method. Besides using a conventional accuracy measure based on squared errors, we also suggest the correlation dimension (CD) technique of nonlinear dynamic time series analysis which takes spatio-temporal dependencies into account for evaluating imputation performances. Depending on the detailed graphical and quantitative analysis, it can be said that although computational methods, particularly EM-MCMC method, are computationally inefficient, they seem favorable for imputation of meteorological time series with respect to different missingness periods considering both measures and both series studied. To conclude, using the EM-MCMC algorithm for imputing missing values before conducting any statistical analyses of meteorological data will definitely decrease the amount of uncertainty and give more robust results. Moreover, the CD measure can be suggested for the performance evaluation of missing data imputation particularly with computational methods since it gives more precise results in meteorological time series.

Time-dependent calculations of transfer ionization by fast proton-helium collision in one-dimensional kinematics

NASA Astrophysics Data System (ADS)

Serov, Vladislav V.; Kheifets, A. S.

2014-12-01

We analyze a transfer ionization (TI) reaction in the fast proton-helium collision H++He →H0+He2 ++ e- by solving a time-dependent Schrödinger equation (TDSE) under the classical projectile motion approximation in one-dimensional kinematics. In addition, we construct various time-independent analogs of our model using lowest-order perturbation theory in the form of the Born series. By comparing various aspects of the TDSE and the Born series calculations, we conclude that the recent discrepancies of experimental and theoretical data may be attributed to deficiency of the Born models used by other authors. We demonstrate that the correct Born series for TI should include the momentum-space overlap between the double-ionization amplitude and the wave function of the transferred electron.

tsiR: An R package for time-series Susceptible-Infected-Recovered models of epidemics.

PubMed

Becker, Alexander D; Grenfell, Bryan T

2017-01-01

tsiR is an open source software package implemented in the R programming language designed to analyze infectious disease time-series data. The software extends a well-studied and widely-applied algorithm, the time-series Susceptible-Infected-Recovered (TSIR) model, to infer parameters from incidence data, such as contact seasonality, and to forward simulate the underlying mechanistic model. The tsiR package aggregates a number of different fitting features previously described in the literature in a user-friendly way, providing support for their broader adoption in infectious disease research. Also included in tsiR are a number of diagnostic tools to assess the fit of the TSIR model. This package should be useful for researchers analyzing incidence data for fully-immunizing infectious diseases.

Time Series Analysis of Photovoltaic Soiling Station Data: Version 1.0, August 2017

DOE Office of Scientific and Technical Information (OSTI.GOV)

Micheli, Leonardo; Muller, Matthew T.; Deceglie, Michael G.

The time series data from PV soiling stations, operating in the USA, at different time periods are analyzed and presented. The current version of the paper includes twenty stations operating between 2013 and 2016, but the paper is intended to be periodically updated as more stations and more data become available. The challenges in working with soiling stations data are discussed, including measurement methodology, quality controls, and measurement uncertainty. The soiling profiles of the soiling stations are made available so that the PV community can make use of this data to guide operations and maintence decisions, estimate soiling derate inmore » performance models, and more generally come to a better understanding of the challenges associated with the variability of PV soiling.« less

Improvements to surrogate data methods for nonstationary time series.

PubMed

Lucio, J H; Valdés, R; Rodríguez, L R

2012-05-01

The method of surrogate data has been extensively applied to hypothesis testing of system linearity, when only one realization of the system, a time series, is known. Normally, surrogate data should preserve the linear stochastic structure and the amplitude distribution of the original series. Classical surrogate data methods (such as random permutation, amplitude adjusted Fourier transform, or iterative amplitude adjusted Fourier transform) are successful at preserving one or both of these features in stationary cases. However, they always produce stationary surrogates, hence existing nonstationarity could be interpreted as dynamic nonlinearity. Certain modifications have been proposed that additionally preserve some nonstationarity, at the expense of reproducing a great deal of nonlinearity. However, even those methods generally fail to preserve the trend (i.e., global nonstationarity in the mean) of the original series. This is the case of time series with unit roots in their autoregressive structure. Additionally, those methods, based on Fourier transform, either need first and last values in the original series to match, or they need to select a piece of the original series with matching ends. These conditions are often inapplicable and the resulting surrogates are adversely affected by the well-known artefact problem. In this study, we propose a simple technique that, applied within existing Fourier-transform-based methods, generates surrogate data that jointly preserve the aforementioned characteristics of the original series, including (even strong) trends. Moreover, our technique avoids the negative effects of end mismatch. Several artificial and real, stationary and nonstationary, linear and nonlinear time series are examined, in order to demonstrate the advantages of the methods. Corresponding surrogate data are produced with the classical and with the proposed methods, and the results are compared.

Reference manual for generation and analysis of Habitat Time Series: version II

USGS Publications Warehouse

Milhous, Robert T.; Bartholow, John M.; Updike, Marlys A.; Moos, Alan R.

1990-01-01

The selection of an instream flow requirement for water resource management often requires the review of how the physical habitat changes through time. This review is referred to as 'Time Series Analysis." The Tune Series Library (fSLIB) is a group of programs to enter, transform, analyze, and display time series data for use in stream habitat assessment. A time series may be defined as a sequence of data recorded or calculated over time. Examples might be historical monthly flow, predicted monthly weighted usable area, daily electrical power generation, annual irrigation diversion, and so forth. The time series can be analyzed, both descriptively and analytically, to understand the importance of the variation in the events over time. This is especially useful in the development of instream flow needs based on habitat availability. The TSLIB group of programs assumes that you have an adequate study plan to guide you in your analysis. You need to already have knowledge about such things as time period and time step, species and life stages to consider, and appropriate comparisons or statistics to be produced and displayed or tabulated. Knowing your destination, you must first evaluate whether TSLIB can get you there. Remember, data are not answers. This publication is a reference manual to TSLIB and is intended to be a guide to the process of using the various programs in TSLIB. This manual is essentially limited to the hands-on use of the various programs. a TSLIB use interface program (called RTSM) has been developed to provide an integrated working environment where the use has a brief on-line description of each TSLIB program with the capability to run the TSLIB program while in the user interface. For information on the RTSM program, refer to Appendix F. Before applying the computer models described herein, it is recommended that the user enroll in the short course "Problem Solving with the Instream Flow Incremental Methodology (IFIM)." This course is offered by the Aquatic Systems Branch of the National Ecology Research Center. For more information about the TSLIB software, refer to the Memorandum of Understanding. Chapter 1 provides a brief introduction to the Instream Flow Incremental Methodology and TSLIB. Other chapters in this manual provide information on the different aspects of using the models. The information contained in the other chapters includes (2) acquisition, entry, manipulation, and listing of streamflow data; (3) entry, manipulation, and listing of the habitat-versus-streamflow function; (4) transferring streamflow data; (5) water resources systems analysis; (6) generation and analysis of daily streamflow and habitat values; (7) generation of the time series of monthly habitats; (8) manipulation, analysis, and display of month time series data; and (9) generation, analysis, and display of annual time series data. Each section includes documentation for the programs therein with at least one page of information for each program, including a program description, instructions for running the program, and sample output. The Appendixes contain the following: (A) sample file formats; (B) descriptions of default filenames; (C) alphabetical summary of batch-procedure files; (D) installing and running TSLIB on a microcomputer; (E) running TSLIB on a CDC Cyber computer; (F) using the TSLIB user interface program (RTSM); and (G) running WATSTORE on the USGS Amdahl mainframe computer. The number for this version of TSLIB--Version II-- is somewhat arbitrary, as the TSLIB programs were collected into a library some time ago; but operators tended to use and manage them as individual programs. Therefore, we will consider the group of programs from the past that were only on the CDC Cyber computer as Version 0; the programs from the past that were on both the Cyber and the IBM-compatible microcomputer as Version I; and the programs contained in this reference manual as Version II.

GIAnT - Generic InSAR Analysis Toolbox

NASA Astrophysics Data System (ADS)

Agram, P.; Jolivet, R.; Riel, B. V.; Simons, M.; Doin, M.; Lasserre, C.; Hetland, E. A.

2012-12-01

We present a computing framework for studying the spatio-temporal evolution of ground deformation from interferometric synthetic aperture radar (InSAR) data. Several open-source tools including Repeat Orbit Interferometry PACkage (ROI-PAC) and InSAR Scientific Computing Environment (ISCE) from NASA-JPL, and Delft Object-oriented Repeat Interferometric Software (DORIS), have enabled scientists to generate individual interferograms from raw radar data with relative ease. Numerous computational techniques and algorithms that reduce phase information from multiple interferograms to a deformation time-series have been developed and verified over the past decade. However, the sharing and direct comparison of products from multiple processing approaches has been hindered by - 1) absence of simple standards for sharing of estimated time-series products, 2) use of proprietary software tools with license restrictions and 3) the closed source nature of the exact implementation of many of these algorithms. We have developed this computing framework to address all of the above issues. We attempt to take the first steps towards creating a community software repository for InSAR time-series analysis. To date, we have implemented the short baseline subset algorithm (SBAS), NSBAS and multi-scale interferometric time-series (MInTS) in this framework and the associated source code is included in the GIAnT distribution. A number of the associated routines have been optimized for performance and scalability with large data sets. Some of the new features in our processing framework are - 1) the use of daily solutions from continuous GPS stations to correct for orbit errors, 2) the use of meteorological data sets to estimate the tropospheric delay screen and 3) a data-driven bootstrapping approach to estimate the uncertainties associated with estimated time-series products. We are currently working on incorporating tidal load corrections for individual interferograms and propagation of noise covariance models through the processing chain for robust estimation of uncertainties in the deformation estimates. We will demonstrate the ease of use of our framework with results ranging from regional scale analysis around Long Valley, CA and Parkfield, CA to continental scale analysis in Western South America. We will also present preliminary results from a new time-series approach that simultaneously estimates deformation over the complete spatial domain at all time epochs on a distributed computing platform. GIAnT has been developed entirely using open source tools and uses Python as the underlying platform. We build on the extensive numerical (NumPy) and scientific (SciPy) computing Python libraries to develop an object-oriented, flexible and modular framework for time-series InSAR applications. The toolbox is currently configured to work with outputs from ROI-PAC, ISCE and DORIS, but can easily be extended to support products from other SAR/InSAR processors. The toolbox libraries include support for hierarchical data format (HDF5) memory mapped files, parallel processing with Python's multi-processing module and support for many convex optimization solvers like CSDP, CVXOPT etc. An extensive set of routines to deal with ASCII and XML files has also been included for controlling the processing parameters.

XDATA

DTIC Science & Technology

2017-12-01

6028 Date Cleared: 30 NOV 2017 13. SUPPLEMENTARY NOTES 14. ABSTRACT Data analysis tools which operate on varied data sources including time series ...public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions...and raw detections from geo-located tweets Micro-paths (10M) (No distance/ time filter) Raw Tracks (10M) Raw Detections (10M) APPROVED FOR PUBLIC

Quantifying and Reducing Uncertainty in Correlated Multi-Area Short-Term Load Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sun, Yannan; Hou, Zhangshuan; Meng, Da

2016-07-17

In this study, we represent and reduce the uncertainties in short-term electric load forecasting by integrating time series analysis tools including ARIMA modeling, sequential Gaussian simulation, and principal component analysis. The approaches are mainly focusing on maintaining the inter-dependency between multiple geographically related areas. These approaches are applied onto cross-correlated load time series as well as their forecast errors. Multiple short-term prediction realizations are then generated from the reduced uncertainty ranges, which are useful for power system risk analyses.

Multi-Instrument Investigation of Ionospheric Flow Channels and Their Impact on the Ionosphere and Thermosphere during Geomagnetic Storms

DTIC Science & Technology

2017-12-29

indicated as shaded intervals in cyan) is shown in the context of the 5-6 August 2011 storm energetics. These are depicted by the time series of [b...of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing   data sources...documented in a series of journal articles [Horvath and Lovell, 2017A; 2017B; 2017C; 2017D]. Our findings contribute to the better understanding of

Rapid on-site defibrillation versus community program.

PubMed

Fedoruk, J C; Paterson, D; Hlynka, M; Fung, K Y; Gobet, Michael; Currie, Wayne

2002-01-01

For patients who suffer out-of-hospital cardiac arrest, the time from collapse to initial defibrillation is the single most important factor that affects survival to hospital discharge. The purpose of this study was to compare the survival rates of cardiac arrest victims within an institution that has a rapid defibrillation program with those of its own urban community, tiered EMS system. A logistic regression analysis of a retrospective data series (n = 23) and comparative analysis to a second retrospective data series (n = 724) were gathered for the study period September 1994 to September 1999. The first data series included all persons at Casino Windsor who suffered a cardiac arrest. Data collected included: age, gender, death/survival (neurologically intact discharge), presenting rhythm (ventricular fibrillation (VF), ventricular tachycardia (VT), or other), time of collapse, time to arrival of security personnel, time to initiation of cardiopulmonary resuscitation (CPR) prior to defibrillation (when applicable), time to arrival of staff nurse, time to initial defibrillation, and time to return of spontaneous circulation (if any). Significantly, all arrests within this series were witnessed by the surveillance camera systems, allowing time of collapse to be accurately determined rather than estimated. These data were compared to those of similar events, times, and intervals for all patients in the greater Windsor area who suffered cardiac arrest. This second series was based upon the Ontario Prehospital Advanced Life Support (OPALS) Study database, as coordinated by the Clinical Epidemiology Unit of the Ottawa Hospital, University of Ottawa. The Casino Windsor had 23 cases of cardiac arrests. Of the cases, 13 (56.5%) were male and 10 (43.5%) were female. All cases (100%) were witnessed. The average of the ages was 61.1 years, of the time to initial defibrillation was 7.7 minutes, and of the time for EMS to reach the patient was 13.3 minutes. The presenting rhythm was VF/VT in 91% of the case. Fifteen patients were discharged alive from hospital for a 65% survival rate. The Greater Windsor Study area included 668 cases of out-of-hospital cardiac arrest: Of these, 410 (61.4%) were male and 258 (38.6%) were female, 365 (54.6%) were witnessed, and 303 (45.4%) were not witnessed. The initial rhythm was VF/VT was in 34.3%. Thirty-seven (5.5%) were discharged alive from the hospital. This study provides further evidence that PAD Programs may enhance cardiac arrest survival rates and should be considered for any venue with large numbers of adults as well as areas with difficult medical access.

High Resolution Time Series of Plankton Communities: From Early Warning of Harmful Blooms to Sentinels of Climate Change

NASA Astrophysics Data System (ADS)

Sosik, H. M.; Campbell, L.; Olson, R. J.

2016-02-01

The combination of ocean observatory infrastructure and automated submersible flow cytometry provides an unprecedented capability for sustained high resolution time series of plankton, including taxa that are harmful or early indicators of ecosystem response to environmental change. On-going time series produced with the FlowCytobot series of instruments document important ways this challenge is already being met for phytoplankton and microzooplankton. FlowCytobot and Imaging FlowCytobot use a combination of laser-based scattering and fluorescence measurements and video imaging of individual particles to enumerate and characterize cells ranging from picocyanobacteria to large chaining-forming diatoms. Over a decade of observations at the Martha's Vineyard Coastal Observatory (MVCO), a cabled facility on the New England Shelf, have been compiled from repeated instrument deployments, typically 6 months or longer in duration. These multi-year high resolution (hourly to daily) time series are providing new insights into dynamics of community structure such as blooms, seasonality, and multi-year trends linked to regional climate-related variables. Similar observations in Texas coastal waters at the Texas Observatory for Algal Succession Time series (TOAST) have repeatedly provided early warning of harmful algal bloom events that threaten human and ecosystem health. As coastal ocean observing systems mature and expand, the continued integration of these type of detailed observations of the plankton will provide unparalleled information about variability and patterns of change at the base of the marine food webs, with direct implications for informed ecosystem-based management.

Hazard function theory for nonstationary natural hazards

NASA Astrophysics Data System (ADS)

Read, L.; Vogel, R. M.

2015-12-01

Studies from the natural hazards literature indicate that many natural processes, including wind speeds, landslides, wildfires, precipitation, streamflow and earthquakes, show evidence of nonstationary behavior such as trends in magnitudes through time. Traditional probabilistic analysis of natural hazards based on partial duration series (PDS) generally assumes stationarity in the magnitudes and arrivals of events, i.e. that the probability of exceedance is constant through time. Given evidence of trends and the consequent expected growth in devastating impacts from natural hazards across the world, new methods are needed to characterize their probabilistic behavior. The field of hazard function analysis (HFA) is ideally suited to this problem because its primary goal is to describe changes in the exceedance probability of an event over time. HFA is widely used in medicine, manufacturing, actuarial statistics, reliability engineering, economics, and elsewhere. HFA provides a rich theory to relate the natural hazard event series (x) with its failure time series (t), enabling computation of corresponding average return periods and reliabilities associated with nonstationary event series. This work investigates the suitability of HFA to characterize nonstationary natural hazards whose PDS magnitudes are assumed to follow the widely applied Poisson-GP model. We derive a 2-parameter Generalized Pareto hazard model and demonstrate how metrics such as reliability and average return period are impacted by nonstationarity and discuss the implications for planning and design. Our theoretical analysis linking hazard event series x, with corresponding failure time series t, should have application to a wide class of natural hazards.

West Africa land use and land cover time series

USGS Publications Warehouse

Cotillon, Suzanne E.

2017-02-16

Started in 1999, the West Africa Land Use Dynamics project represents an effort to map land use and land cover, characterize the trends in time and space, and understand their effects on the environment across West Africa. The outcome of the West Africa Land Use Dynamics project is the production of a three-time period (1975, 2000, and 2013) land use and land cover dataset for the Sub-Saharan region of West Africa, including the Cabo Verde archipelago. The West Africa Land Use Land Cover Time Series dataset offers a unique basis for characterizing and analyzing land changes across the region, systematically and at an unprecedented level of detail.

Functional clustering of time series gene expression data by Granger causality

PubMed Central

2012-01-01

Background A common approach for time series gene expression data analysis includes the clustering of genes with similar expression patterns throughout time. Clustered gene expression profiles point to the joint contribution of groups of genes to a particular cellular process. However, since genes belong to intricate networks, other features, besides comparable expression patterns, should provide additional information for the identification of functionally similar genes. Results In this study we perform gene clustering through the identification of Granger causality between and within sets of time series gene expression data. Granger causality is based on the idea that the cause of an event cannot come after its consequence. Conclusions This kind of analysis can be used as a complementary approach for functional clustering, wherein genes would be clustered not solely based on their expression similarity but on their topological proximity built according to the intensity of Granger causality among them. PMID:23107425

Complex Rotation Quantum Dynamic Neural Networks (CRQDNN) using Complex Quantum Neuron (CQN): Applications to time series prediction.

PubMed

Cui, Yiqian; Shi, Junyou; Wang, Zili

2015-11-01

Quantum Neural Networks (QNN) models have attracted great attention since it innovates a new neural computing manner based on quantum entanglement. However, the existing QNN models are mainly based on the real quantum operations, and the potential of quantum entanglement is not fully exploited. In this paper, we proposes a novel quantum neuron model called Complex Quantum Neuron (CQN) that realizes a deep quantum entanglement. Also, a novel hybrid networks model Complex Rotation Quantum Dynamic Neural Networks (CRQDNN) is proposed based on Complex Quantum Neuron (CQN). CRQDNN is a three layer model with both CQN and classical neurons. An infinite impulse response (IIR) filter is embedded in the Networks model to enable the memory function to process time series inputs. The Levenberg-Marquardt (LM) algorithm is used for fast parameter learning. The networks model is developed to conduct time series predictions. Two application studies are done in this paper, including the chaotic time series prediction and electronic remaining useful life (RUL) prediction. Copyright © 2015 Elsevier Ltd. All rights reserved.

Long-Term Stability of Radio Sources in VLBI Analysis

NASA Technical Reports Server (NTRS)

Engelhardt, Gerald; Thorandt, Volkmar

2010-01-01

Positional stability of radio sources is an important requirement for modeling of only one source position for the complete length of VLBI data of presently more than 20 years. The stability of radio sources can be verified by analyzing time series of radio source coordinates. One approach is a statistical test for normal distribution of residuals to the weighted mean for each radio source component of the time series. Systematic phenomena in the time series can thus be detected. Nevertheless, an inspection of rate estimation and weighted root-mean-square (WRMS) variations about the mean is also necessary. On the basis of the time series computed by the BKG group in the frame of the ICRF2 working group, 226 stable radio sources with an axis stability of 10 as could be identified. They include 100 ICRF2 axes-defining sources which are determined independently of the method applied in the ICRF2 working group. 29 stable radio sources with a source structure index of less than 3.0 can also be used to increase the number of 295 ICRF2 defining sources.

Refining Markov state models for conformational dynamics using ensemble-averaged data and time-series trajectories

NASA Astrophysics Data System (ADS)

Matsunaga, Y.; Sugita, Y.

2018-06-01

A data-driven modeling scheme is proposed for conformational dynamics of biomolecules based on molecular dynamics (MD) simulations and experimental measurements. In this scheme, an initial Markov State Model (MSM) is constructed from MD simulation trajectories, and then, the MSM parameters are refined using experimental measurements through machine learning techniques. The second step can reduce the bias of MD simulation results due to inaccurate force-field parameters. Either time-series trajectories or ensemble-averaged data are available as a training data set in the scheme. Using a coarse-grained model of a dye-labeled polyproline-20, we compare the performance of machine learning estimations from the two types of training data sets. Machine learning from time-series data could provide the equilibrium populations of conformational states as well as their transition probabilities. It estimates hidden conformational states in more robust ways compared to that from ensemble-averaged data although there are limitations in estimating the transition probabilities between minor states. We discuss how to use the machine learning scheme for various experimental measurements including single-molecule time-series trajectories.

A Markovian Entropy Measure for the Analysis of Calcium Activity Time Series

PubMed Central

Rahman, Atiqur; Odorizzi, Laura; LeFew, Michael C.; Golino, Caroline A.; Kemper, Peter; Saha, Margaret S.

2016-01-01

Methods to analyze the dynamics of calcium activity often rely on visually distinguishable features in time series data such as spikes, waves, or oscillations. However, systems such as the developing nervous system display a complex, irregular type of calcium activity which makes the use of such methods less appropriate. Instead, for such systems there exists a class of methods (including information theoretic, power spectral, and fractal analysis approaches) which use more fundamental properties of the time series to analyze the observed calcium dynamics. We present a new analysis method in this class, the Markovian Entropy measure, which is an easily implementable calcium time series analysis method which represents the observed calcium activity as a realization of a Markov Process and describes its dynamics in terms of the level of predictability underlying the transitions between the states of the process. We applied our and other commonly used calcium analysis methods on a dataset from Xenopus laevis neural progenitors which displays irregular calcium activity and a dataset from murine synaptic neurons which displays activity time series that are well-described by visually-distinguishable features. We find that the Markovian Entropy measure is able to distinguish between biologically distinct populations in both datasets, and that it can separate biologically distinct populations to a greater extent than other methods in the dataset exhibiting irregular calcium activity. These results support the benefit of using the Markovian Entropy measure to analyze calcium dynamics, particularly for studies using time series data which do not exhibit easily distinguishable features. PMID:27977764

Mapping Wetlands of Dongting Lake in China Using Landsat and SENTINEL-1 Time Series at 30M

NASA Astrophysics Data System (ADS)

Xing, L.; Tang, X.; Wang, H.; Fan, W.; Gao, X.

2018-04-01

Mapping and monitoring wetlands of Dongting lake using optical sensor data has been limited by cloud cover, and open access Sentinal-1 C-band data could provide cloud-free SAR images with both have high spatial and temporal resolution, which offer new opportunities for monitoring wetlands. In this study, we combined optical data and SAR data to map wetland of Dongting Lake reserves in 2016. Firstly, we generated two monthly composited Landsat land surface reflectance, NDVI, NDWI, TC-Wetness time series and Sentinel-1 (backscattering coefficient for VH and VV) time series. Secondly, we derived surface water body with two monthly frequencies based on the threshold method using the Sentinel-1 time series. Then the permanent water and seasonal water were separated by the submergence ratio. Other land cover types were identified based on SVM classifier using Landsat time series. Results showed that (1) the overall accuracies and kappa coefficients were above 86.6 % and 0.8. (3) Natural wetlands including permanent water body (14.8 %), seasonal water body (34.6 %), and permanent marshes (10.9 %) were the main land cover types, accounting for 60.3 % of the three wetland reserves. Human-made wetlands, such as rice fields, accounted 34.3 % of the total area. Generally, this study proposed a new flowchart for wetlands mapping in Dongting lake by combining multi-source remote sensing data, and the use of the two-monthly composited optical time series effectively made up the missing data due to the clouds and increased the possibility of precise wetlands classification.

Nonlinear time-series-based adaptive control applications

NASA Technical Reports Server (NTRS)

Mohler, R. R.; Rajkumar, V.; Zakrzewski, R. R.

1991-01-01

A control design methodology based on a nonlinear time-series reference model is presented. It is indicated by highly nonlinear simulations that such designs successfully stabilize troublesome aircraft maneuvers undergoing large changes in angle of attack as well as large electric power transients due to line faults. In both applications, the nonlinear controller was significantly better than the corresponding linear adaptive controller. For the electric power network, a flexible AC transmission system with series capacitor power feedback control is studied. A bilinear autoregressive moving average reference model is identified from system data, and the feedback control is manipulated according to a desired reference state. The control is optimized according to a predictive one-step quadratic performance index. A similar algorithm is derived for control of rapid changes in aircraft angle of attack over a normally unstable flight regime. In the latter case, however, a generalization of a bilinear time-series model reference includes quadratic and cubic terms in angle of attack.

Using diurnal temperature signals to infer vertical groundwater-surface water exchange

USGS Publications Warehouse

Irvine, Dylan J.; Briggs, Martin A.; Lautz, Laura K.; Gordon, Ryan P.; McKenzie, Jeffrey M.; Cartwright, Ian

2017-01-01

Heat is a powerful tracer to quantify fluid exchange between surface water and groundwater. Temperature time series can be used to estimate pore water fluid flux, and techniques can be employed to extend these estimates to produce detailed plan-view flux maps. Key advantages of heat tracing include cost-effective sensors and ease of data collection and interpretation, without the need for expensive and time-consuming laboratory analyses or induced tracers. While the collection of temperature data in saturated sediments is relatively straightforward, several factors influence the reliability of flux estimates that are based on time series analysis (diurnal signals) of recorded temperatures. Sensor resolution and deployment are particularly important in obtaining robust flux estimates in upwelling conditions. Also, processing temperature time series data involves a sequence of complex steps, including filtering temperature signals, selection of appropriate thermal parameters, and selection of the optimal analytical solution for modeling. This review provides a synthesis of heat tracing using diurnal temperature oscillations, including details on optimal sensor selection and deployment, data processing, model parameterization, and an overview of computing tools available. Recent advances in diurnal temperature methods also provide the opportunity to determine local saturated thermal diffusivity, which can improve the accuracy of fluid flux modeling and sensor spacing, which is related to streambed scour and deposition. These parameters can also be used to determine the reliability of flux estimates from the use of heat as a tracer.

Mapping forest height, foliage height profiles and disturbance characteristics with time series of gap-filled Landsat and ALI imagery

NASA Astrophysics Data System (ADS)

Helmer, E.; Ruzycki, T. S.; Wunderle, J. M.; Kwit, C.; Ewert, D. N.; Voggesser, S. M.; Brandeis, T. J.

2011-12-01

We mapped tropical dry forest height (RMSE = 0.9 m, R2 = 0.84, range 0.6-7 m) and foliage height profiles with a time series of gap-filled Landsat and Advanced Land Imager (ALI) imagery for the island of Eleuthera, The Bahamas. We also mapped disturbance type and age with decision tree classification of the image time series. Having mapped these variables in the context of studies of wintering habitat of an endangered Nearctic-Neotropical migrant bird, the Kirtland's Warbler (Dendroica kirtlandii), we then illustrated relationships between forest vertical structure, disturbance type and counts of forage species important to the Kirtland's Warbler. The ALI imagery and the Landsat time series were both critical to the result for forest height, which the strong relationship of forest height with disturbance type and age facilitated. Also unique to this study was that seven of the eight image time steps were cloud-gap-filled images: mosaics of the clear parts of several cloudy scenes, in which cloud gaps in a reference scene for each time step are filled with image data from alternate scenes. We created each cloud-cleared image, including a virtually seamless ALI image mosaic, with regression tree normalization of the image data that filled cloud gaps. We also illustrated how viewing time series imagery as red-green-blue composites of tasseled cap wetness (RGB wetness composites) aids reference data collection for classifying tropical forest disturbance type and age.

Petroleum supply monthly, April 1991. [Glossary included

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1991-04-29

Data presented in the PSM (Petroleum Supply Monthly) describe the supply and disposition of petroleum products in the United States and major US geographic regions. The data series describe production, imports and exports, inter-Petroleum Administration for Defense (PAD) District movements, and inventories by the primary suppliers of petroleum products in the United States (50 States and the District of Columbia). The reporting universe includes those petroleum sectors in Primary Supply. Included are: petroleum refiners, motor gasoline blenders, operators of natural gas processing plants and fractionators, inter-PAD transporters, importers, and major inventory holders of petroleum products and crude oil. When aggregated,more » the data reported by these sectors approximately represent the consumption of petroleum products in the United States. The tables and figures in the Summary Statistics section of the PSM present a time series of selected petroleum data on a US level. Most time series include preliminary estimates for one month. The Detailed Statistics tables of the PSM present statistics for the most current month available as well as year-to-date. Industry terminology and product definitions are listed alphabetically in the Glossary. 14 figs., 65 tabs.« less

38 CFR 36.4202 - Definitions.

Code of Federal Regulations, 2012 CFR

2012-07-01

... § 36.4202 Definitions. Wherever used in 38 U.S.C. 3712 or the § 36.4200 series, unless the context... § 36.4200 series, which have been paid and debited to the loan account. Unpaid late charges may not be included in the indebtedness. Lender. The payee or assignee or transferee of an obligation at the time it...

38 CFR 36.4202 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... § 36.4202 Definitions. Wherever used in 38 U.S.C. 3712 or the § 36.4200 series, unless the context... § 36.4200 series, which have been paid and debited to the loan account. Unpaid late charges may not be included in the indebtedness. Lender. The payee or assignee or transferee of an obligation at the time it...

GPS data exploration for seismologists and geodesists

NASA Astrophysics Data System (ADS)

Webb, F.; Bock, Y.; Kedar, S.; Dong, D.; Jamason, P.; Chang, R.; Prawirodirdjo, L.; MacLeod, I.; Wadsworth, G.

2007-12-01

Over the past decade, GPS and seismic networks spanning the western US plate boundaries have produced vast amounts of data that need to be made accessible to both the geodesy and seismology communities. Unlike seismic data, raw geodetic data requires significant processing before geophysical interpretations can be made. This requires the generation of data-products (time series, velocities and strain maps) and dissemination strategies to bridge these differences and assure efficient use of data across traditionally separate communities. "GPS DATA PRODUCTS FOR SOLID EARTH SCIENCE" (GDPSES) is a multi-year NASA funded project, designed to produce and deliver high quality GPS time series, velocities, and strain fields, derived from multiple GPS networks along the western US plate boundary, and to make these products easily accessible to geophysicists. Our GPS product dissemination is through modern web-based IT methodology. Product browsing is facilitated through a web tool known as GPS Explorer and continuous streams of GPS time series are provided using web services to the seismic archive, where it can be accessed by seismologists using traditional seismic data viewing and manipulation tools. GPS-Explorer enables users to efficiently browse several layers of data products from raw data through time series, velocities and strain by providing the user with a web interface, which seamlessly interacts with a continuously updated database of these data products through the use of web-services. The current archive contains GDPSES data products beginning in 1995, and includes observations from GPS stations in EarthScope's Plate Boundary Observatory (PBO), as well as from real-time real-time CGPS stations. The generic, standards-based approach used in this project enables GDPSES to seamlessly expand indefinitely to include other space-time-dependent data products from additional GPS networks. The prototype GPS-Explorer provides users with a personalized working environment in which the user may zoom in and access subsets of the data via web services. It provides users with a variety of interactive web tools interconnected in a portlet environment to explore and save datasets of interest to return to at a later date. At the same time the GPS time series are also made available through the seismic data archive, where the GPS networks are treated as regular seismic networks, whose data is made available in data formats used by seismic utilities such as SEED readers and SAC. A key challenge, stemming from the fundamental differences between seismic and geodetic time series, is the representation of reprocessed of GPS data in the seismic archive. As GPS processing algorithms evolve and their accuracy increases, a periodic complete recreation of the the GPS time series archive is necessary.

Time series modeling by a regression approach based on a latent process.

PubMed

Chamroukhi, Faicel; Samé, Allou; Govaert, Gérard; Aknin, Patrice

2009-01-01

Time series are used in many domains including finance, engineering, economics and bioinformatics generally to represent the change of a measurement over time. Modeling techniques may then be used to give a synthetic representation of such data. A new approach for time series modeling is proposed in this paper. It consists of a regression model incorporating a discrete hidden logistic process allowing for activating smoothly or abruptly different polynomial regression models. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. The M step of the EM algorithm uses a multi-class Iterative Reweighted Least-Squares (IRLS) algorithm to estimate the hidden process parameters. To evaluate the proposed approach, an experimental study on simulated data and real world data was performed using two alternative approaches: a heteroskedastic piecewise regression model using a global optimization algorithm based on dynamic programming, and a Hidden Markov Regression Model whose parameters are estimated by the Baum-Welch algorithm. Finally, in the context of the remote monitoring of components of the French railway infrastructure, and more particularly the switch mechanism, the proposed approach has been applied to modeling and classifying time series representing the condition measurements acquired during switch operations.

Falcon: Visual analysis of large, irregularly sampled, and multivariate time series data in additive manufacturing

DOE PAGES

Steed, Chad A.; Halsey, William; Dehoff, Ryan; ...

2017-02-16

Flexible visual analysis of long, high-resolution, and irregularly sampled time series data from multiple sensor streams is a challenge in several domains. In the field of additive manufacturing, this capability is critical for realizing the full potential of large-scale 3D printers. Here, we propose a visual analytics approach that helps additive manufacturing researchers acquire a deep understanding of patterns in log and imagery data collected by 3D printers. Our specific goals include discovering patterns related to defects and system performance issues, optimizing build configurations to avoid defects, and increasing production efficiency. We introduce Falcon, a new visual analytics system thatmore » allows users to interactively explore large, time-oriented data sets from multiple linked perspectives. Falcon provides overviews, detailed views, and unique segmented time series visualizations, all with adjustable scale options. To illustrate the effectiveness of Falcon at providing thorough and efficient knowledge discovery, we present a practical case study involving experts in additive manufacturing and data from a large-scale 3D printer. The techniques described are applicable to the analysis of any quantitative time series, though the focus of this paper is on additive manufacturing.« less

Falcon: Visual analysis of large, irregularly sampled, and multivariate time series data in additive manufacturing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steed, Chad A.; Halsey, William; Dehoff, Ryan

Flexible visual analysis of long, high-resolution, and irregularly sampled time series data from multiple sensor streams is a challenge in several domains. In the field of additive manufacturing, this capability is critical for realizing the full potential of large-scale 3D printers. Here, we propose a visual analytics approach that helps additive manufacturing researchers acquire a deep understanding of patterns in log and imagery data collected by 3D printers. Our specific goals include discovering patterns related to defects and system performance issues, optimizing build configurations to avoid defects, and increasing production efficiency. We introduce Falcon, a new visual analytics system thatmore » allows users to interactively explore large, time-oriented data sets from multiple linked perspectives. Falcon provides overviews, detailed views, and unique segmented time series visualizations, all with adjustable scale options. To illustrate the effectiveness of Falcon at providing thorough and efficient knowledge discovery, we present a practical case study involving experts in additive manufacturing and data from a large-scale 3D printer. The techniques described are applicable to the analysis of any quantitative time series, though the focus of this paper is on additive manufacturing.« less

A data mining framework for time series estimation.

PubMed

Hu, Xiao; Xu, Peng; Wu, Shaozhi; Asgari, Shadnaz; Bergsneider, Marvin

2010-04-01

Time series estimation techniques are usually employed in biomedical research to derive variables less accessible from a set of related and more accessible variables. These techniques are traditionally built from systems modeling approaches including simulation, blind decovolution, and state estimation. In this work, we define target time series (TTS) and its related time series (RTS) as the output and input of a time series estimation process, respectively. We then propose a novel data mining framework for time series estimation when TTS and RTS represent different sets of observed variables from the same dynamic system. This is made possible by mining a database of instances of TTS, its simultaneously recorded RTS, and the input/output dynamic models between them. The key mining strategy is to formulate a mapping function for each TTS-RTS pair in the database that translates a feature vector extracted from RTS to the dissimilarity between true TTS and its estimate from the dynamic model associated with the same TTS-RTS pair. At run time, a feature vector is extracted from an inquiry RTS and supplied to the mapping function associated with each TTS-RTS pair to calculate a dissimilarity measure. An optimal TTS-RTS pair is then selected by analyzing these dissimilarity measures. The associated input/output model of the selected TTS-RTS pair is then used to simulate the TTS given the inquiry RTS as an input. An exemplary implementation was built to address a biomedical problem of noninvasive intracranial pressure assessment. The performance of the proposed method was superior to that of a simple training-free approach of finding the optimal TTS-RTS pair by a conventional similarity-based search on RTS features. 2009 Elsevier Inc. All rights reserved.

When Should Zero Be Included on a Scale Showing Magnitude?

ERIC Educational Resources Information Center

Kozak, Marcin

2011-01-01

This article addresses an important problem of graphing quantitative data: should one include zero on the scale showing magnitude? Based on a real time series example, the problem is discussed and some recommendations are proposed.

A better understanding of long-range temporal dependence of traffic flow time series

NASA Astrophysics Data System (ADS)

Feng, Shuo; Wang, Xingmin; Sun, Haowei; Zhang, Yi; Li, Li

2018-02-01

Long-range temporal dependence is an important research perspective for modelling of traffic flow time series. Various methods have been proposed to depict the long-range temporal dependence, including autocorrelation function analysis, spectral analysis and fractal analysis. However, few researches have studied the daily temporal dependence (i.e. the similarity between different daily traffic flow time series), which can help us better understand the long-range temporal dependence, such as the origin of crossover phenomenon. Moreover, considering both types of dependence contributes to establishing more accurate model and depicting the properties of traffic flow time series. In this paper, we study the properties of daily temporal dependence by simple average method and Principal Component Analysis (PCA) based method. Meanwhile, we also study the long-range temporal dependence by Detrended Fluctuation Analysis (DFA) and Multifractal Detrended Fluctuation Analysis (MFDFA). The results show that both the daily and long-range temporal dependence exert considerable influence on the traffic flow series. The DFA results reveal that the daily temporal dependence creates crossover phenomenon when estimating the Hurst exponent which depicts the long-range temporal dependence. Furthermore, through the comparison of the DFA test, PCA-based method turns out to be a better method to extract the daily temporal dependence especially when the difference between days is significant.

[Gene method for inconsistent hydrological frequency calculation. I: Inheritance, variability and evolution principles of hydrological genes].

PubMed

Xie, Ping; Wu, Zi Yi; Zhao, Jiang Yan; Sang, Yan Fang; Chen, Jie

2018-04-01

A stochastic hydrological process is influenced by both stochastic and deterministic factors. A hydrological time series contains not only pure random components reflecting its inheri-tance characteristics, but also deterministic components reflecting variability characteristics, such as jump, trend, period, and stochastic dependence. As a result, the stochastic hydrological process presents complicated evolution phenomena and rules. To better understand these complicated phenomena and rules, this study described the inheritance and variability characteristics of an inconsistent hydrological series from two aspects: stochastic process simulation and time series analysis. In addition, several frequency analysis approaches for inconsistent time series were compared to reveal the main problems in inconsistency study. Then, we proposed a new concept of hydrological genes origined from biological genes to describe the inconsistent hydrolocal processes. The hydrologi-cal genes were constructed using moments methods, such as general moments, weight function moments, probability weight moments and L-moments. Meanwhile, the five components, including jump, trend, periodic, dependence and pure random components, of a stochastic hydrological process were defined as five hydrological bases. With this method, the inheritance and variability of inconsistent hydrological time series were synthetically considered and the inheritance, variability and evolution principles were fully described. Our study would contribute to reveal the inheritance, variability and evolution principles in probability distribution of hydrological elements.

Why didn't Box-Jenkins win (again)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pack, D.J.; Downing, D.J.

This paper focuses on the forecasting performance of the Box-Jenkins methodology applied to the 111 time series of the Makridakis competition. It considers the influence of the following factors: (1) time series length, (2) time-series information (autocorrelation) content, (3) time-series outliers or structural changes, (4) averaging results over time series, and (5) forecast time origin choice. It is found that the 111 time series contain substantial numbers of very short series, series with obvious structural change, and series whose histories are relatively uninformative. If these series are typical of those that one must face in practice, the real message ofmore » the competition is that univariate time series extrapolations will frequently fail regardless of the methodology employed to produce them.« less

Estimations of the Global Distribution and Time Series of UV Noontime Irradiance (305, 310, 324, 380 nm, and Erythemal) from TOMS and SeaWiFS Data

NASA Technical Reports Server (NTRS)

Herman, J.

2004-01-01

The amount of UV irradiance reaching the Earth's surface is estimated from the measured cloud reflectivity, ozone, aerosol amounts, and surface reflectivity time series from 1980 to 1992 and 1997 to 2000 to estimate changes that have occurred over a 21-year period. Recent analysis of the TOMS data shows that there has been an apparent increase in reflectivity (decrease in W) in the Southern Hemisphere that is related to a calibration error in EP-TOMS. Data from the well-calibrated SeaWiFS satellite instrument have been used to correct the EP-TOMS reflectivity and UV time series. After correction, some of the local trend features seen in the N7 time series (1980 to 1992) have been continued in the combined time series, but the overall zonal average and global trends have changed. In addition to correcting the EP-TOMS radiance calibration, the use of SeaWiFS cloud data permits estimation of UV irradiance at higher spatial resolution (1 to 4 km) than is available from TOMS (100 km) under the assumption that ozone is slowly varying over a scale of 100 km. The key results include a continuing decrease in cloud cover over Europe and North America with a corresponding increase in UV and a decrease in UV irradiance near Antarctica.

Road safety forecasts in five European countries using structural time series models.

PubMed

Antoniou, Constantinos; Papadimitriou, Eleonora; Yannis, George

2014-01-01

Modeling road safety development is a complex task and needs to consider both the quantifiable impact of specific parameters as well as the underlying trends that cannot always be measured or observed. The objective of this research is to apply structural time series models for obtaining reliable medium- to long-term forecasts of road traffic fatality risk using data from 5 countries with different characteristics from all over Europe (Cyprus, Greece, Hungary, Norway, and Switzerland). Two structural time series models are considered: (1) the local linear trend model and the (2) latent risk time series model. Furthermore, a structured decision tree for the selection of the applicable model for each situation (developed within the Road Safety Data, Collection, Transfer and Analysis [DaCoTA] research project, cofunded by the European Commission) is outlined. First, the fatality and exposure data that are used for the development of the models are presented and explored. Then, the modeling process is presented, including the model selection process, introduction of intervention variables, and development of mobility scenarios. The forecasts using the developed models appear to be realistic and within acceptable confidence intervals. The proposed methodology is proved to be very efficient for handling different cases of data availability and quality, providing an appropriate alternative from the family of structural time series models in each country. A concluding section providing perspectives and directions for future research is presented.

Multifractal analysis of visibility graph-based Ito-related connectivity time series.

PubMed

Czechowski, Zbigniew; Lovallo, Michele; Telesca, Luciano

2016-02-01

In this study, we investigate multifractal properties of connectivity time series resulting from the visibility graph applied to normally distributed time series generated by the Ito equations with multiplicative power-law noise. We show that multifractality of the connectivity time series (i.e., the series of numbers of links outgoing any node) increases with the exponent of the power-law noise. The multifractality of the connectivity time series could be due to the width of connectivity degree distribution that can be related to the exit time of the associated Ito time series. Furthermore, the connectivity time series are characterized by persistence, although the original Ito time series are random; this is due to the procedure of visibility graph that, connecting the values of the time series, generates persistence but destroys most of the nonlinear correlations. Moreover, the visibility graph is sensitive for detecting wide "depressions" in input time series.

Analyzing Single-Molecule Time Series via Nonparametric Bayesian Inference

PubMed Central

Hines, Keegan E.; Bankston, John R.; Aldrich, Richard W.

2015-01-01

The ability to measure the properties of proteins at the single-molecule level offers an unparalleled glimpse into biological systems at the molecular scale. The interpretation of single-molecule time series has often been rooted in statistical mechanics and the theory of Markov processes. While existing analysis methods have been useful, they are not without significant limitations including problems of model selection and parameter nonidentifiability. To address these challenges, we introduce the use of nonparametric Bayesian inference for the analysis of single-molecule time series. These methods provide a flexible way to extract structure from data instead of assuming models beforehand. We demonstrate these methods with applications to several diverse settings in single-molecule biophysics. This approach provides a well-constrained and rigorously grounded method for determining the number of biophysical states underlying single-molecule data. PMID:25650922

A Maple package for improved global mapping forecast

NASA Astrophysics Data System (ADS)

Carli, H.; Duarte, L. G. S.; da Mota, L. A. C. P.

2014-03-01

We present a Maple implementation of the well known global approach to time series analysis and some further developments designed to improve the computational efficiency of the forecasting capabilities of the approach. This global approach can be summarized as being a reconstruction of the phase space, based on a time ordered series of data obtained from the system. After that, using the reconstructed vectors, a portion of this space is used to produce a mapping, a polynomial fitting, through a minimization procedure, that represents the system and can be employed to forecast further entries for the series. In the present implementation, we introduce a set of commands, tools, in order to perform all these tasks. For example, the command VecTS deals mainly with the reconstruction of the vector in the phase space. The command GfiTS deals with producing the minimization and the fitting. ForecasTS uses all these and produces the prediction of the next entries. For the non-standard algorithms, we here present two commands: IforecasTS and NiforecasTS that, respectively deal with the one-step and the N-step forecasting. Finally, we introduce two further tools to aid the forecasting. The commands GfiTS and AnalysTS, basically, perform an analysis of the behavior of each portion of a series regarding the settings used on the commands just mentioned above. Catalogue identifier: AERW_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AERW_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 3001 No. of bytes in distributed program, including test data, etc.: 95018 Distribution format: tar.gz Programming language: Maple 14. Computer: Any capable of running Maple Operating system: Any capable of running Maple. Tested on Windows ME, Windows XP, Windows 7. RAM: 128 MB Classification: 4.3, 4.9, 5 Nature of problem: Time series analysis and improving forecast capability. Solution method: The method of solution is partially based on a result published in [1]. Restrictions: If the time series that is being analyzed presents a great amount of noise or if the dynamical system behind the time series is of high dimensionality (Dim≫3), then the method may not work well. Unusual features: Our implementation can, in the cases where the dynamics behind the time series is given by a system of low dimensionality, greatly improve the forecast. Running time: This depends strongly on the command that is being used. References: [1] Barbosa, L.M.C.R., Duarte, L.G.S., Linhares, C.A. and da Mota, L.A.C.P., Improving the global fitting method on nonlinear time series analysis, Phys. Rev. E 74, 026702 (2006).

Visualizing the intercity correlation of PM2.5 time series in the Beijing-Tianjin-Hebei region using ground-based air quality monitoring data.

PubMed

Liu, Jianzheng; Li, Weifeng; Wu, Jiansheng; Liu, Yonghong

2018-01-01

The Beijing-Tianjin-Hebei area faces a severe fine particulate matter (PM2.5) problem. To date, considerable progress has been made toward understanding the PM2.5 problem, including spatial-temporal characterization, driving factors, and health effects. However, little research has been done on the dynamic interactions and relationships between PM2.5 concentrations in different cities in this area. To address the research gap, this study discovered a phenomenon of time-lagged intercity correlations of PM2.5 time series and proposed a visualization framework based on this phenomenon to visualize the interaction in PM2.5 concentrations between cities. The visualizations produced using the framework show that there are significant time-lagged correlations between the PM2.5 time series in different cities in this area. The visualizations also show that the correlations are more significant in colder months and between cities that are closer, and that there are seasonal changes in the temporal order of the correlated PM2.5 time series. Further analysis suggests that the time-lagged intercity correlations of PM2.5 time series are most likely due to synoptic meteorological variations. We argue that the visualizations demonstrate the interactions of air pollution between cities in the Beijing-Tianjin-Hebei area and the significant effect of synoptic meteorological conditions on PM2.5 pollution. The visualization framework could help determine the pathway of regional transportation of air pollution and may also be useful in delineating the area of interaction of PM2.5 pollution for impact analysis.

Visualizing the intercity correlation of PM2.5 time series in the Beijing-Tianjin-Hebei region using ground-based air quality monitoring data

PubMed Central

Li, Weifeng; Wu, Jiansheng; Liu, Yonghong

2018-01-01

The Beijing-Tianjin-Hebei area faces a severe fine particulate matter (PM2.5) problem. To date, considerable progress has been made toward understanding the PM2.5 problem, including spatial-temporal characterization, driving factors, and health effects. However, little research has been done on the dynamic interactions and relationships between PM2.5 concentrations in different cities in this area. To address the research gap, this study discovered a phenomenon of time-lagged intercity correlations of PM2.5 time series and proposed a visualization framework based on this phenomenon to visualize the interaction in PM2.5 concentrations between cities. The visualizations produced using the framework show that there are significant time-lagged correlations between the PM2.5 time series in different cities in this area. The visualizations also show that the correlations are more significant in colder months and between cities that are closer, and that there are seasonal changes in the temporal order of the correlated PM2.5 time series. Further analysis suggests that the time-lagged intercity correlations of PM2.5 time series are most likely due to synoptic meteorological variations. We argue that the visualizations demonstrate the interactions of air pollution between cities in the Beijing-Tianjin-Hebei area and the significant effect of synoptic meteorological conditions on PM2.5 pollution. The visualization framework could help determine the pathway of regional transportation of air pollution and may also be useful in delineating the area of interaction of PM2.5 pollution for impact analysis. PMID:29438417

Time series analysis as input for clinical predictive modeling: Modeling cardiac arrest in a pediatric ICU

PubMed Central

2011-01-01

Background Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. Methods We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Results Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of candidate features; 9) training models for various data subsets; and 10) measuring model performance characteristics in unseen data to estimate their external validity. Conclusions We have proposed a ten step process that results in data sets that contain time series features and are suitable for predictive modeling by a number of methods. We illustrated the process through an example of cardiac arrest prediction in a pediatric intensive care setting. PMID:22023778

Time series analysis as input for clinical predictive modeling: modeling cardiac arrest in a pediatric ICU.

PubMed

Kennedy, Curtis E; Turley, James P

2011-10-24

Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of candidate features; 9) training models for various data subsets; and 10) measuring model performance characteristics in unseen data to estimate their external validity. We have proposed a ten step process that results in data sets that contain time series features and are suitable for predictive modeling by a number of methods. We illustrated the process through an example of cardiac arrest prediction in a pediatric intensive care setting.

The long-term changes in total ozone, as derived from Dobson measurements at Arosa (1948-2001)

NASA Astrophysics Data System (ADS)

Krzyscin, J. W.

2003-04-01

The longest possible total ozone time series (Arosa, Switzerland) is examined for a detection of trends. Two-step procedure is proposed to estimate the long-term (decadal) variations in the ozone time series. The first step consists of a standard least-squares multiple regression applied to the total ozone monthly means to parameterize "natural" (related to the oscillations in the atmospheric dynamics) variations in the analyzed time series. The standard proxies for the dynamical ozone variations are used including; the 11-year solar activity cycle, and indices of QBO, ENSO and NAO. We use the detrended time series of temperature at 100 hPa and 500 hPa over Arosa to parameterize short-term variations (with time periods<1 year) in total ozone related to local changes in the meteorological conditions over the station. The second step consists of a smooth-curve fitting to the total ozone residuals (original minus modeled "natural" time series), the time derivation applied to this curve to obtain local trends, and bootstrapping of the residual time series to estimate the standard error of local trends. Locally weighted regression and the wavelet analysis methodology are used to extract the smooth component out of the residual time series. The time integral over the local trend values provides the cumulative long-term change since the data beginning. Examining the pattern of the cumulative change we see the periods with total ozone loss (the end of 50s up to early 60s - probably the effect of the nuclear bomb tests), recovery (mid 60s up to beginning of 70s), apparent decrease (beginning of 70s lasting to mid 90s - probably the effect of the atmosphere contamination by anthropogenic substances containing chlorine), and with a kind of stabilization or recovery (starting in the mid of 90s - probably the effect of the Montreal protocol to eliminate substances reducing the ozone layer). We can also estimate that a full ozone recovery (return to the undisturbed total ozone level from the beginning of 70s) is expected around 2050. We propose to calculate both time series of local trends and the cumulative long-term change instead single trend value derived as a slope of straight line fit to the data.

A Combined Length-of-Day Series Spanning 1832-1997

NASA Technical Reports Server (NTRS)

Gross, Richard S.

1999-01-01

The Earth's rotation is not constant but exhibits minute changes on all observable time scales ranging from subdaily to secular. This rich spectrum of observed Earth rotation changes reflects the rich variety of astronomical and geophysical phenomena that are causing the Earth's rotation to change, including, but not limited to, ocean and solid body tides, atmospheric wind and pressure changes, oceanic current and sea level height changes, post-glacial rebound, and torques acting at the core-mantle boundary. In particular, the decadal-scale variations of the Earth's rotation are thought to be largely caused by interactions between the Earth's outer core and mantle. Comparing the inferred Earth rotation variations caused by the various core-mantle interactions to observed variations requires Earth rotation observations spanning decades, if not centuries. During the past century many different techniques have been used to observe the Earth's rotation. By combining the individual Earth rotation series determined by each of these techniques, a series of the Earth's rotation can be obtained that is based upon independent measurements spanning the greatest possible time interval. In this study, independent observations of the Earth's rotation are combined to generate a length-of-day series spanning 1832-1997. The observations combined include lunar occultation measurements spanning 1832-1955, optical astrometric measurements spanning 1956-1982, lunar laser ranging measurements spanning 1970-1997, and very long baseline interferometric measurements spanning 1978-1998. These series are combined using a Kalman filter developed at JPL for just this purpose. The resulting combined length-of-day series will be presented and compared with other available length-of-day series of similar duration.

Detection of deformation time-series in Miyake-jima using PALSAR/InSAR

NASA Astrophysics Data System (ADS)

Ozawa, T.; Ueda, H.

2010-12-01

Volcano deformation is often complicated temporally and spatially. Then deformation mapping by InSAR is useful to understand it in detail. However, InSAR is affected by the atmospheric, the ionospheric and other noises, and then we sometimes miss an important temporal change of deformation with a few cm. So we want to develop InSAR time-series analysis which detects volcano deformation precisely. Generally, the area of 10×10km which covers general volcano size is included in several SAR scenes obtained from different orbits or observation modes. First, interferograms are generated for each orbit path. In InSAR processing, the atmospheric noise reduction using the simulation from numerical weather model is used. Long wavelength noise due to orbit error and the ionospheric disturbance is corrected by adjusting to GPS deformation time-series, assuming it to be a plane. Next, we estimate deformation time-series from obtained interferograms. Radar incidence directions for each orbit path are different, but those for observation modes with 34.3° and 41.5° offnadir angles are almost included in one plane. Then slant-range change for all orbit paths can be described by the horizontal and the vertical components of its co-plane. Inversely, we estimate them for all epochs with the constraint that temporal change of deformation is smooth. Simultaneously, we estimate DEM error. As one of case studies, we present an application in Miyake-jima. Miyake-jima is a volcanic island located to 200km south of Tokyo, and a large amount of volcanic gas has been ejecting since the 2000 eruption. Crustal deformation associated with such volcanic activity has been observed by continuous GPS observations. However, its distribution is complicated, and therefore we applied this method to detect precise deformation time-series. In the most of GPS sites, obtained time-series were good agreement with GPS time-series, and the root-mean-square of residuals was less than 1cm. However, the temporal step of deformation was estimated in 2008, and it is not consistent with GPS time-series. We think that the effect of an orbit maneuver in 2008 has appeared. An improvement for such noise is one of next subjects. In the obtained deformation map, contraction around the caldera and uplift along the north-west-south coast were found. It is obvious that this deformation pattern cannot be explained by simple one inflation or deflation source, and its interpretation is also one of next subjects. In the caldera bottom, subsidence with 14cm/yr was found. Though its subsidence speed was constant until 2008, it decelerated to 20cm/yr from 2009. Furthermore subsidence speed in 2010 was 3cm/yr. Around the same time, low-frequency earthquakes increased just under the caldera. Then we speculate that deceleration of subsidence may directly relate with the volcanic activity. Although the result shows volcano deformation in detail, some mis-estimations were obtained. We believe that this InSAR time-series analysis is useful, but more improvements are necessary.

Data Quality Monitoring and Noise Analysis at the EUREF Permanent Network

NASA Astrophysics Data System (ADS)

Kenyeres, A.; Bruyninx, C.

2004-12-01

The EUREF Permanent Network (EPN) includes now more then 150 GNSS stations of different quality and different observation history. The greatest portion of the sites is settled on the tectonically stable parts of Eurasia, where only mm-level yearly displacements are expected. In order to extract the relevant geophysical information, sophisticated analysis tools and stable, long term observations are necessary. As the EPN is operational since 1996, it offers the potential to estimate high quality velocities associated with reliable uncertainties. In order to support this work, a set of efficient and demonstrative tools have been developed to monitor the data and station quality. The periodically upgraded results are displayed on the website of the EPN Central Bureau (CB) (www.epncb.oma.be) in terms of sky plots, graphs of observation percentage, cycle slips and multipath. The different quality plots are indirectly used for the interpretation of the time series. Sudden changes or unusual variation in the time series (beyond the obvious equipment change) often correlates with changes in the environment mirrored by the quality plots. These graphs are vital for the proper interpretation and the understanding of the real processes. Knowing the nuisance factors, we can generate cleaner time series. We are presenting relevant examples of this work. Two kinds of time series plots are displayed at the EPN CB website: raw and improved time series. They are cumulative solutions of the weekly EPN SINEX files using the minimum constraint approach. Within the improved time series the outliers and offsets are already taken into account. We will also present preliminary results of a detailed noise analysis of the EPN time series. The target of this work is twofold: on one side we aim at computing more realistic velocity estimates of the EPN stations and on the other side the information about the station noise characteristics will support the removal and proper interpretation of site-specific phenomena .

Complex-valued time-series correlation increases sensitivity in FMRI analysis.

PubMed

Kociuba, Mary C; Rowe, Daniel B

2016-07-01

To develop a linear matrix representation of correlation between complex-valued (CV) time-series in the temporal Fourier frequency domain, and demonstrate its increased sensitivity over correlation between magnitude-only (MO) time-series in functional MRI (fMRI) analysis. The standard in fMRI is to discard the phase before the statistical analysis of the data, despite evidence of task related change in the phase time-series. With a real-valued isomorphism representation of Fourier reconstruction, correlation is computed in the temporal frequency domain with CV time-series data, rather than with the standard of MO data. A MATLAB simulation compares the Fisher-z transform of MO and CV correlations for varying degrees of task related magnitude and phase amplitude change in the time-series. The increased sensitivity of the complex-valued Fourier representation of correlation is also demonstrated with experimental human data. Since the correlation description in the temporal frequency domain is represented as a summation of second order temporal frequencies, the correlation is easily divided into experimentally relevant frequency bands for each voxel's temporal frequency spectrum. The MO and CV correlations for the experimental human data are analyzed for four voxels of interest (VOIs) to show the framework with high and low contrast-to-noise ratios in the motor cortex and the supplementary motor cortex. The simulation demonstrates the increased strength of CV correlations over MO correlations for low magnitude contrast-to-noise time-series. In the experimental human data, the MO correlation maps are noisier than the CV maps, and it is more difficult to distinguish the motor cortex in the MO correlation maps after spatial processing. Including both magnitude and phase in the spatial correlation computations more accurately defines the correlated left and right motor cortices. Sensitivity in correlation analysis is important to preserve the signal of interest in fMRI data sets with high noise variance, and avoid excessive processing induced correlation. Copyright © 2016 Elsevier Inc. All rights reserved.

Novel Flood Detection and Analysis Method Using Recurrence Property

NASA Astrophysics Data System (ADS)

Wendi, Dadiyorto; Merz, Bruno; Marwan, Norbert

2016-04-01

Temporal changes in flood hazard are known to be difficult to detect and attribute due to multiple drivers that include processes that are non-stationary and highly variable. These drivers, such as human-induced climate change, natural climate variability, implementation of flood defence, river training, or land use change, could impact variably on space-time scales and influence or mask each other. Flood time series may show complex behavior that vary at a range of time scales and may cluster in time. This study focuses on the application of recurrence based data analysis techniques (recurrence plot) for understanding and quantifying spatio-temporal changes in flood hazard in Germany. The recurrence plot is known as an effective tool to visualize the dynamics of phase space trajectories i.e. constructed from a time series by using an embedding dimension and a time delay, and it is known to be effective in analyzing non-stationary and non-linear time series. The emphasis will be on the identification of characteristic recurrence properties that could associate typical dynamic behavior to certain flood situations.

Regenerating time series from ordinal networks.

PubMed

McCullough, Michael; Sakellariou, Konstantinos; Stemler, Thomas; Small, Michael

2017-03-01

Recently proposed ordinal networks not only afford novel methods of nonlinear time series analysis but also constitute stochastic approximations of the deterministic flow time series from which the network models are constructed. In this paper, we construct ordinal networks from discrete sampled continuous chaotic time series and then regenerate new time series by taking random walks on the ordinal network. We then investigate the extent to which the dynamics of the original time series are encoded in the ordinal networks and retained through the process of regenerating new time series by using several distinct quantitative approaches. First, we use recurrence quantification analysis on traditional recurrence plots and order recurrence plots to compare the temporal structure of the original time series with random walk surrogate time series. Second, we estimate the largest Lyapunov exponent from the original time series and investigate the extent to which this invariant measure can be estimated from the surrogate time series. Finally, estimates of correlation dimension are computed to compare the topological properties of the original and surrogate time series dynamics. Our findings show that ordinal networks constructed from univariate time series data constitute stochastic models which approximate important dynamical properties of the original systems.

Regenerating time series from ordinal networks

NASA Astrophysics Data System (ADS)

McCullough, Michael; Sakellariou, Konstantinos; Stemler, Thomas; Small, Michael

2017-03-01

Recently proposed ordinal networks not only afford novel methods of nonlinear time series analysis but also constitute stochastic approximations of the deterministic flow time series from which the network models are constructed. In this paper, we construct ordinal networks from discrete sampled continuous chaotic time series and then regenerate new time series by taking random walks on the ordinal network. We then investigate the extent to which the dynamics of the original time series are encoded in the ordinal networks and retained through the process of regenerating new time series by using several distinct quantitative approaches. First, we use recurrence quantification analysis on traditional recurrence plots and order recurrence plots to compare the temporal structure of the original time series with random walk surrogate time series. Second, we estimate the largest Lyapunov exponent from the original time series and investigate the extent to which this invariant measure can be estimated from the surrogate time series. Finally, estimates of correlation dimension are computed to compare the topological properties of the original and surrogate time series dynamics. Our findings show that ordinal networks constructed from univariate time series data constitute stochastic models which approximate important dynamical properties of the original systems.

Finding hidden periodic signals in time series - an application to stock prices

NASA Astrophysics Data System (ADS)

O'Shea, Michael

2014-03-01

Data in the form of time series appear in many areas of science. In cases where the periodicity is apparent and the only other contribution to the time series is stochastic in origin, the data can be `folded' to improve signal to noise and this has been done for light curves of variable stars with the folding resulting in a cleaner light curve signal. Stock index prices versus time are classic examples of time series. Repeating patterns have been claimed by many workers and include unusually large returns on small-cap stocks during the month of January, and small returns on the Dow Jones Industrial average (DJIA) in the months June through September compared to the rest of the year. Such observations imply that these prices have a periodic component. We investigate this for the DJIA. If such a component exists it is hidden in a large non-periodic variation and a large stochastic variation. We show how to extract this periodic component and for the first time reveal its yearly (averaged) shape. This periodic component leads directly to the `Sell in May and buy at Halloween' adage. We also drill down and show that this yearly variation emerges from approximately half of the underlying stocks making up the DJIA index.

Spatial analysis of precipitation time series over the Upper Indus Basin

NASA Astrophysics Data System (ADS)

Latif, Yasir; Yaoming, Ma; Yaseen, Muhammad

2018-01-01

The upper Indus basin (UIB) holds one of the most substantial river systems in the world, contributing roughly half of the available surface water in Pakistan. This water provides necessary support for agriculture, domestic consumption, and hydropower generation; all critical for a stable economy in Pakistan. This study has identified trends, analyzed variability, and assessed changes in both annual and seasonal precipitation during four time series, identified herein as: (first) 1961-2013, (second) 1971-2013, (third) 1981-2013, and (fourth) 1991-2013, over the UIB. This study investigated spatial characteristics of the precipitation time series over 15 weather stations and provides strong evidence of annual precipitation by determining significant trends at 6 stations (Astore, Chilas, Dir, Drosh, Gupis, and Kakul) out of the 15 studied stations, revealing a significant negative trend during the fourth time series. Our study also showed significantly increased precipitation at Bunji, Chitral, and Skardu, whereas such trends at the rest of the stations appear insignificant. Moreover, our study found that seasonal precipitation decreased at some locations (at a high level of significance), as well as periods of scarce precipitation during all four seasons. The observed decreases in precipitation appear stronger and more significant in autumn; having 10 stations exhibiting decreasing precipitation during the fourth time series, with respect to time and space. Furthermore, the observed decreases in precipitation appear robust and more significant for regions at high elevation (>1300 m). This analysis concludes that decreasing precipitation dominated the UIB, both temporally and spatially including in the higher areas.

Featureless classification of light curves

NASA Astrophysics Data System (ADS)

Kügler, S. D.; Gianniotis, N.; Polsterer, K. L.

2015-08-01

In the era of rapidly increasing amounts of time series data, classification of variable objects has become the main objective of time-domain astronomy. Classification of irregularly sampled time series is particularly difficult because the data cannot be represented naturally as a vector which can be directly fed into a classifier. In the literature, various statistical features serve as vector representations. In this work, we represent time series by a density model. The density model captures all the information available, including measurement errors. Hence, we view this model as a generalization to the static features which directly can be derived, e.g. as moments from the density. Similarity between each pair of time series is quantified by the distance between their respective models. Classification is performed on the obtained distance matrix. In the numerical experiments, we use data from the OGLE (Optical Gravitational Lensing Experiment) and ASAS (All Sky Automated Survey) surveys and demonstrate that the proposed representation performs up to par with the best currently used feature-based approaches. The density representation preserves all static information present in the observational data, in contrast to a less-complete description by features. The density representation is an upper boundary in terms of information made available to the classifier. Consequently, the predictive power of the proposed classification depends on the choice of similarity measure and classifier, only. Due to its principled nature, we advocate that this new approach of representing time series has potential in tasks beyond classification, e.g. unsupervised learning.

Analysis on Difference of Forest Phenology Extracted from EVI and LAI Based on PhenoCams

NASA Astrophysics Data System (ADS)

Wang, C.; Jing, L.; Qinhuo, L.

2017-12-01

Land surface phenology can make up for the deficiency of field observation with advantages of capturing the continuous expression of phenology on a large scale. However, there are some variability in phenological metrics derived from different satellite time-series data of vegetation parameters. This paper aims at assessing the difference of phenology information extracted from EVI and LAI time series. To achieve this, some web-camera sites were selected to analyze the characteristics between MODIS-EVI and MODIS-LAI time series from 2010 to 2014 for different forest types, including evergreen coniferous forest, evergreen broadleaf forest, deciduous coniferous forest and deciduous broadleaf forest. At the same time, satellite-based phenological metrics were extracted by the Logistics algorithm and compared with camera-based phenological metrics. Results show that the SOS and EOS that are extracted from LAI are close to bud burst and leaf defoliation respectively, while the SOS and EOS that are extracted from EVI is close to leaf unfolding and leaf coloring respectively. Thus the SOS that is extracted from LAI is earlier than that from EVI, while the EOS that is extracted from LAI is later than that from EVI at deciduous forest sites. Although the seasonal variation characteristics of evergreen forests are not apparent, significant discrepancies exist in LAI time series and EVI time series. In addition, Satellite- and camera-based phenological metrics agree well generally, but EVI has higher correlation with the camera-based canopy greenness (green chromatic coordinate, gcc) than LAI.

NOVA.

ERIC Educational Resources Information Center

WGBH-TV, Boston, MA.

News clippings, reviews, and feature articles about the Public Broadcasting System science-adventure series "Nova" are collected here. Included are comments from the New York Times, Washington Post, Christian Science Monitor, and TV Guide. Commentaries are primarily favorable and include synopses of various episodes. (DGC)

GPS Position Time Series @ JPL

NASA Technical Reports Server (NTRS)

Owen, Susan; Moore, Angelyn; Kedar, Sharon; Liu, Zhen; Webb, Frank; Heflin, Mike; Desai, Shailen

2013-01-01

Different flavors of GPS time series analysis at JPL - Use same GPS Precise Point Positioning Analysis raw time series - Variations in time series analysis/post-processing driven by different users. center dot JPL Global Time Series/Velocities - researchers studying reference frame, combining with VLBI/SLR/DORIS center dot JPL/SOPAC Combined Time Series/Velocities - crustal deformation for tectonic, volcanic, ground water studies center dot ARIA Time Series/Coseismic Data Products - Hazard monitoring and response focused center dot ARIA data system designed to integrate GPS and InSAR - GPS tropospheric delay used for correcting InSAR - Caltech's GIANT time series analysis uses GPS to correct orbital errors in InSAR - Zhen Liu's talking tomorrow on InSAR Time Series analysis

Identifying Changes of Complex Flood Dynamics with Recurrence Analysis

NASA Astrophysics Data System (ADS)

Wendi, D.; Merz, B.; Marwan, N.

2016-12-01

Temporal changes in flood hazard system are known to be difficult to detect and attribute due to multiple drivers that include complex processes that are non-stationary and highly variable. These drivers, such as human-induced climate change, natural climate variability, implementation of flood defense, river training, or land use change, could impact variably on space-time scales and influence or mask each other. Flood time series may show complex behavior that vary at a range of time scales and may cluster in time. Moreover hydrological time series (i.e. discharge) are often subject to measurement errors, such as rating curve error especially in the case of extremes where observation are actually derived through extrapolation. This study focuses on the application of recurrence based data analysis techniques (recurrence plot) for understanding and quantifying spatio-temporal changes in flood hazard in Germany. The recurrence plot is known as an effective tool to visualize the dynamics of phase space trajectories i.e. constructed from a time series by using an embedding dimension and a time delay, and it is known to be effective in analyzing non-stationary and non-linear time series. Sensitivity of the common measurement errors and noise on recurrence analysis will also be analyzed and evaluated against conventional methods. The emphasis will be on the identification of characteristic recurrence properties that could associate typical dynamic to certain flood events.

A Methodology for the Parametric Reconstruction of Non-Steady and Noisy Meteorological Time Series

NASA Astrophysics Data System (ADS)

Rovira, F.; Palau, J. L.; Millán, M.

2009-09-01

Climatic and meteorological time series often show some persistence (in time) in the variability of certain features. One could regard annual, seasonal and diurnal time variability as trivial persistence in the variability of some meteorological magnitudes (as, e.g., global radiation, air temperature above surface, etc.). In these cases, the traditional Fourier transform into frequency space will show the principal harmonics as the components with the largest amplitude. Nevertheless, meteorological measurements often show other non-steady (in time) variability. Some fluctuations in measurements (at different time scales) are driven by processes that prevail on some days (or months) of the year but disappear on others. By decomposing a time series into time-frequency space through the continuous wavelet transformation, one is able to determine both the dominant modes of variability and how those modes vary in time. This study is based on a numerical methodology to analyse non-steady principal harmonics in noisy meteorological time series. This methodology combines both the continuous wavelet transform and the development of a parametric model that includes the time evolution of the principal and the most statistically significant harmonics of the original time series. The parameterisation scheme proposed in this study consists of reproducing the original time series by means of a statistically significant finite sum of sinusoidal signals (waves), each defined by using the three usual parameters: amplitude, frequency and phase. To ensure the statistical significance of the parametric reconstruction of the original signal, we propose a standard statistical t-student analysis of the confidence level of the amplitude in the parametric spectrum for the different wave components. Once we have assured the level of significance of the different waves composing the parametric model, we can obtain the statistically significant principal harmonics (in time) of the original time series by using the Fourier transform of the modelled signal. Acknowledgements The CEAM Foundation is supported by the Generalitat Valenciana and BANCAIXA (València, Spain). This study has been partially funded by the European Commission (FP VI, Integrated Project CIRCE - No. 036961) and by the Ministerio de Ciencia e Innovación, research projects "TRANSREG” (CGL2007-65359/CLI) and "GRACCIE” (CSD2007-00067, Program CONSOLIDER-INGENIO 2010).

76 FR 67628 - Airworthiness Directives; Bell Helicopter Textron, Inc. (Bell) Model 204B, 205A, 205A-1, 205B...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-02

... retirement life of 3,600 hours time-in-service (TIS) for certain part-numbered main rotor yokes installed on the Bell Model 204, 205 series, and 212 series helicopters. Those ADs were prompted by reports of... the applicability to include yokes produced under a Parts Manufacturing Approval (PMA) whose design...

Assessing and Forecasting Facilities in Higher Education Including the Top Facilities Issues. APPA Thought Leaders Series, 2010

ERIC Educational Resources Information Center

Lunday, Elizabeth

2010-01-01

The APPA (Association of Higher Education Facilities Officers) Thought Leaders Series turned five years old this year--a significant event in a momentous time for higher education. Participants in the 2010 symposium looked back at both the achievements and the missteps of higher education over the last half-decade, a period that posed many…

29 CFR 550.1 - “Talent fees” as used in section 7(e)(3)(c) of the Fair Labor Standards Act, as amended.

Code of Federal Regulations, 2014 CFR

2014-07-01

... performer on a particular commercial program or a particular series of commercial programs (including... particular series of sustaining programs; (b) In pursuance of an applicable employment agreement or... requiring additional compensation as a result of the time worked on the program if the parties agree in...

36 CFR 1254.92 - How do I submit a request to microfilm records and donated historical materials?

Code of Federal Regulations, 2011 CFR

2011-07-01

..., records preparation, and other NARA requirements in a shorter time frame. (1) You may include in your request only one project to microfilm a complete body of documents, such as an entire series, a major continuous segment of a very large series which is reasonably divisible, or a limited number of separate...

36 CFR 1254.92 - How do I submit a request to microfilm records and donated historical materials?

Code of Federal Regulations, 2010 CFR

2010-07-01

..., records preparation, and other NARA requirements in a shorter time frame. (1) You may include in your request only one project to microfilm a complete body of documents, such as an entire series, a major continuous segment of a very large series which is reasonably divisible, or a limited number of separate...

Statistical fingerprinting for malware detection and classification

DOEpatents

Prowell, Stacy J.; Rathgeb, Christopher T.

2015-09-15

A system detects malware in a computing architecture with an unknown pedigree. The system includes a first computing device having a known pedigree and operating free of malware. The first computing device executes a series of instrumented functions that, when executed, provide a statistical baseline that is representative of the time it takes the software application to run on a computing device having a known pedigree. A second computing device executes a second series of instrumented functions that, when executed, provides an actual time that is representative of the time the known software application runs on the second computing device. The system detects malware when there is a difference in execution times between the first and the second computing devices.

A hybrid wavelet de-noising and Rank-Set Pair Analysis approach for forecasting hydro-meteorological time series.

PubMed

Wang, Dong; Borthwick, Alistair G; He, Handan; Wang, Yuankun; Zhu, Jieyu; Lu, Yuan; Xu, Pengcheng; Zeng, Xiankui; Wu, Jichun; Wang, Lachun; Zou, Xinqing; Liu, Jiufu; Zou, Ying; He, Ruimin

2018-01-01

Accurate, fast forecasting of hydro-meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, wavelet de-noising (WD) and Rank-Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro-meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD-RSPA approach. Two types of hydro-meteorological data sets with different characteristics and different levels of human influences at some representative stations are used to illustrate the WD-RSPA approach. The approach is also compared to three other generic methods: the conventional Auto Regressive Integrated Moving Average (ARIMA) method, Artificial Neural Networks (ANNs) (BP-error Back Propagation, MLP-Multilayer Perceptron and RBF-Radial Basis Function), and RSPA alone. Nine error metrics are used to evaluate the model performance. Compared to three other generic methods, the results generated by WD-REPA model presented invariably smaller error measures which means the forecasting capability of the WD-REPA model is better than other models. The results show that WD-RSPA is accurate, feasible, and effective. In particular, WD-RSPA is found to be the best among the various generic methods compared in this paper, even when the extreme events are included within a time series. Copyright © 2017 Elsevier Inc. All rights reserved.

A general framework for time series data mining based on event analysis: application to the medical domains of electroencephalography and stabilometry.

PubMed

Lara, Juan A; Lizcano, David; Pérez, Aurora; Valente, Juan P

2014-10-01

There are now domains where information is recorded over a period of time, leading to sequences of data known as time series. In many domains, like medicine, time series analysis requires to focus on certain regions of interest, known as events, rather than analyzing the whole time series. In this paper, we propose a framework for knowledge discovery in both one-dimensional and multidimensional time series containing events. We show how our approach can be used to classify medical time series by means of a process that identifies events in time series, generates time series reference models of representative events and compares two time series by analyzing the events they have in common. We have applied our framework on time series generated in the areas of electroencephalography (EEG) and stabilometry. Framework performance was evaluated in terms of classification accuracy, and the results confirmed that the proposed schema has potential for classifying EEG and stabilometric signals. The proposed framework is useful for discovering knowledge from medical time series containing events, such as stabilometric and electroencephalographic time series. These results would be equally applicable to other medical domains generating iconographic time series, such as, for example, electrocardiography (ECG). Copyright © 2014 Elsevier Inc. All rights reserved.

IDSP- INTERACTIVE DIGITAL SIGNAL PROCESSOR

NASA Technical Reports Server (NTRS)

Mish, W. H.

1994-01-01

The Interactive Digital Signal Processor, IDSP, consists of a set of time series analysis "operators" based on the various algorithms commonly used for digital signal analysis work. The processing of a digital time series to extract information is usually achieved by the application of a number of fairly standard operations. However, it is often desirable to "experiment" with various operations and combinations of operations to explore their effect on the results. IDSP is designed to provide an interactive and easy-to-use system for this type of digital time series analysis. The IDSP operators can be applied in any sensible order (even recursively), and can be applied to single time series or to simultaneous time series. IDSP is being used extensively to process data obtained from scientific instruments onboard spacecraft. It is also an excellent teaching tool for demonstrating the application of time series operators to artificially-generated signals. IDSP currently includes over 43 standard operators. Processing operators provide for Fourier transformation operations, design and application of digital filters, and Eigenvalue analysis. Additional support operators provide for data editing, display of information, graphical output, and batch operation. User-developed operators can be easily interfaced with the system to provide for expansion and experimentation. Each operator application generates one or more output files from an input file. The processing of a file can involve many operators in a complex application. IDSP maintains historical information as an integral part of each file so that the user can display the operator history of the file at any time during an interactive analysis. IDSP is written in VAX FORTRAN 77 for interactive or batch execution and has been implemented on a DEC VAX-11/780 operating under VMS. The IDSP system generates graphics output for a variety of graphics systems. The program requires the use of Versaplot and Template plotting routines and IMSL Math/Library routines. These software packages are not included in IDSP. The virtual memory requirement for the program is approximately 2.36 MB. The IDSP system was developed in 1982 and was last updated in 1986. Versaplot is a registered trademark of Versatec Inc. Template is a registered trademark of Template Graphics Software Inc. IMSL Math/Library is a registered trademark of IMSL Inc.

Historical instrumental climate data for Australia - quality and utility for palaeoclimatic studies

NASA Astrophysics Data System (ADS)

Nicholls, Neville; Collins, Dean; Trewin, Blair; Hope, Pandora

2006-10-01

The quality and availability of climate data suitable for palaeoclimatic calibration and verification for the Australian region are discussed and documented. Details of the various datasets, including problems with the data, are presented. High-quality datasets, where such problems are reduced or even eliminated, are discussed. Many climate datasets are now analysed onto grids, facilitating the preparation of regional-average time series. Work is under way to produce such high-quality, gridded datasets for a variety of hitherto unavailable climate data, including surface humidity, pan evaporation, wind, and cloud. An experiment suggests that only a relatively small number of palaeoclimatic time series could provide a useful estimate of long-term changes in Australian annual average temperature. Copyright

Validation of Vegetation Index Time Series from Suomi NPP Visible Infrared Imaging Radiometer Suite Using Tower Radiation Flux Measurements

NASA Astrophysics Data System (ADS)

Miura, T.; Kato, A.; Wang, J.; Vargas, M.; Lindquist, M.

2015-12-01

Satellite vegetation index (VI) time series data serve as an important means to monitor and characterize seasonal changes of terrestrial vegetation and their interannual variability. It is, therefore, critical to ensure quality of such VI products and one method of validating VI product quality is cross-comparison with in situ flux tower measurements. In this study, we evaluated the quality of VI time series derived from Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (NPP) spacecraft by cross-comparison with in situ radiation flux measurements at select flux tower sites over North America and Europe. VIIRS is a new polar-orbiting satellite sensor series, slated to replace National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer in the afternoon overpass and to continue the highly-calibrated data streams initiated with Moderate Resolution Imaging Spectrometer of National Aeronautics and Space Administration's Earth Observing System. The selected sites covered a wide range of biomes, including croplands, grasslands, evergreen needle forest, woody savanna, and open shrublands. The two VIIRS indices of the Top-of-Atmosphere (TOA) Normalized Difference Vegetation Index (NDVI) and the atmospherically-corrected, Top-of-Canopy (TOC) Enhanced Vegetation Index (EVI) (daily, 375 m spatial resolution) were compared against the TOC NDVI and a two-band version of EVI (EVI2) calculated from tower radiation flux measurements, respectively. VIIRS and Tower VI time series showed comparable seasonal profiles across biomes with statistically significant correlations (> 0.60; p-value < 0.01). "Start-of-season (SOS)" phenological metric values extracted from VIIRS and Tower VI time series were also highly compatible (R2 > 0.95), with mean differences of 2.3 days and 5.0 days for the NDVI and the EVI, respectively. These results indicate that VIIRS VI time series can capture seasonal evolution of vegetated land surface as good as in situ radiometric measurements. Future studies that address biophysical or physiological interpretations of Tower VI time series derived from radiation flux measurements are desirable.

Statistical Analysis of Time-Series from Monitoring of Active Volcanic Vents

NASA Astrophysics Data System (ADS)

Lachowycz, S.; Cosma, I.; Pyle, D. M.; Mather, T. A.; Rodgers, M.; Varley, N. R.

2016-12-01

Despite recent advances in the collection and analysis of time-series from volcano monitoring, and the resulting insights into volcanic processes, challenges remain in forecasting and interpreting activity from near real-time analysis of monitoring data. Statistical methods have potential to characterise the underlying structure and facilitate intercomparison of these time-series, and so inform interpretation of volcanic activity. We explore the utility of multiple statistical techniques that could be widely applicable to monitoring data, including Shannon entropy and detrended fluctuation analysis, by their application to various data streams from volcanic vents during periods of temporally variable activity. Each technique reveals changes through time in the structure of some of the data that were not apparent from conventional analysis. For example, we calculate the Shannon entropy (a measure of the randomness of a signal) of time-series from the recent dome-forming eruptions of Volcán de Colima (Mexico) and Soufrière Hills (Montserrat). The entropy of real-time seismic measurements and the count rate of certain volcano-seismic event types from both volcanoes is found to be temporally variable, with these data generally having higher entropy during periods of lava effusion and/or larger explosions. In some instances, the entropy shifts prior to or coincident with changes in seismic or eruptive activity, some of which were not clearly recognised by real-time monitoring. Comparison with other statistics demonstrates the sensitivity of the entropy to the data distribution, but that it is distinct from conventional statistical measures such as coefficient of variation. We conclude that each analysis technique examined could provide valuable insights for interpretation of diverse monitoring time-series.

Progress Report on the Airborne Metadata and Time Series Working Groups of the 2016 ESDSWG

NASA Astrophysics Data System (ADS)

Evans, K. D.; Northup, E. A.; Chen, G.; Conover, H.; Ames, D. P.; Teng, W. L.; Olding, S. W.; Krotkov, N. A.

2016-12-01

NASA's Earth Science Data Systems Working Groups (ESDSWG) was created over 10 years ago. The role of the ESDSWG is to make recommendations relevant to NASA's Earth science data systems from users' experiences. Each group works independently focusing on a unique topic. Participation in ESDSWG groups comes from a variety of NASA-funded science and technology projects, including MEaSUREs and ROSS. Participants include NASA information technology experts, affiliated contractor staff and other interested community members from academia and industry. Recommendations from the ESDSWG groups will enhance NASA's efforts to develop long term data products. The Airborne Metadata Working Group is evaluating the suitability of the current Common Metadata Repository (CMR) and Unified Metadata Model (UMM) for airborne data sets and to develop new recommendations as necessary. The overarching goal is to enhance the usability, interoperability, discovery and distribution of airborne observational data sets. This will be done by assessing the suitability (gaps) of the current UMM model for airborne data using lessons learned from current and past field campaigns, listening to user needs and community recommendations and assessing the suitability of ISO metadata and other standards to fill the gaps. The Time Series Working Group (TSWG) is a continuation of the 2015 Time Series/WaterML2 Working Group. The TSWG is using a case study-driven approach to test the new Open Geospatial Consortium (OGC) TimeseriesML standard to determine any deficiencies with respect to its ability to fully describe and encode NASA earth observation-derived time series data. To do this, the time series working group is engaging with the OGC TimeseriesML Standards Working Group (SWG) regarding unsatisfied needs and possible solutions. The effort will end with the drafting of an OGC Engineering Report based on the use cases and interactions with the OGC TimeseriesML SWG. Progress towards finalizing recommendations will be presented at the meeting.

Using environmental tracers to determine the relative importance of travel times in the unsaturated and saturated zones for the delay of nitrate reduction measures

NASA Astrophysics Data System (ADS)

Gerber, Christoph; Purtschert, Roland; Hunkeler, Daniel; Hug, Rainer; Sültenfuss, Jürgen

2018-06-01

Groundwater quality in many regions with intense agriculture has deteriorated due to the leaching of nitrate and other agricultural pollutants. Modified agricultural practices can reduce the input of nitrate to groundwater bodies, but it is crucial to determine the time span over which these measures become effective at reducing nitrate levels in pumping wells. Such estimates can be obtained from hydrogeological modeling or lumped-parameter models (LPM) in combination with environmental tracer data. Two challenges in such tracer-based estimates are (i) accounting for the different modes of transport in the unsaturated zone (USZ), and (ii) assessing uncertainties. Here we extend a recently published Bayesian inference scheme for simple LPMs to include an explicit USZ model and apply it to the Dünnerngäu aquifer, Switzerland. Compared to a previous estimate of travel times in the aquifer based on a 2D hydrogeological model, our approach provides a more accurate assessment of the dynamics of nitrate concentrations in the aquifer. We find that including tracer measurements (3H/3He, 85Kr, 39Ar, 4He) reduces uncertainty in nitrate predictions if nitrate time series at wells are not available or short, but does not necessarily lead to better predictions if long nitrate time series are available. Additionally, the combination of tracer data with nitrate time series allows for a separation of the travel times in the unsaturated and saturated zone.

Detection of a sudden change of the field time series based on the Lorenz system.

PubMed

Da, ChaoJiu; Li, Fang; Shen, BingLu; Yan, PengCheng; Song, Jian; Ma, DeShan

2017-01-01

We conducted an exploratory study of the detection of a sudden change of the field time series based on the numerical solution of the Lorenz system. First, the time when the Lorenz path jumped between the regions on the left and right of the equilibrium point of the Lorenz system was quantitatively marked and the sudden change time of the Lorenz system was obtained. Second, the numerical solution of the Lorenz system was regarded as a vector; thus, this solution could be considered as a vector time series. We transformed the vector time series into a time series using the vector inner product, considering the geometric and topological features of the Lorenz system path. Third, the sudden change of the resulting time series was detected using the sliding t-test method. Comparing the test results with the quantitatively marked time indicated that the method could detect every sudden change of the Lorenz path, thus the method is effective. Finally, we used the method to detect the sudden change of the pressure field time series and temperature field time series, and obtained good results for both series, which indicates that the method can apply to high-dimension vector time series. Mathematically, there is no essential difference between the field time series and vector time series; thus, we provide a new method for the detection of the sudden change of the field time series.

Volatility of linear and nonlinear time series

NASA Astrophysics Data System (ADS)

Kalisky, Tomer; Ashkenazy, Yosef; Havlin, Shlomo

2005-07-01

Previous studies indicated that nonlinear properties of Gaussian distributed time series with long-range correlations, ui , can be detected and quantified by studying the correlations in the magnitude series ∣ui∣ , the “volatility.” However, the origin for this empirical observation still remains unclear and the exact relation between the correlations in ui and the correlations in ∣ui∣ is still unknown. Here we develop analytical relations between the scaling exponent of linear series ui and its magnitude series ∣ui∣ . Moreover, we find that nonlinear time series exhibit stronger (or the same) correlations in the magnitude time series compared with linear time series with the same two-point correlations. Based on these results we propose a simple model that generates multifractal time series by explicitly inserting long range correlations in the magnitude series; the nonlinear multifractal time series is generated by multiplying a long-range correlated time series (that represents the magnitude series) with uncorrelated time series [that represents the sign series sgn(ui) ]. We apply our techniques on daily deep ocean temperature records from the equatorial Pacific, the region of the El-Ninõ phenomenon, and find: (i) long-range correlations from several days to several years with 1/f power spectrum, (ii) significant nonlinear behavior as expressed by long-range correlations of the volatility series, and (iii) broad multifractal spectrum.

Energy-Based Wavelet De-Noising of Hydrologic Time Series

PubMed Central

Sang, Yan-Fang; Liu, Changming; Wang, Zhonggen; Wen, Jun; Shang, Lunyu

2014-01-01

De-noising is a substantial issue in hydrologic time series analysis, but it is a difficult task due to the defect of methods. In this paper an energy-based wavelet de-noising method was proposed. It is to remove noise by comparing energy distribution of series with the background energy distribution, which is established from Monte-Carlo test. Differing from wavelet threshold de-noising (WTD) method with the basis of wavelet coefficient thresholding, the proposed method is based on energy distribution of series. It can distinguish noise from deterministic components in series, and uncertainty of de-noising result can be quantitatively estimated using proper confidence interval, but WTD method cannot do this. Analysis of both synthetic and observed series verified the comparable power of the proposed method and WTD, but de-noising process by the former is more easily operable. The results also indicate the influences of three key factors (wavelet choice, decomposition level choice and noise content) on wavelet de-noising. Wavelet should be carefully chosen when using the proposed method. The suitable decomposition level for wavelet de-noising should correspond to series' deterministic sub-signal which has the smallest temporal scale. If too much noise is included in a series, accurate de-noising result cannot be obtained by the proposed method or WTD, but the series would show pure random but not autocorrelation characters, so de-noising is no longer needed. PMID:25360533

Duality between Time Series and Networks

PubMed Central

Campanharo, Andriana S. L. O.; Sirer, M. Irmak; Malmgren, R. Dean; Ramos, Fernando M.; Amaral, Luís A. Nunes.

2011-01-01

Studying the interaction between a system's components and the temporal evolution of the system are two common ways to uncover and characterize its internal workings. Recently, several maps from a time series to a network have been proposed with the intent of using network metrics to characterize time series. Although these maps demonstrate that different time series result in networks with distinct topological properties, it remains unclear how these topological properties relate to the original time series. Here, we propose a map from a time series to a network with an approximate inverse operation, making it possible to use network statistics to characterize time series and time series statistics to characterize networks. As a proof of concept, we generate an ensemble of time series ranging from periodic to random and confirm that application of the proposed map retains much of the information encoded in the original time series (or networks) after application of the map (or its inverse). Our results suggest that network analysis can be used to distinguish different dynamic regimes in time series and, perhaps more importantly, time series analysis can provide a powerful set of tools that augment the traditional network analysis toolkit to quantify networks in new and useful ways. PMID:21858093

Using Diurnal Temperature Signals to Infer Vertical Groundwater-Surface Water Exchange.

PubMed

Irvine, Dylan J; Briggs, Martin A; Lautz, Laura K; Gordon, Ryan P; McKenzie, Jeffrey M; Cartwright, Ian

2017-01-01

Heat is a powerful tracer to quantify fluid exchange between surface water and groundwater. Temperature time series can be used to estimate pore water fluid flux, and techniques can be employed to extend these estimates to produce detailed plan-view flux maps. Key advantages of heat tracing include cost-effective sensors and ease of data collection and interpretation, without the need for expensive and time-consuming laboratory analyses or induced tracers. While the collection of temperature data in saturated sediments is relatively straightforward, several factors influence the reliability of flux estimates that are based on time series analysis (diurnal signals) of recorded temperatures. Sensor resolution and deployment are particularly important in obtaining robust flux estimates in upwelling conditions. Also, processing temperature time series data involves a sequence of complex steps, including filtering temperature signals, selection of appropriate thermal parameters, and selection of the optimal analytical solution for modeling. This review provides a synthesis of heat tracing using diurnal temperature oscillations, including details on optimal sensor selection and deployment, data processing, model parameterization, and an overview of computing tools available. Recent advances in diurnal temperature methods also provide the opportunity to determine local saturated thermal diffusivity, which can improve the accuracy of fluid flux modeling and sensor spacing, which is related to streambed scour and deposition. These parameters can also be used to determine the reliability of flux estimates from the use of heat as a tracer. © 2016, National Ground Water Association.

The Gaussian Graphical Model in Cross-Sectional and Time-Series Data.

PubMed

Epskamp, Sacha; Waldorp, Lourens J; Mõttus, René; Borsboom, Denny

2018-04-16

We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in three kinds of psychological data sets: data sets in which consecutive cases are assumed independent (e.g., cross-sectional data), temporally ordered data sets (e.g., n = 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In time-series analysis, the GGM can be used to model the residual structure of a vector-autoregression analysis (VAR), also termed graphical VAR. Two network models can then be obtained: a temporal network and a contemporaneous network. When analyzing data from multiple subjects, a GGM can also be formed on the covariance structure of stationary means-the between-subjects network. We discuss the interpretation of these models and propose estimation methods to obtain these networks, which we implement in the R packages graphicalVAR and mlVAR. The methods are showcased in two empirical examples, and simulation studies on these methods are included in the supplementary materials.

Computing the multifractal spectrum from time series: an algorithmic approach.

PubMed

Harikrishnan, K P; Misra, R; Ambika, G; Amritkar, R E

2009-12-01

We show that the existing methods for computing the f(alpha) spectrum from a time series can be improved by using a new algorithmic scheme. The scheme relies on the basic idea that the smooth convex profile of a typical f(alpha) spectrum can be fitted with an analytic function involving a set of four independent parameters. While the standard existing schemes [P. Grassberger et al., J. Stat. Phys. 51, 135 (1988); A. Chhabra and R. V. Jensen, Phys. Rev. Lett. 62, 1327 (1989)] generally compute only an incomplete f(alpha) spectrum (usually the top portion), we show that this can be overcome by an algorithmic approach, which is automated to compute the D(q) and f(alpha) spectra from a time series for any embedding dimension. The scheme is first tested with the logistic attractor with known f(alpha) curve and subsequently applied to higher-dimensional cases. We also show that the scheme can be effectively adapted for analyzing practical time series involving noise, with examples from two widely different real world systems. Moreover, some preliminary results indicating that the set of four independent parameters may be used as diagnostic measures are also included.

Machine learning for cardiac ultrasound time series data

NASA Astrophysics Data System (ADS)

Yuan, Baichuan; Chitturi, Sathya R.; Iyer, Geoffrey; Li, Nuoyu; Xu, Xiaochuan; Zhan, Ruohan; Llerena, Rafael; Yen, Jesse T.; Bertozzi, Andrea L.

2017-03-01

We consider the problem of identifying frames in a cardiac ultrasound video associated with left ventricular chamber end-systolic (ES, contraction) and end-diastolic (ED, expansion) phases of the cardiac cycle. Our procedure involves a simple application of non-negative matrix factorization (NMF) to a series of frames of a video from a single patient. Rank-2 NMF is performed to compute two end-members. The end members are shown to be close representations of the actual heart morphology at the end of each phase of the heart function. Moreover, the entire time series can be represented as a linear combination of these two end-member states thus providing a very low dimensional representation of the time dynamics of the heart. Unlike previous work, our methods do not require any electrocardiogram (ECG) information in order to select the end-diastolic frame. Results are presented for a data set of 99 patients including both healthy and diseased examples.

Nonlinear modeling of chaotic time series: Theory and applications

NASA Astrophysics Data System (ADS)

Casdagli, M.; Eubank, S.; Farmer, J. D.; Gibson, J.; Desjardins, D.; Hunter, N.; Theiler, J.

We review recent developments in the modeling and prediction of nonlinear time series. In some cases, apparent randomness in time series may be due to chaotic behavior of a nonlinear but deterministic system. In such cases, it is possible to exploit the determinism to make short term forecasts that are much more accurate than one could make from a linear stochastic model. This is done by first reconstructing a state space, and then using nonlinear function approximation methods to create a dynamical model. Nonlinear models are valuable not only as short term forecasters, but also as diagnostic tools for identifying and quantifying low-dimensional chaotic behavior. During the past few years, methods for nonlinear modeling have developed rapidly, and have already led to several applications where nonlinear models motivated by chaotic dynamics provide superior predictions to linear models. These applications include prediction of fluid flows, sunspots, mechanical vibrations, ice ages, measles epidemics, and human speech.

Cluster analysis of word frequency dynamics

NASA Astrophysics Data System (ADS)

Maslennikova, Yu S.; Bochkarev, V. V.; Belashova, I. A.

2015-01-01

This paper describes the analysis and modelling of word usage frequency time series. During one of previous studies, an assumption was put forward that all word usage frequencies have uniform dynamics approaching the shape of a Gaussian function. This assumption can be checked using the frequency dictionaries of the Google Books Ngram database. This database includes 5.2 million books published between 1500 and 2008. The corpus contains over 500 billion words in American English, British English, French, German, Spanish, Russian, Hebrew, and Chinese. We clustered time series of word usage frequencies using a Kohonen neural network. The similarity between input vectors was estimated using several algorithms. As a result of the neural network training procedure, more than ten different forms of time series were found. They describe the dynamics of word usage frequencies from birth to death of individual words. Different groups of word forms were found to have different dynamics of word usage frequency variations.

Implemented Lomb-Scargle periodogram: a valuable tool for improving cyclostratigraphic research on unevenly sampled deep-sea stratigraphic sequences

NASA Astrophysics Data System (ADS)

Pardo-Iguzquiza, Eulogio; Rodríguez-Tovar, Francisco J.

2011-12-01

One important handicap when working with stratigraphic sequences is the discontinuous character of the sedimentary record, especially relevant in cyclostratigraphic analysis. Uneven palaeoclimatic/palaeoceanographic time series are common, their cyclostratigraphic analysis being comparatively difficult because most spectral methodologies are appropriate only when working with even sampling. As a means to solve this problem, a program for calculating the smoothed Lomb-Scargle periodogram and cross-periodogram, which additionally evaluates the statistical confidence of the estimated power spectrum through a Monte Carlo procedure (the permutation test), has been developed. The spectral analysis of a short uneven time series calls for assessment of the statistical significance of the spectral peaks, since a periodogram can always be calculated but the main challenge resides in identifying true spectral features. To demonstrate the effectiveness of this program, two case studies are presented: the one deals with synthetic data and the other with paleoceanographic/palaeoclimatic proxies. On a simulated time series of 500 data, two uneven time series (with 100 and 25 data) were generated by selecting data at random. Comparative analysis between the power spectra from the simulated series and from the two uneven time series demonstrates the usefulness of the smoothed Lomb-Scargle periodogram for uneven sequences, making it possible to distinguish between statistically significant and spurious spectral peaks. Fragmentary time series of Cd/Ca ratios and δ18O from core AII107-131 of SPECMAP were analysed as a real case study. The efficiency of the direct and cross Lomb-Scargle periodogram in recognizing Milankovitch and sub-Milankovitch signals related to palaeoclimatic/palaeoceanographic changes is demonstrated. As implemented, the Lomb-Scargle periodogram may be applied to any palaeoclimatic/palaeoceanographic proxies, including those usually recovered from contourites, and it holds special interest in the context of centennial- to millennial-scale climatic changes affecting contouritic currents.

Live from Antarctica: Then and now

NASA Astrophysics Data System (ADS)

This real-time educational video series, featuring Camille Jennings from Maryland Public Television, includes information from Antarctic scientists and interactive discussion between the scientists and school children from both Maryland and Hawaii. This is part of a 'Passport to Knowledge Special' series. In this part of the four part Antarctic series, the history of Antarctica from its founding to the present, its mammals, plants, and other life forms are shown and discussed. The importance of Antarctica as a research facility is explained, along with different experiments and research that the facilities there perform.

Live from Antarctica: Then and Now

NASA Technical Reports Server (NTRS)

1994-01-01

This real-time educational video series, featuring Camille Jennings from Maryland Public Television, includes information from Antarctic scientists and interactive discussion between the scientists and school children from both Maryland and Hawaii. This is part of a 'Passport to Knowledge Special' series. In this part of the four part Antarctic series, the history of Antarctica from its founding to the present, its mammals, plants, and other life forms are shown and discussed. The importance of Antarctica as a research facility is explained, along with different experiments and research that the facilities there perform.

Process for reducing series resistance of solar-cell metal-contact systems with a soldering-flux etchant

DOEpatents

Coyle, R.T.; Barrett, J.M.

1982-05-04

Disclosed is a process for substantially reducing the series resistance of a solar cell having a thick film metal contact assembly thereon while simultaneously removing oxide coatings from the surface of the assembly prior to applying solder therewith. The process includes applying a flux to the contact assembly and heating the cell for a period of time sufficient to substantially remove the series resistance associated with the assembly by etching the assembly with the flux while simultaneously removing metal oxides from said surface of said assembly.

Process for reducing series resistance of solar cell metal contact systems with a soldering flux etchant

DOEpatents

Coyle, R. T.; Barrett, Joy M.

1984-01-01

Disclosed is a process for substantially reducing the series resistance of a solar cell having a thick film metal contact assembly thereon while simultaneously removing oxide coatings from the surface of the assembly prior to applying solder therewith. The process includes applying a flux to the contact assembly and heating the cell for a period of time sufficient to substantially remove the series resistance associated with the assembly by etching the assembly with the flux while simultaneously removing metal oxides from said surface of said assembly.

Investigating the creeping section of the San Andreas Fault using ALOS PALSAR interferometry

NASA Astrophysics Data System (ADS)

Agram, P. S.; Wortham, C.; Zebker, H. A.

2010-12-01

In recent years, time-series InSAR techniques have been used to study the temporal characteristics of various geophysical phenomena that produce surface deformation including earthquakes and magma migration in volcanoes. Conventional InSAR and time-series InSAR techniques have also been successfully used to study aseismic creep across faults in urban areas like the Northern Hayward Fault in California [1-3]. However, application of these methods to studying the time-dependent creep across the Central San Andreas Fault using C-band ERS and Envisat radar satellites has resulted in limited success. While these techniques estimate the average long-term far-field deformation rates reliably, creep measurement close to the fault (< 3-4 Km) is virtually impossible due to heavy decorrelation at C-band (6cm wavelength). Shanker and Zebker (2009) [4] used the Persistent Scatterer (PS) time-series InSAR technique to estimate a time-dependent non-uniform creep signal across a section of the creeping segment of the San Andreas Fault. However, the identified PS network was spatially very sparse (1 per sq. km) to study temporal characteristics of deformation of areas close to the fault. In this work, we use L-band (24cm wavelength) SAR data from the PALSAR instrument on-board the ALOS satellite, launched by Japanese Aerospace Exploration Agency (JAXA) in 2006, to study the temporal characteristics of creep across the Central San Andreas Fault. The longer wavelength at L-band improves observed correlation over the entire scene which significantly increased the ground area coverage of estimated deformation in each interferogram but at the cost of decreased sensitivity of interferometric phase to surface deformation. However, noise levels in our deformation estimates can be decreased by combining information from multiple SAR acquisitions using time-series InSAR techniques. We analyze 13 SAR acquisitions spanning the time-period from March 2007 to Dec 2009 using the Short Baseline Subset Analysis (SBAS) time-series InSAR technique [3]. We present detailed comparisons of estimated time-series of fault creep as a function of position along the fault including the locked section around Parkfield, CA. We also present comparisons between the InSAR time-series and GPS network observations in the Parkfield region. During these three years of observation, the average fault creep is estimated to be 35 mm/yr. References [1] Bürgmann,R., E. Fielding and, J. Sukhatme, Slip along the Hayward fault, California, estimated from space-based synthetic aperture radar interferometry, Geology,26, 559-562, 1998. [2] Ferretti, A., C. Prati and F. Rocca, Permanent Scatterers in SAR Interferometry, IEEE Trans. Geosci. Remote Sens., 39, 8-20, 2001. [3] Lanari, R.,F. Casu, M. Manzo, and P. Lundgren, Application of SBAS D- InSAR technique to fault creep: A case study of the Hayward Fault, California. Remote Sensing of Environment, 109(1), 20-28, 2007. [4] Shanker, A. P., and H. Zebker, Edgelist phase unwrapping algorithm for time-series InSAR. J. Opt. Soc. Am. A, 37(4), 2010.

On the maximum-entropy/autoregressive modeling of time series

NASA Technical Reports Server (NTRS)

Chao, B. F.

1984-01-01

The autoregressive (AR) model of a random process is interpreted in the light of the Prony's relation which relates a complex conjugate pair of poles of the AR process in the z-plane (or the z domain) on the one hand, to the complex frequency of one complex harmonic function in the time domain on the other. Thus the AR model of a time series is one that models the time series as a linear combination of complex harmonic functions, which include pure sinusoids and real exponentials as special cases. An AR model is completely determined by its z-domain pole configuration. The maximum-entropy/autogressive (ME/AR) spectrum, defined on the unit circle of the z-plane (or the frequency domain), is nothing but a convenient, but ambiguous visual representation. It is asserted that the position and shape of a spectral peak is determined by the corresponding complex frequency, and the height of the spectral peak contains little information about the complex amplitude of the complex harmonic functions.

Event coincidence analysis for quantifying statistical interrelationships between event time series. On the role of flood events as triggers of epidemic outbreaks

NASA Astrophysics Data System (ADS)

Donges, J. F.; Schleussner, C.-F.; Siegmund, J. F.; Donner, R. V.

2016-05-01

Studying event time series is a powerful approach for analyzing the dynamics of complex dynamical systems in many fields of science. In this paper, we describe the method of event coincidence analysis to provide a framework for quantifying the strength, directionality and time lag of statistical interrelationships between event series. Event coincidence analysis allows to formulate and test null hypotheses on the origin of the observed interrelationships including tests based on Poisson processes or, more generally, stochastic point processes with a prescribed inter-event time distribution and other higher-order properties. Applying the framework to country-level observational data yields evidence that flood events have acted as triggers of epidemic outbreaks globally since the 1950s. Facing projected future changes in the statistics of climatic extreme events, statistical techniques such as event coincidence analysis will be relevant for investigating the impacts of anthropogenic climate change on human societies and ecosystems worldwide.

A rapid learning and dynamic stepwise updating algorithm for flat neural networks and the application to time-series prediction.

PubMed

Chen, C P; Wan, J Z

1999-01-01

A fast learning algorithm is proposed to find an optimal weights of the flat neural networks (especially, the functional-link network). Although the flat networks are used for nonlinear function approximation, they can be formulated as linear systems. Thus, the weights of the networks can be solved easily using a linear least-square method. This formulation makes it easier to update the weights instantly for both a new added pattern and a new added enhancement node. A dynamic stepwise updating algorithm is proposed to update the weights of the system on-the-fly. The model is tested on several time-series data including an infrared laser data set, a chaotic time-series, a monthly flour price data set, and a nonlinear system identification problem. The simulation results are compared to existing models in which more complex architectures and more costly training are needed. The results indicate that the proposed model is very attractive to real-time processes.

Quantifying evolutionary dynamics from variant-frequency time series

NASA Astrophysics Data System (ADS)

Khatri, Bhavin S.

2016-09-01

From Kimura’s neutral theory of protein evolution to Hubbell’s neutral theory of biodiversity, quantifying the relative importance of neutrality versus selection has long been a basic question in evolutionary biology and ecology. With deep sequencing technologies, this question is taking on a new form: given a time-series of the frequency of different variants in a population, what is the likelihood that the observation has arisen due to selection or neutrality? To tackle the 2-variant case, we exploit Fisher’s angular transformation, which despite being discovered by Ronald Fisher a century ago, has remained an intellectual curiosity. We show together with a heuristic approach it provides a simple solution for the transition probability density at short times, including drift, selection and mutation. Our results show under that under strong selection and sufficiently frequent sampling these evolutionary parameters can be accurately determined from simulation data and so they provide a theoretical basis for techniques to detect selection from variant or polymorphism frequency time-series.

Quantifying evolutionary dynamics from variant-frequency time series.

PubMed

Khatri, Bhavin S

2016-09-12

From Kimura's neutral theory of protein evolution to Hubbell's neutral theory of biodiversity, quantifying the relative importance of neutrality versus selection has long been a basic question in evolutionary biology and ecology. With deep sequencing technologies, this question is taking on a new form: given a time-series of the frequency of different variants in a population, what is the likelihood that the observation has arisen due to selection or neutrality? To tackle the 2-variant case, we exploit Fisher's angular transformation, which despite being discovered by Ronald Fisher a century ago, has remained an intellectual curiosity. We show together with a heuristic approach it provides a simple solution for the transition probability density at short times, including drift, selection and mutation. Our results show under that under strong selection and sufficiently frequent sampling these evolutionary parameters can be accurately determined from simulation data and so they provide a theoretical basis for techniques to detect selection from variant or polymorphism frequency time-series.

Optical signal processing using photonic reservoir computing

NASA Astrophysics Data System (ADS)

Salehi, Mohammad Reza; Dehyadegari, Louiza

2014-10-01

As a new approach to recognition and classification problems, photonic reservoir computing has such advantages as parallel information processing, power efficient and high speed. In this paper, a photonic structure has been proposed for reservoir computing which is investigated using a simple, yet, non-partial noisy time series prediction task. This study includes the application of a suitable topology with self-feedbacks in a network of SOA's - which lends the system a strong memory - and leads to adjusting adequate parameters resulting in perfect recognition accuracy (100%) for noise-free time series, which shows a 3% improvement over previous results. For the classification of noisy time series, the rate of accuracy showed a 4% increase and amounted to 96%. Furthermore, an analytical approach was suggested to solve rate equations which led to a substantial decrease in the simulation time, which is an important parameter in classification of large signals such as speech recognition, and better results came up compared with previous works.

Using GNSS for Assessment Recent Sea Level Rise in the Northwestern Part of the Arabian Gulf

NASA Astrophysics Data System (ADS)

Alothman, A. O.; Bos, M. S.; Fernandes, R.

2017-12-01

Due to the global warming acting recently (in the 21st century) on the planet Earth, an associated sea level rise is predicted to reach up to 30 cm to 60 cm in some regions. Sea level monitoring is important for the Kingdom of Saudi Arabia, since it is surrounded by very long cost of about 3400 km in length and hundreds of isolated islands. The eastern coast line of KSA, in the Arabian Gulf, needs some monitoring in the long term, due to low land nature of the region. Also, the ongoing oil withdrawal activities in the area, may affect the regional sea level rise. In addition to these two facts, the tectonic structure of the Arabian Peninsula is one factor. The Regional Relative sea level in the eastern cost of Saudi Arabia has been estimated in the past using tide gauge data of more than 28 years using the vertical displacement of permanent Global Navigation Satellite System GNSS stations having time span of only about 3 years. In this paper, we discuss and update the methodology and results from Alothman et al. (2014), particularly by checking and extending the GNSS solutions. Since 3 of the 6 GPS stations used only started observing in the end of 2011, the longer time series have now significantly lower uncertainties in the estimated vertical rate. Longer time span of GNSS observations were included and 500 synthetic time series were estimated and seasonal signals were analysed. it is concluded that the varying seasonal signal present in the GNSS time series causes an underestimation of 0.1 mm/yr for short time series of 3 years. In addition to the implications of using short time series to estimate the vertical land motion, we found that if the varying seasonal signals are present in the data, the problem is aggravated. This finding can be useful for other studies analyzing short GNSS time series.

Estimating secular velocities from GPS data contaminated by postseismic motion at sites with limited pre-earthquake data

NASA Astrophysics Data System (ADS)

Murray, J. R.; Svarc, J. L.

2016-12-01

Constant secular velocities estimated from Global Positioning System (GPS)-derived position time series are a central input for modeling interseismic deformation in seismically active regions. Both postseismic motion and temporally correlated noise produce long-period signals that are difficult to separate from secular motion and can bias velocity estimates. For GPS sites installed post-earthquake it is especially challenging to uniquely estimate velocities and postseismic signals and to determine when the postseismic transient has decayed sufficiently to enable use of subsequent data for estimating secular rates. Within 60 km of the 2003 M6.5 San Simeon and 2004 M6 Parkfield earthquakes in California, 16 continuous GPS sites (group 1) were established prior to mid-2001, and 52 stations (group 2) were installed following the events. We use group 1 data to investigate how early in the post-earthquake time period one may reliably begin using group 2 data to estimate velocities. For each group 1 time series, we obtain eight velocity estimates using observation time windows with successively later start dates (2006 - 2013) and a parameterization that includes constant velocity, annual, and semi-annual terms but no postseismic decay. We compare these to velocities estimated using only pre-San Simeon data to find when the pre- and post-earthquake velocities match within uncertainties. To obtain realistic velocity uncertainties, for each time series we optimize a temporally correlated noise model consisting of white, flicker, random walk, and, in some cases, band-pass filtered noise contributions. Preliminary results suggest velocities can be reliably estimated using data from 2011 to the present. Ongoing work will assess velocity bias as a function of epicentral distance and length of post-earthquake time series as well as explore spatio-temporal filtering of detrended group 1 time series to provide empirical corrections for postseismic motion in group 2 time series.

Geomagnetic field declination: from decadal to centennial scales

NASA Astrophysics Data System (ADS)

Dobrica, Venera; Demetrescu, Crisan; Mandea, Mioara

2018-04-01

Declination annual mean time series longer than 1 century provided by 24 geomagnetic observatories worldwide, together with 5 Western European reconstructed declination series over the last 4 centuries, have been analyzed in terms of the frequency constituents of the secular variation at inter-decadal and sub-centennial timescales of 20-35 and 70-90 years. Observatory and reconstructed time series have been processed by several types of filtering, namely Hodrick-Prescott, running averages, and Butterworth. The Hodrick-Prescott filtering allows us to separate a quasi-oscillation at a decadal timescale, which is assumed to be related to external variations and called the 11-year constituent, from a long-term trend. The latter has been decomposed into two other oscillations called inter-decadal and sub-centennial constituents by applying a Butterworth filtering with cutoffs at 30 and 73 years, respectively. The analysis shows that the generally accepted geomagnetic jerks occur around extrema in the time derivative of the trend and coincide with extrema in the time derivative of the 11-year constituent. The sub-centennial constituent is traced back to 1600 in the five 400-year-long time series and seems to be a major constituent of the secular variation, geomagnetic jerks included.

A method for analyzing temporal patterns of variability of a time series from Poincare plots.

PubMed

Fishman, Mikkel; Jacono, Frank J; Park, Soojin; Jamasebi, Reza; Thungtong, Anurak; Loparo, Kenneth A; Dick, Thomas E

2012-07-01

The Poincaré plot is a popular two-dimensional, time series analysis tool because of its intuitive display of dynamic system behavior. Poincaré plots have been used to visualize heart rate and respiratory pattern variabilities. However, conventional quantitative analysis relies primarily on statistical measurements of the cumulative distribution of points, making it difficult to interpret irregular or complex plots. Moreover, the plots are constructed to reflect highly correlated regions of the time series, reducing the amount of nonlinear information that is presented and thereby hiding potentially relevant features. We propose temporal Poincaré variability (TPV), a novel analysis methodology that uses standard techniques to quantify the temporal distribution of points and to detect nonlinear sources responsible for physiological variability. In addition, the analysis is applied across multiple time delays, yielding a richer insight into system dynamics than the traditional circle return plot. The method is applied to data sets of R-R intervals and to synthetic point process data extracted from the Lorenz time series. The results demonstrate that TPV complements the traditional analysis and can be applied more generally, including Poincaré plots with multiple clusters, and more consistently than the conventional measures and can address questions regarding potential structure underlying the variability of a data set.

Classification mapping and species identification of salt marshes based on a short-time interval NDVI time-series from HJ-1 optical imagery

NASA Astrophysics Data System (ADS)

Sun, Chao; Liu, Yongxue; Zhao, Saishuai; Zhou, Minxi; Yang, Yuhao; Li, Feixue

2016-03-01

Salt marshes are seen as the most dynamic and valuable ecosystems in coastal zones, and in these areas, it is crucial to obtain accurate remote sensing information on the spatial distributions of species over time. However, discriminating various types of salt marsh is rather difficult because of their strong spectral similarities. Previous salt marsh mapping studies have focused mainly on high spatial and spectral (i.e., hyperspectral) resolution images combined with auxiliary information; however, the results are often limited to small regions. With a high temporal and moderate spatial resolution, the Chinese HuanJing-1 (HJ-1) satellite optical imagery can be used not only to monitor phenological changes of salt marsh vegetation over short-time intervals, but also to obtain coverage of large areas. Here, we apply HJ-1 satellite imagery to the middle coast of Jiangsu in east China to monitor changes in saltmarsh vegetation cover. First, we constructed a monthly NDVI time-series to classify various types of salt marsh and then we tested the possibility of using compressed time-series continuously, to broaden the applicability of this particular approach. Our principal findings are as follows: (1) the overall accuracy of salt marsh mapping based on the monthly NDVI time-series was 90.3%, which was ∼16.0% higher than the single-phase classification strategy; (2) a compressed time-series, including NDVI from six key months (April, June-September, and November), demonstrated very little reduction (2.3%) in overall accuracy but led to obvious improvements in unstable regions; and (3) a simple rule for Spartina alterniflora identification was established using a scene solely from November, which may provide an effective way for regularly monitoring its distribution.

Health Insurance Data

MedlinePlus

... define geographic areas and how geography changes over time. Latest Information ... audio files from the Census Bureau, including "Profile America," a daily series of bite-sized statistics, placing current data in ...

A Methodological Framework for Model Selection in Interrupted Time Series Studies.

PubMed

Lopez Bernal, J; Soumerai, S; Gasparrini, A

2018-06-06

Interrupted time series is a powerful and increasingly popular design for evaluating public health and health service interventions. The design involves analysing trends in the outcome of interest and estimating the change in trend following an intervention relative to the counterfactual (the expected ongoing trend if the intervention had not occurred). There are two key components to modelling this effect: first, defining the counterfactual; second, defining the type of effect that the intervention is expected to have on the outcome, known as the impact model. The counterfactual is defined by extrapolating the underlying trends observed before the intervention to the post-intervention period. In doing this, authors must consider the pre-intervention period that will be included, any time varying confounders, whether trends may vary within different subgroups of the population and whether trends are linear or non-linear. Defining the impact model involves specifying the parameters that model the intervention, including for instance whether to allow for an abrupt level change or a gradual slope change, whether to allow for a lag before any effect on the outcome, whether to allow a transition period during which the intervention is being implemented and whether a ceiling or floor effect might be expected. Inappropriate model specification can bias the results of an interrupted time series analysis and using a model that is not closely tailored to the intervention or testing multiple models increases the risk of false positives being detected. It is important that authors use substantive knowledge to customise their interrupted time series model a priori to the intervention and outcome under study. Where there is uncertainty in model specification, authors should consider using separate data sources to define the intervention, running limited sensitivity analyses or undertaking initial exploratory studies. Copyright © 2018. Published by Elsevier Inc.

Mapping croplands, cropping patterns, and crop types using MODIS time-series data

NASA Astrophysics Data System (ADS)

Chen, Yaoliang; Lu, Dengsheng; Moran, Emilio; Batistella, Mateus; Dutra, Luciano Vieira; Sanches, Ieda Del'Arco; da Silva, Ramon Felipe Bicudo; Huang, Jingfeng; Luiz, Alfredo José Barreto; de Oliveira, Maria Antonia Falcão

2018-07-01

The importance of mapping regional and global cropland distribution in timely ways has been recognized, but separation of crop types and multiple cropping patterns is challenging due to their spectral similarity. This study developed a new approach to identify crop types (including soy, cotton and maize) and cropping patterns (Soy-Maize, Soy-Cotton, Soy-Pasture, Soy-Fallow, Fallow-Cotton and Single crop) in the state of Mato Grosso, Brazil. The Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series data for 2015 and 2016 and field survey data were used in this research. The major steps of this proposed approach include: (1) reconstructing NDVI time series data by removing the cloud-contaminated pixels using the temporal interpolation algorithm, (2) identifying the best periods and developing temporal indices and phenological parameters to distinguish croplands from other land cover types, and (3) developing crop temporal indices to extract cropping patterns using NDVI time-series data and group cropping patterns into crop types. Decision tree classifier was used to map cropping patterns based on these temporal indices. Croplands from Landsat imagery in 2016, cropping pattern samples from field survey in 2016, and the planted area of crop types in 2015 were used for accuracy assessment. Overall accuracies of approximately 90%, 73% and 86%, respectively were obtained for croplands, cropping patterns, and crop types. The adjusted coefficients of determination of total crop, soy, maize, and cotton areas with corresponding statistical areas were 0.94, 0.94, 0.88 and 0.88, respectively. This research indicates that the proposed approach is promising for mapping large-scale croplands, their cropping patterns and crop types.

The analysis of the possibility of using 10-minute rainfall series to determine the maximum rainfall amount with 5 minutes duration

NASA Astrophysics Data System (ADS)

Kaźmierczak, Bartosz; Wartalska, Katarzyna; Wdowikowski, Marcin; Kotowski, Andrzej

2017-11-01

Modern scientific research in the area of heavy rainfall analysis regarding to the sewerage design indicates the need to develop and use probabilistic rain models. One of the issues that remains to be resolved is the length of the shortest amount of rain to be analyzed. It is commonly believed that the best time is 5 minutes, while the least rain duration measured by the national services is often 10 or even 15 minutes. Main aim of this paper is to present the difference between probabilistic rainfall models results given from rainfall time series including and excluding 5 minutes rainfall duration. Analysis were made for long-time period from 1961-2010 on polish meteorological station Legnica. To develop best fitted to measurement rainfall data probabilistic model 4 probabilistic distributions were used. Results clearly indicates that models including 5 minutes rainfall duration remains more appropriate to use.

Signal processing of anthropometric data

NASA Astrophysics Data System (ADS)

Zimmermann, W. J.

1983-09-01

The Anthropometric Measurements Laboratory has accumulated a large body of data from a number of previous experiments. The data is very noisy, therefore it requires the application of some signal processing schemes. Moreover, it was not regarded as time series measurements but as positional information; hence, the data is stored as coordinate points as defined by the motion of the human body. The accumulated data defines two groups or classes. Some of the data was collected from an experiment designed to measure the flexibility of the limbs, referred to as radial movement. The remaining data was collected from experiments designed to determine the surface of the reach envelope. An interactive signal processing package was designed and implemented. Since the data does not include time this package does not include a time series element. Presently the results is restricted to processing data obtained from those experiments designed to measure flexibility.

Signal processing of anthropometric data

NASA Technical Reports Server (NTRS)

Zimmermann, W. J.

1983-01-01

The Anthropometric Measurements Laboratory has accumulated a large body of data from a number of previous experiments. The data is very noisy, therefore it requires the application of some signal processing schemes. Moreover, it was not regarded as time series measurements but as positional information; hence, the data is stored as coordinate points as defined by the motion of the human body. The accumulated data defines two groups or classes. Some of the data was collected from an experiment designed to measure the flexibility of the limbs, referred to as radial movement. The remaining data was collected from experiments designed to determine the surface of the reach envelope. An interactive signal processing package was designed and implemented. Since the data does not include time this package does not include a time series element. Presently the results is restricted to processing data obtained from those experiments designed to measure flexibility.

Modern trends in Class III orthognathic treatment: A time series analysis.

PubMed

Lee, Chang-Hoon; Park, Hyun-Hee; Seo, Byoung-Moo; Lee, Shin-Jae

2017-03-01

To examine the current trends in surgical-orthodontic treatment for patients with Class III malocclusion using time-series analysis. The records of 2994 consecutive patients who underwent orthognathic surgery from January 1, 2004, through December 31, 2015, at Seoul National University Dental Hospital, Seoul, Korea, were reviewed. Clinical data from each surgical and orthodontic treatment record included patient's sex, age at the time of surgery, malocclusion classification, type of orthognathic surgical procedure, place where the orthodontic treatment was performed, orthodontic treatment modality, and time elapsed for pre- and postoperative orthodontic treatment. Out of the orthognathic surgery patients, 86% had Class III malocclusion. Among them, two-jaw surgeries have become by far the most common orthognathic surgical treatment these days. The age at the time of surgery and the number of new patients had seasonal variations, which demonstrated opposing patterns. There was neither positive nor negative correlation between pre- and postoperative orthodontic treatment time. Elapsed orthodontic treatment time for both before and after Class III orthognathic surgeries has been decreasing over the years. Results of the time series analysis might provide clinicians with some insights into current surgical and orthodontic management.

Detection of a sudden change of the field time series based on the Lorenz system

PubMed Central

Li, Fang; Shen, BingLu; Yan, PengCheng; Song, Jian; Ma, DeShan

2017-01-01

We conducted an exploratory study of the detection of a sudden change of the field time series based on the numerical solution of the Lorenz system. First, the time when the Lorenz path jumped between the regions on the left and right of the equilibrium point of the Lorenz system was quantitatively marked and the sudden change time of the Lorenz system was obtained. Second, the numerical solution of the Lorenz system was regarded as a vector; thus, this solution could be considered as a vector time series. We transformed the vector time series into a time series using the vector inner product, considering the geometric and topological features of the Lorenz system path. Third, the sudden change of the resulting time series was detected using the sliding t-test method. Comparing the test results with the quantitatively marked time indicated that the method could detect every sudden change of the Lorenz path, thus the method is effective. Finally, we used the method to detect the sudden change of the pressure field time series and temperature field time series, and obtained good results for both series, which indicates that the method can apply to high-dimension vector time series. Mathematically, there is no essential difference between the field time series and vector time series; thus, we provide a new method for the detection of the sudden change of the field time series. PMID:28141832

On the pressure field of nonlinear standing water waves

NASA Technical Reports Server (NTRS)

Schwartz, L. W.

1980-01-01

The pressure field produced by two dimensional nonlinear time and space periodic standing waves was calculated as a series expansion in the wave height. The high order series was summed by the use of Pade approximants. Calculations included the pressure variation at great depth, which was considered to be a likely cause of microseismic activity, and the pressure distribution on a vertical barrier or breakwater.

Optimal Subset Selection of Time-Series MODIS Images and Sample Data Transfer with Random Forests for Supervised Classification Modelling

PubMed Central

Zhou, Fuqun; Zhang, Aining

2016-01-01

Nowadays, various time-series Earth Observation data with multiple bands are freely available, such as Moderate Resolution Imaging Spectroradiometer (MODIS) datasets including 8-day composites from NASA, and 10-day composites from the Canada Centre for Remote Sensing (CCRS). It is challenging to efficiently use these time-series MODIS datasets for long-term environmental monitoring due to their vast volume and information redundancy. This challenge will be greater when Sentinel 2–3 data become available. Another challenge that researchers face is the lack of in-situ data for supervised modelling, especially for time-series data analysis. In this study, we attempt to tackle the two important issues with a case study of land cover mapping using CCRS 10-day MODIS composites with the help of Random Forests’ features: variable importance, outlier identification. The variable importance feature is used to analyze and select optimal subsets of time-series MODIS imagery for efficient land cover mapping, and the outlier identification feature is utilized for transferring sample data available from one year to an adjacent year for supervised classification modelling. The results of the case study of agricultural land cover classification at a regional scale show that using only about a half of the variables we can achieve land cover classification accuracy close to that generated using the full dataset. The proposed simple but effective solution of sample transferring could make supervised modelling possible for applications lacking sample data. PMID:27792152

Trend analysis of Arctic sea ice extent

NASA Astrophysics Data System (ADS)

Silva, M. E.; Barbosa, S. M.; Antunes, Luís; Rocha, Conceição

2009-04-01

The extent of Arctic sea ice is a fundamental parameter of Arctic climate variability. In the context of climate change, the area covered by ice in the Arctic is a particularly useful indicator of recent changes in the Arctic environment. Climate models are in near universal agreement that Arctic sea ice extent will decline through the 21st century as a consequence of global warming and many studies predict a ice free Arctic as soon as 2012. Time series of satellite passive microwave observations allow to assess the temporal changes in the extent of Arctic sea ice. Much of the analysis of the ice extent time series, as in most climate studies from observational data, have been focussed on the computation of deterministic linear trends by ordinary least squares. However, many different processes, including deterministic, unit root and long-range dependent processes can engender trend like features in a time series. Several parametric tests have been developed, mainly in econometrics, to discriminate between stationarity (no trend), deterministic trend and stochastic trends. Here, these tests are applied in the trend analysis of the sea ice extent time series available at National Snow and Ice Data Center. The parametric stationary tests, Augmented Dickey-Fuller (ADF), Phillips-Perron (PP) and the KPSS, do not support an overall deterministic trend in the time series of Arctic sea ice extent. Therefore, alternative parametrizations such as long-range dependence should be considered for characterising long-term Arctic sea ice variability.

Neural networks and traditional time series methods: a synergistic combination in state economic forecasts.

PubMed

Hansen, J V; Nelson, R D

1997-01-01

Ever since the initial planning for the 1997 Utah legislative session, neural-network forecasting techniques have provided valuable insights for analysts forecasting tax revenues. These revenue estimates are critically important since agency budgets, support for education, and improvements to infrastructure all depend on their accuracy. Underforecasting generates windfalls that concern taxpayers, whereas overforecasting produces budget shortfalls that cause inadequately funded commitments. The pattern finding ability of neural networks gives insightful and alternative views of the seasonal and cyclical components commonly found in economic time series data. Two applications of neural networks to revenue forecasting clearly demonstrate how these models complement traditional time series techniques. In the first, preoccupation with a potential downturn in the economy distracts analysis based on traditional time series methods so that it overlooks an emerging new phenomenon in the data. In this case, neural networks identify the new pattern that then allows modification of the time series models and finally gives more accurate forecasts. In the second application, data structure found by traditional statistical tools allows analysts to provide neural networks with important information that the networks then use to create more accurate models. In summary, for the Utah revenue outlook, the insights that result from a portfolio of forecasts that includes neural networks exceeds the understanding generated from strictly statistical forecasting techniques. In this case, the synergy clearly results in the whole of the portfolio of forecasts being more accurate than the sum of the individual parts.

Optimal Subset Selection of Time-Series MODIS Images and Sample Data Transfer with Random Forests for Supervised Classification Modelling.

PubMed

Zhou, Fuqun; Zhang, Aining

2016-10-25

Nowadays, various time-series Earth Observation data with multiple bands are freely available, such as Moderate Resolution Imaging Spectroradiometer (MODIS) datasets including 8-day composites from NASA, and 10-day composites from the Canada Centre for Remote Sensing (CCRS). It is challenging to efficiently use these time-series MODIS datasets for long-term environmental monitoring due to their vast volume and information redundancy. This challenge will be greater when Sentinel 2-3 data become available. Another challenge that researchers face is the lack of in-situ data for supervised modelling, especially for time-series data analysis. In this study, we attempt to tackle the two important issues with a case study of land cover mapping using CCRS 10-day MODIS composites with the help of Random Forests' features: variable importance, outlier identification. The variable importance feature is used to analyze and select optimal subsets of time-series MODIS imagery for efficient land cover mapping, and the outlier identification feature is utilized for transferring sample data available from one year to an adjacent year for supervised classification modelling. The results of the case study of agricultural land cover classification at a regional scale show that using only about a half of the variables we can achieve land cover classification accuracy close to that generated using the full dataset. The proposed simple but effective solution of sample transferring could make supervised modelling possible for applications lacking sample data.

Radiation: Time, Space and Spirit--Keys to Scientific Literacy Series.

ERIC Educational Resources Information Center

Stonebarger, Bill

This discussion of radiation considers the spectrum of electromagnetic energy including light, x-rays, radioactivity, and other waves. Radiation is considered from three aspects; time, space, and spirit. Time refers to a sense of history; space refers to geography; and spirit refers to life and thought. Several chapters on the history and concepts…

Status and trends of the Lake Huron offshore demersal fish community, 1976-2015

USGS Publications Warehouse

Roseman, Edward; Chriscinske, Margret Ann; Castle, Dana Kristina; Prichard, Carson G.

2016-01-01

The USGS Great Lakes Science Center has conducted trawl surveys to assess annual changes in the offshore demersal fish community of Lake Huron since 1973. Sample sites include five ports in U.S. waters with less frequent sampling near Goderich, Ontario. The 2015 fall bottom trawl survey was carried out between 14 and 28 October and included all U.S. ports, as well as Goderich, ON. The 2015 main basin prey fish biomass estimate for Lake Huron was 19.4 kilotonnes, a decline of about 50 percent from 2014. This estimate is the second lowest in the time series, and is approximately 5 percent of the maximum estimate in the time series observed in 1987. No adult alewife were collected in 2015 and YOY alewife was the second lowest in the time series, up slightly from the record low in 2014. The estimated biomass of yearling and older rainbow smelt also decreased and was the lowest observed in the time series. Estimated adult bloater biomass in Lake Huron declined to about half of the 2014 estimate. YOY alewife, rainbow smelt, and bloater abundance and biomass decreased over 2014. Biomass estimates for deepwater sculpins declined while trout-perch and ninespine stickleback increased over 2014 values, but all remained low compared to historic estimates. The 2014 biomass estimate for round goby increased from 2014 but remains at only 7 percent of the maximum observed in 2003. Wild juvenile lake trout were captured again in 2015, suggesting that natural reproduction by lake trout continues to occur.

The Relationship of Item-Level Response Times with Test-Taker and Item Variables in an Operational CAT Environment. LSAC Research Report Series.

ERIC Educational Resources Information Center

Swygert, Kimberly A.

In this study, data from an operational computerized adaptive test (CAT) were examined in order to gather information concerning item response times in a CAT environment. The CAT under study included multiple-choice items measuring verbal, quantitative, and analytical reasoning. The analyses included the fitting of regression models describing the…

A lower and more constrained estimate of climate sensitivity using updated observations and detailed radiative forcing time series

NASA Astrophysics Data System (ADS)

Skeie, R. B.; Berntsen, T.; Aldrin, M.; Holden, M.; Myhre, G.

2012-04-01

A key question in climate science is to quantify the sensitivity of the climate system to perturbation in the radiative forcing (RF). This sensitivity is often represented by the equilibrium climate sensitivity, but this quantity is poorly constrained with significant probabilities for high values. In this work the equilibrium climate sensitivity (ECS) is estimated based on observed near-surface temperature change from the instrumental record, changes in ocean heat content and detailed RF time series. RF time series from pre-industrial times to 2010 for all main anthropogenic and natural forcing mechanisms are estimated and the cloud lifetime effect and the semi-direct effect, which are not RF mechanisms in a strict sense, are included in the analysis. The RF time series are linked to the observations of ocean heat content and temperature change through an energy balance model and a stochastic model, using a Bayesian approach to estimate the ECS from the data. The posterior mean of the ECS is 1.9˚C with 90% credible interval (C.I.) ranging from 1.2 to 2.9˚C, which is tighter than previously published estimates. Observational data up to and including year 2010 are used in this study. This is at least ten additional years compared to the majority of previously published studies that have used the instrumental record in attempts to constrain the ECS. We show that the additional 10 years of data, and especially 10 years of additional ocean heat content data, have significantly narrowed the probability density function of the ECS. If only data up to and including year 2000 are used in the analysis, the 90% C.I. is 1.4 to 10.6˚C with a pronounced heavy tail in line with previous estimates of ECS constrained by observations in the 20th century. Also the transient climate response (TCR) is estimated in this study. Using observational data up to and including year 2010 gives a 90% C.I. of 1.0 to 2.1˚C, while the 90% C.I. is significantly broader ranging from 1.1 to 3.4 ˚C if only data up to and including year 2000 is used.

Multiple Indicator Stationary Time Series Models.

ERIC Educational Resources Information Center

Sivo, Stephen A.

2001-01-01

Discusses the propriety and practical advantages of specifying multivariate time series models in the context of structural equation modeling for time series and longitudinal panel data. For time series data, the multiple indicator model specification improves on classical time series analysis. For panel data, the multiple indicator model…

Quantification of Toxic Effects for Water Concentration-based Aquatic Life Criteria -Part B

EPA Science Inventory

Erickson et al. (1991) conducted a series of experiments on the toxicity of pentachloroethane (PCE) to juvenile fathead minnows. These experiments included evaluations of bioaccumulation kinetics, the time-course of mortality under both constant and time-variable exposures, the r...

Challenges in Extracting Information From Large Hydrogeophysical-monitoring Datasets

NASA Astrophysics Data System (ADS)

Day-Lewis, F. D.; Slater, L. D.; Johnson, T.

2012-12-01

Over the last decade, new automated geophysical data-acquisition systems have enabled collection of increasingly large and information-rich geophysical datasets. Concurrent advances in field instrumentation, web services, and high-performance computing have made real-time processing, inversion, and visualization of large three-dimensional tomographic datasets practical. Geophysical-monitoring datasets have provided high-resolution insights into diverse hydrologic processes including groundwater/surface-water exchange, infiltration, solute transport, and bioremediation. Despite the high information content of such datasets, extraction of quantitative or diagnostic hydrologic information is challenging. Visual inspection and interpretation for specific hydrologic processes is difficult for datasets that are large, complex, and (or) affected by forcings (e.g., seasonal variations) unrelated to the target hydrologic process. New strategies are needed to identify salient features in spatially distributed time-series data and to relate temporal changes in geophysical properties to hydrologic processes of interest while effectively filtering unrelated changes. Here, we review recent work using time-series and digital-signal-processing approaches in hydrogeophysics. Examples include applications of cross-correlation, spectral, and time-frequency (e.g., wavelet and Stockwell transforms) approaches to (1) identify salient features in large geophysical time series; (2) examine correlation or coherence between geophysical and hydrologic signals, even in the presence of non-stationarity; and (3) condense large datasets while preserving information of interest. Examples demonstrate analysis of large time-lapse electrical tomography and fiber-optic temperature datasets to extract information about groundwater/surface-water exchange and contaminant transport.

Forecasting of cyanobacterial density in Torrão reservoir using artificial neural networks.

PubMed

Torres, Rita; Pereira, Elisa; Vasconcelos, Vítor; Teles, Luís Oliva

2011-06-01

The ability of general regression neural networks (GRNN) to forecast the density of cyanobacteria in the Torrão reservoir (Tâmega river, Portugal), in a period of 15 days, based on three years of collected physical and chemical data, was assessed. Several models were developed and 176 were selected based on their correlation values for the verification series. A time lag of 11 was used, equivalent to one sample (periods of 15 days in the summer and 30 days in the winter). Several combinations of the series were used. Input and output data collected from three depths of the reservoir were applied (surface, euphotic zone limit and bottom). The model that presented a higher average correlation value presented the correlations 0.991; 0.843; 0.978 for training, verification and test series. This model had the three series independent in time: first test series, then verification series and, finally, training series. Only six input variables were considered significant to the performance of this model: ammonia, phosphates, dissolved oxygen, water temperature, pH and water evaporation, physical and chemical parameters referring to the three depths of the reservoir. These variables are common to the next four best models produced and, although these included other input variables, their performance was not better than the selected best model.

Firefly Algorithm in detection of TEC seismo-ionospheric anomalies

NASA Astrophysics Data System (ADS)

Akhoondzadeh, Mehdi

2015-07-01

Anomaly detection in time series of different earthquake precursors is an essential introduction to create an early warning system with an allowable uncertainty. Since these time series are more often non linear, complex and massive, therefore the applied predictor method should be able to detect the discord patterns from a large data in a short time. This study acknowledges Firefly Algorithm (FA) as a simple and robust predictor to detect the TEC (Total Electron Content) seismo-ionospheric anomalies around the time of the some powerful earthquakes including Chile (27 February 2010), Varzeghan (11 August 2012) and Saravan (16 April 2013). Outstanding anomalies were observed 7 and 5 days before the Chile and Varzeghan earthquakes, respectively and also 3 and 8 days prior to the Saravan earthquake.

Efficient Algorithms for Segmentation of Item-Set Time Series

NASA Astrophysics Data System (ADS)

Chundi, Parvathi; Rosenkrantz, Daniel J.

We propose a special type of time series, which we call an item-set time series, to facilitate the temporal analysis of software version histories, email logs, stock market data, etc. In an item-set time series, each observed data value is a set of discrete items. We formalize the concept of an item-set time series and present efficient algorithms for segmenting a given item-set time series. Segmentation of a time series partitions the time series into a sequence of segments where each segment is constructed by combining consecutive time points of the time series. Each segment is associated with an item set that is computed from the item sets of the time points in that segment, using a function which we call a measure function. We then define a concept called the segment difference, which measures the difference between the item set of a segment and the item sets of the time points in that segment. The segment difference values are required to construct an optimal segmentation of the time series. We describe novel and efficient algorithms to compute segment difference values for each of the measure functions described in the paper. We outline a dynamic programming based scheme to construct an optimal segmentation of the given item-set time series. We use the item-set time series segmentation techniques to analyze the temporal content of three different data sets—Enron email, stock market data, and a synthetic data set. The experimental results show that an optimal segmentation of item-set time series data captures much more temporal content than a segmentation constructed based on the number of time points in each segment, without examining the item set data at the time points, and can be used to analyze different types of temporal data.

A novel weight determination method for time series data aggregation

NASA Astrophysics Data System (ADS)

Xu, Paiheng; Zhang, Rong; Deng, Yong

2017-09-01

Aggregation in time series is of great importance in time series smoothing, predicting and other time series analysis process, which makes it crucial to address the weights in times series correctly and reasonably. In this paper, a novel method to obtain the weights in time series is proposed, in which we adopt induced ordered weighted aggregation (IOWA) operator and visibility graph averaging (VGA) operator and linearly combine the weights separately generated by the two operator. The IOWA operator is introduced to the weight determination of time series, through which the time decay factor is taken into consideration. The VGA operator is able to generate weights with respect to the degree distribution in the visibility graph constructed from the corresponding time series, which reflects the relative importance of vertices in time series. The proposed method is applied to two practical datasets to illustrate its merits. The aggregation of Construction Cost Index (CCI) demonstrates the ability of proposed method to smooth time series, while the aggregation of The Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) illustrate how proposed method maintain the variation tendency of original data.

Fifth-order complex Korteweg-de Vries-type equations

NASA Astrophysics Data System (ADS)

Khanal, Netra; Wu, Jiahong; Yuan, Juan-Ming

2012-05-01

This paper studies spatially periodic complex-valued solutions of the fifth-order Korteweg-de Vries (KdV)-type equations. The aim is at several fundamental issues including the existence, uniqueness and finite-time blowup problems. Special attention is paid to the Kawahara equation, a fifth-order KdV-type equation. When a Burgers dissipation is attached to the Kawahara equation, we establish the existence and uniqueness of the Fourier series solution with the Fourier modes decaying algebraically in terms of the wave numbers. We also examine a special series solution to the Kawahara equation and prove the convergence and global regularity of such solutions associated with a single mode initial data. In addition, finite-time blowup results are discussed for the special series solution of the Kawahara equation.

A randomized intervention of reminder letter for human papillomavirus vaccine series completion.

PubMed

Chao, Chun; Preciado, Melissa; Slezak, Jeff; Xu, Lanfang

2015-01-01

Completion rate for the three-dose series of the human papillomavirus (HPV) vaccine has generally been low. This study evaluated the effectiveness of a reminder letter intervention on HPV vaccine three-dose series completion. Female members of Kaiser Permanente Southern California Health Plan who received at least one dose, but not more than two doses, of the HPV vaccine by February 13, 2013, and who were between ages 9 and 26 years at the time of first HPV vaccination were included. Eighty percent of these females were randomized to receive the reminder letter, and 20% were randomized to receive standard of care (control). The reminder letters were mailed quarterly to those who had not completed the series. The proportion of series completion at the end of the 12-month evaluation period was compared using chi-square test. A total of 9,760 females were included in the intervention group and 2,445 in the control group. HPV vaccine series completion was 56.4% in the intervention group and 46.6% in the control groups (p < .001). The effect of the intervention appeared to be stronger in girls aged 9-17 years compared with young women aged 18-26 years at the first dose and in blacks compared with whites. Reminder letters scheduled quarterly were effective to enhance HPV vaccine series completion among those who initiated the vaccine. However, a large gap in series completion remained despite the intervention. Future studies should address other barriers to series completion, including those at the providers and the health care system level. Copyright © 2015 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Land science with Sentinel-2 and Sentinel-3 data series synergy

NASA Astrophysics Data System (ADS)

Moreno, Jose; Guanter, Luis; Alonso, Luis; Gomez, Luis; Amoros, Julia; Camps, Gustavo; Delegido, Jesus

2010-05-01

Although the GMES/Sentinel satellite series were primarily designed to provide observations for operational services and routine applications, there is a growing interest in the scientific community towards the usage of Sentinel data for more advanced and innovative science. Apart from the improved spatial and spectral capabilities, the availability of consistent time series covering a period of over 20 years opens possibilities never explored before, such as systematic data assimilation approaches exploiting the time-series concept, or the incorporation in the modelling approaches of processes covering time scales from weeks to decades. Sentinel-3 will provide continuity to current ENVISAT MERIS/AATSR capabilities. The results already derived from MERIS/AATRS will be more systematically exploited by using OLCI in synergy with SLST. Particularly innovative is the case of Sentinel-2, which is specifically designed for land applications. Built on a constellation of two satellites operating simultaneously to provide 5 days geometric revisit time, the Sentinel-2 system will providing global and systematic acquisitions with high spatial resolution and with a high revisit time tailored towards the needs of land monitoring. Apart from providing continuity to Landsat and SPOT time series, the Sentinel-2 Multi-Spectral Instrument (MSI) incorporates new narrow bands around the red-edge for improved retrievals of biophysical parameters. The limitations imposed by the need of a proper cloud screening and atmospheric corrections have represented a serious constraint in the past for optical data. The fact that both Sentinel-2 and 3 have dedicated bands to allow such needed corrections for optical data represents an important step towards a proper exploitation, guarantying consistent time series showing actual variability in land surface conditions without the artefacts introduced by the atmosphere. Expected operational products (such as Land Cover maps, Leaf Area Index, Fractional Vegetation Cover, Fraction of Absorbed Photosynthetically Active Radiation, and Leaf Chlorophyll and Water Contents), will be enhanced with new scientific applications. Higher level products will also be provided, by means of mosaicking, averaging, synthesising or compositing of spatially and temporally resampled data. A key element in the exploitation of the Sentinel series will be the adequate use of data synergy, which will open new possibilities for improved Land Models. This paper analyses in particular the possibilities offered by mosaicking and compositing information derived from Sentinel-2 observations in high spatial resolution to complement dense time series derived from Sentinel-3 data with more frequent coverage. Interpolation of gaps in high spatial resolution time series (from Sentinel-2 data) by using medium/low resolution data from Sentinel-3 (OLCI and SLSTR) is also a way of making series more temporally consistent with high spatial resolution. The primary goal of such temporal interpolation / spatial mosaicking techniques is to derive consistent surface reflectance data virtually for every date and geographical location, no matter the initial spatial/temporal coverage of the original data used to produce the composite. As a result, biophysical products can be derived in a more consistent way from the spectral information of Sentinel-3 data by making use of a description of surface heterogeneity derived from Sentinel-2 data. Using data from dedicated experiments (SEN2FLEX, CEFLES2, SEN3EXP), that include a large dataset of satellite and airborne data and of ground-based measurements of atmospheric and vegetation parameters, different techniques are tested, including empirical / statistical approaches that builds nonlinear regression by mapping spectra to a high dimensional space, up to model inversion / data assimilation scenarios. Exploitation of the temporal domain and spatial multi-scale domain becomes then a driver for the systematic exploitation of GMES/Sentinels data time series. This paper review current status, and identifies research priorities in such direction.

Updating Landsat time series of surface-reflectance composites and forest change products with new observations

NASA Astrophysics Data System (ADS)

Hermosilla, Txomin; Wulder, Michael A.; White, Joanne C.; Coops, Nicholas C.; Hobart, Geordie W.

2017-12-01

The use of time series satellite data allows for the temporally dense, systematic, transparent, and synoptic capture of land dynamics over time. Subsequent to the opening of the Landsat archive, several time series approaches for characterizing landscape change have been developed, often representing a particular analytical time window. The information richness and widespread utility of these time series data have created a need to maintain the currency of time series information via the addition of new data, as it becomes available. When an existing time series is temporally extended, it is critical that previously generated change information remains consistent, thereby not altering reported change statistics or science outcomes based on that change information. In this research, we investigate the impacts and implications of adding additional years to an existing 29-year annual Landsat time series for forest change. To do so, we undertook a spatially explicit comparison of the 29 overlapping years of a time series representing 1984-2012, with a time series representing 1984-2016. Surface reflectance values, and presence, year, and type of change were compared. We found that the addition of years to extend the time series had minimal effect on the annual surface reflectance composites, with slight band-specific differences (r ≥ 0.1) in the final years of the original time series being updated. The area of stand replacing disturbances and determination of change year are virtually unchanged for the overlapping period between the two time-series products. Over the overlapping temporal period (1984-2012), the total area of change differs by 0.53%, equating to an annual difference in change area of 0.019%. Overall, the spatial and temporal agreement of the changes detected by both time series was 96%. Further, our findings suggest that the entire pre-existing historic time series does not need to be re-processed during the update process. Critically, given the time series change detection and update approach followed here, science outcomes or reports representing one temporal epoch can be considered stable and will not be altered when a time series is updated with newly available data.

Understanding inhibitory mechanisms of lumbar spinal manipulation using H-reflex and F-wave responses: a methodological approach.

PubMed

Dishman, J Donald; Weber, Kenneth A; Corbin, Roger L; Burke, Jeanmarie R

2012-09-30

The purpose of this research was to characterize unique neurophysiologic events following a high velocity, low amplitude (HVLA) spinal manipulation (SM) procedure. Descriptive time series analysis techniques of time plots, outlier detection and autocorrelation functions were applied to time series of tibial nerve H-reflexes that were evoked at 10-s intervals from 100 s before the event until 100 s after three distinct events L5-S1 HVLA SM, or a L5-S1 joint pre-loading procedure, or the control condition. Sixty-six subjects were randomly assigned to three procedures, i.e., 22 time series per group. If the detection of outliers and correlograms revealed a pattern of non-randomness that was only time-locked to a single, specific event in the normalized time series, then an experimental effect would be inferred beyond the inherent variability of H-reflex responses. Tibial nerve F-wave responses were included to determine if any new information about central nervous function following a HVLA SM procedure could be ascertained. Time series analyses of H(max)/M(max) ratios, pre-post L5-S1 HVLA SM, substantiated the hypothesis that the specific aspects of the manipulative thrust lead to a greater attenuation of the H(max)/M(max) ratio as compared to the non-specific aspects related to the postural perturbation and joint pre-loading. The attenuation of the H(max)/M(max) ratio following the HVLA SM procedure was reliable and may hold promise as a translational tool to measure the consistency and accuracy of protocol implementation involving SM in clinical trials research. F-wave responses were not sensitive to mechanical perturbations of the lumbar spine. Copyright © 2012 Elsevier B.V. All rights reserved.

Time-series analysis to study the impact of an intersection on dispersion along a street canyon.

PubMed

Richmond-Bryant, Jennifer; Eisner, Alfred D; Hahn, Intaek; Fortune, Christopher R; Drake-Richman, Zora E; Brixey, Laurie A; Talih, M; Wiener, Russell W; Ellenson, William D

2009-12-01

This paper presents data analysis from the Brooklyn Traffic Real-Time Ambient Pollutant Penetration and Environmental Dispersion (B-TRAPPED) study to assess the transport of ultrafine particulate matter (PM) across urban intersections. Experiments were performed in a street canyon perpendicular to a highway in Brooklyn, NY, USA. Real-time ultrafine PM samplers were positioned on either side of an intersection at multiple locations along a street to collect time-series number concentration data. Meteorology equipment was positioned within the street canyon and at an upstream background site to measure wind speed and direction. Time-series analysis was performed on the PM data to compute a transport velocity along the direction of the street for the cases where background winds were parallel and perpendicular to the street. The data were analyzed for sampler pairs located (1) on opposite sides of the intersection and (2) on the same block. The time-series analysis demonstrated along-street transport, including across the intersection when background winds were parallel to the street canyon and there was minimal transport and no communication across the intersection when background winds were perpendicular to the street canyon. Low but significant values of the cross-correlation function (CCF) underscore the turbulent nature of plume transport along the street canyon. The low correlations suggest that flow switching around corners or traffic-induced turbulence at the intersection may have aided dilution of the PM plume from the highway. This observation supports similar findings in the literature. Furthermore, the time-series analysis methodology applied in this study is introduced as a technique for studying spatiotemporal variation in the urban microscale environment.

Quantification and clustering of phenotypic screening data using time-series analysis for chemotherapy of schistosomiasis.

PubMed

Lee, Hyokyeong; Moody-Davis, Asher; Saha, Utsab; Suzuki, Brian M; Asarnow, Daniel; Chen, Steven; Arkin, Michelle; Caffrey, Conor R; Singh, Rahul

2012-01-01

Neglected tropical diseases, especially those caused by helminths, constitute some of the most common infections of the world's poorest people. Development of techniques for automated, high-throughput drug screening against these diseases, especially in whole-organism settings, constitutes one of the great challenges of modern drug discovery. We present a method for enabling high-throughput phenotypic drug screening against diseases caused by helminths with a focus on schistosomiasis. The proposed method allows for a quantitative analysis of the systemic impact of a drug molecule on the pathogen as exhibited by the complex continuum of its phenotypic responses. This method consists of two key parts: first, biological image analysis is employed to automatically monitor and quantify shape-, appearance-, and motion-based phenotypes of the parasites. Next, we represent these phenotypes as time-series and show how to compare, cluster, and quantitatively reason about them using techniques of time-series analysis. We present results on a number of algorithmic issues pertinent to the time-series representation of phenotypes. These include results on appropriate representation of phenotypic time-series, analysis of different time-series similarity measures for comparing phenotypic responses over time, and techniques for clustering such responses by similarity. Finally, we show how these algorithmic techniques can be used for quantifying the complex continuum of phenotypic responses of parasites. An important corollary is the ability of our method to recognize and rigorously group parasites based on the variability of their phenotypic response to different drugs. The methods and results presented in this paper enable automatic and quantitative scoring of high-throughput phenotypic screens focused on helmintic diseases. Furthermore, these methods allow us to analyze and stratify parasites based on their phenotypic response to drugs. Together, these advancements represent a significant breakthrough for the process of drug discovery against schistosomiasis in particular and can be extended to other helmintic diseases which together afflict a large part of humankind.

Quantification and clustering of phenotypic screening data using time-series analysis for chemotherapy of schistosomiasis

PubMed Central

2012-01-01

Background Neglected tropical diseases, especially those caused by helminths, constitute some of the most common infections of the world's poorest people. Development of techniques for automated, high-throughput drug screening against these diseases, especially in whole-organism settings, constitutes one of the great challenges of modern drug discovery. Method We present a method for enabling high-throughput phenotypic drug screening against diseases caused by helminths with a focus on schistosomiasis. The proposed method allows for a quantitative analysis of the systemic impact of a drug molecule on the pathogen as exhibited by the complex continuum of its phenotypic responses. This method consists of two key parts: first, biological image analysis is employed to automatically monitor and quantify shape-, appearance-, and motion-based phenotypes of the parasites. Next, we represent these phenotypes as time-series and show how to compare, cluster, and quantitatively reason about them using techniques of time-series analysis. Results We present results on a number of algorithmic issues pertinent to the time-series representation of phenotypes. These include results on appropriate representation of phenotypic time-series, analysis of different time-series similarity measures for comparing phenotypic responses over time, and techniques for clustering such responses by similarity. Finally, we show how these algorithmic techniques can be used for quantifying the complex continuum of phenotypic responses of parasites. An important corollary is the ability of our method to recognize and rigorously group parasites based on the variability of their phenotypic response to different drugs. Conclusions The methods and results presented in this paper enable automatic and quantitative scoring of high-throughput phenotypic screens focused on helmintic diseases. Furthermore, these methods allow us to analyze and stratify parasites based on their phenotypic response to drugs. Together, these advancements represent a significant breakthrough for the process of drug discovery against schistosomiasis in particular and can be extended to other helmintic diseases which together afflict a large part of humankind. PMID:22369037

Highly comparative time-series analysis: the empirical structure of time series and their methods.

PubMed

Fulcher, Ben D; Little, Max A; Jones, Nick S

2013-06-06

The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines.

Highly comparative time-series analysis: the empirical structure of time series and their methods

PubMed Central

Fulcher, Ben D.; Little, Max A.; Jones, Nick S.

2013-01-01

The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines. PMID:23554344

A new method of real-time detection of changes in periodic data stream

NASA Astrophysics Data System (ADS)

Lyu, Chen; Lu, Guoliang; Cheng, Bin; Zheng, Xiangwei

2017-07-01

The change point detection in periodic time series is much desirable in many practical usages. We present a novel algorithm for this task, which includes two phases: 1) anomaly measure- on the basis of a typical regression model, we propose a new computation method to measure anomalies in time series which does not require any reference data from other measurement(s); 2) change detection- we introduce a new martingale test for detection which can be operated in an unsupervised and nonparametric way. We have conducted extensive experiments to systematically test our algorithm. The results make us believe that our algorithm can be directly applicable in many real-world change-point-detection applications.

A Framework and Algorithms for Multivariate Time Series Analytics (MTSA): Learning, Monitoring, and Recommendation

ERIC Educational Resources Information Center

Ngan, Chun-Kit

2013-01-01

Making decisions over multivariate time series is an important topic which has gained significant interest in the past decade. A time series is a sequence of data points which are measured and ordered over uniform time intervals. A multivariate time series is a set of multiple, related time series in a particular domain in which domain experts…

Kernel canonical-correlation Granger causality for multiple time series

NASA Astrophysics Data System (ADS)

Wu, Guorong; Duan, Xujun; Liao, Wei; Gao, Qing; Chen, Huafu

2011-04-01

Canonical-correlation analysis as a multivariate statistical technique has been applied to multivariate Granger causality analysis to infer information flow in complex systems. It shows unique appeal and great superiority over the traditional vector autoregressive method, due to the simplified procedure that detects causal interaction between multiple time series, and the avoidance of potential model estimation problems. However, it is limited to the linear case. Here, we extend the framework of canonical correlation to include the estimation of multivariate nonlinear Granger causality for drawing inference about directed interaction. Its feasibility and effectiveness are verified on simulated data.

Implementation of Automatic Clustering Algorithm and Fuzzy Time Series in Motorcycle Sales Forecasting

NASA Astrophysics Data System (ADS)

Rasim; Junaeti, E.; Wirantika, R.

2018-01-01

Accurate forecasting for the sale of a product depends on the forecasting method used. The purpose of this research is to build motorcycle sales forecasting application using Fuzzy Time Series method combined with interval determination using automatic clustering algorithm. Forecasting is done using the sales data of motorcycle sales in the last ten years. Then the error rate of forecasting is measured using Means Percentage Error (MPE) and Means Absolute Percentage Error (MAPE). The results of forecasting in the one-year period obtained in this study are included in good accuracy.

A Review of Subsequence Time Series Clustering

PubMed Central

Teh, Ying Wah

2014-01-01

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies. PMID:25140332

A review of subsequence time series clustering.

PubMed

Zolhavarieh, Seyedjamal; Aghabozorgi, Saeed; Teh, Ying Wah

2014-01-01

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.

Engineering Probiotics that Improve Warfighter Performance by Maintaining Lean Body Mass and Inhibiting Anxiety

DTIC Science & Technology

2017-10-03

and Microbiome Research Seminar Series . Baylor College of Medicine. 10/26/16. 12. "Rewiring the DNA binding domains ofbacterial two-component system...Structural and Quantitative Biology Seminar Series . 11/16/15. 16. "Engineering bacterial two component signal transduction systems to function as sensors...hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and

Multiscale structure of time series revealed by the monotony spectrum.

PubMed

Vamoş, Călin

2017-03-01

Observation of complex systems produces time series with specific dynamics at different time scales. The majority of the existing numerical methods for multiscale analysis first decompose the time series into several simpler components and the multiscale structure is given by the properties of their components. We present a numerical method which describes the multiscale structure of arbitrary time series without decomposing them. It is based on the monotony spectrum defined as the variation of the mean amplitude of the monotonic segments with respect to the mean local time scale during successive averagings of the time series, the local time scales being the durations of the monotonic segments. The maxima of the monotony spectrum indicate the time scales which dominate the variations of the time series. We show that the monotony spectrum can correctly analyze a diversity of artificial time series and can discriminate the existence of deterministic variations at large time scales from the random fluctuations. As an application we analyze the multifractal structure of some hydrological time series.

Phase correction and error estimation in InSAR time series analysis

NASA Astrophysics Data System (ADS)

Zhang, Y.; Fattahi, H.; Amelung, F.

2017-12-01

During the last decade several InSAR time series approaches have been developed in response to the non-idea acquisition strategy of SAR satellites, such as large spatial and temporal baseline with non-regular acquisitions. The small baseline tubes and regular acquisitions of new SAR satellites such as Sentinel-1 allows us to form fully connected networks of interferograms and simplifies the time series analysis into a weighted least square inversion of an over-determined system. Such robust inversion allows us to focus more on the understanding of different components in InSAR time-series and its uncertainties. We present an open-source python-based package for InSAR time series analysis, called PySAR (https://yunjunz.github.io/PySAR/), with unique functionalities for obtaining unbiased ground displacement time-series, geometrical and atmospheric correction of InSAR data and quantifying the InSAR uncertainty. Our implemented strategy contains several features including: 1) improved spatial coverage using coherence-based network of interferograms, 2) unwrapping error correction using phase closure or bridging, 3) tropospheric delay correction using weather models and empirical approaches, 4) DEM error correction, 5) optimal selection of reference date and automatic outlier detection, 6) InSAR uncertainty due to the residual tropospheric delay, decorrelation and residual DEM error, and 7) variance-covariance matrix of final products for geodetic inversion. We demonstrate the performance using SAR datasets acquired by Cosmo-Skymed and TerraSAR-X, Sentinel-1 and ALOS/ALOS-2, with application on the highly non-linear volcanic deformation in Japan and Ecuador (figure 1). Our result shows precursory deformation before the 2015 eruptions of Cotopaxi volcano, with a maximum uplift of 3.4 cm on the western flank (fig. 1b), with a standard deviation of 0.9 cm (fig. 1a), supporting the finding by Morales-Rivera et al. (2017, GRL); and a post-eruptive subsidence on the same area, with a maximum of -3 +/- 0.9 cm (fig. 1c). Time-series displacement map (fig. 2) shows a highly non-linear deformation behavior, indicating the complicated magma propagation process during this eruption cycle.

Reconstructing land use history from Landsat time-series. Case study of a swidden agriculture system in Brazil

NASA Astrophysics Data System (ADS)

Dutrieux, Loïc P.; Jakovac, Catarina C.; Latifah, Siti H.; Kooistra, Lammert

2016-05-01

We developed a method to reconstruct land use history from Landsat images time-series. The method uses a breakpoint detection framework derived from the econometrics field and applicable to time-series regression models. The Breaks For Additive Season and Trend (BFAST) framework is used for defining the time-series regression models which may contain trend and phenology, hence appropriately modelling vegetation intra and inter-annual dynamics. All available Landsat data are used for a selected study area, and the time-series are partitioned into segments delimited by breakpoints. Segments can be associated to land use regimes, while the breakpoints then correspond to shifts in land use regimes. In order to further characterize these shifts, we classified the unlabelled breakpoints returned by the algorithm into their corresponding processes. We used a Random Forest classifier, trained from a set of visually interpreted time-series profiles to infer the processes and assign labels to the breakpoints. The whole approach was applied to quantifying the number of cultivation cycles in a swidden agriculture system in Brazil (state of Amazonas). Number and frequency of cultivation cycles is of particular ecological relevance in these systems since they largely affect the capacity of the forest to regenerate after land abandonment. We applied the method to a Landsat time-series of Normalized Difference Moisture Index (NDMI) spanning the 1984-2015 period and derived from it the number of cultivation cycles during that period at the individual field scale level. Agricultural fields boundaries used to apply the method were derived using a multi-temporal segmentation approach. We validated the number of cultivation cycles predicted by the method against in-situ information collected from farmers interviews, resulting in a Normalized Residual Mean Squared Error (NRMSE) of 0.25. Overall the method performed well, producing maps with coherent spatial patterns. We identified various sources of error in the approach, including low data availability in the 90s and sub-object mixture of land uses. We conclude that the method holds great promise for land use history mapping in the tropics and beyond.

Reconstructing Land Use History from Landsat Time-Series. Case study of Swidden Agriculture Intensification in Brazil

NASA Astrophysics Data System (ADS)

Dutrieux, L.; Jakovac, C. C.; Siti, L. H.; Kooistra, L.

2015-12-01

We developed a method to reconstruct land use history from Landsat images time-series. The method uses a breakpoint detection framework derived from the econometrics field and applicable to time-series regression models. The BFAST framework is used for defining the time-series regression models which may contain trend and phenology, hence appropriately modelling vegetation intra and inter-annual dynamics. All available Landsat data are used, and the time-series are partitioned into segments delimited by breakpoints. Segments can be associated to land use regimes, while the breakpoints then correspond to shifts in regimes. To further characterize these shifts, we classified the unlabelled breakpoints returned by the algorithm into their corresponding processes. We used a Random Forest classifier, trained from a set of visually interpreted time-series profiles to infer the processes and assign labels to the breakpoints. The whole approach was applied to quantifying the number of cultivation cycles in a swidden agriculture system in Brazil. Number and frequency of cultivation cycles is of particular ecological relevance in these systems since they largely affect the capacity of the forest to regenerate after abandonment. We applied the method to a Landsat time-series of Normalized Difference Moisture Index (NDMI) spanning the 1984-2015 period and derived from it the number of cultivation cycles during that period at the individual field scale level. Agricultural fields boundaries used to apply the method were derived using a multi-temporal segmentation. We validated the number of cultivation cycles predicted against in-situ information collected from farmers interviews, resulting in a Normalized RMSE of 0.25. Overall the method performed well, producing maps with coherent patterns. We identified various sources of error in the approach, including low data availability in the 90s and sub-object mixture of land uses. We conclude that the method holds great promise for land use history mapping in the tropics and beyond. Spatial and temporal patterns were further analysed with an ecological perspective in a follow-up study. Results show that changes in land use patterns such as land use intensification and reduced agricultural expansion reflect the socio-economic transformations that occurred in the region

Estimating water temperatures in small streams in western Oregon using neural network models

USGS Publications Warehouse

Risley, John C.; Roehl, Edwin A.; Conrads, Paul

2003-01-01

Artificial neural network models were developed to estimate water temperatures in small streams using data collected at 148 sites throughout western Oregon from June to September 1999. The sites were located on 1st-, 2nd-, or 3rd-order streams having undisturbed or minimally disturbed conditions. Data collected at each site for model development included continuous hourly water temperature and description of riparian habitat. Additional data pertaining to the landscape characteristics of the basins upstream of the sites were assembled using geographic information system (GIS) techniques. Hourly meteorological time series data collected at 25 locations within the study region also were assembled. Clustering analysis was used to partition 142 sites into 3 groups. Separate models were developed for each group. The riparian habitat, basin characteristic, and meteorological time series data were independent variables and water temperature time series were dependent variables to the models, respectively. Approximately one-third of the data vectors were used for model training, and the remaining two-thirds were used for model testing. Critical input variables included riparian shade, site elevation, and percentage of forested area of the basin. Coefficient of determination and root mean square error for the models ranged from 0.88 to 0.99 and 0.05 to 0.59 oC, respectively. The models also were tested and validated using temperature time series, habitat, and basin landscape data from 6 sites that were separate from the 142 sites that were used to develop the models. The models are capable of estimating water temperatures at locations along 1st-, 2nd-, and 3rd-order streams in western Oregon. The model user must assemble riparian habitat and basin landscape characteristics data for a site of interest. These data, in addition to meteorological data, are model inputs. Output from the models include simulated hourly water temperatures for the June to September period. Adjustments can be made to the shade input data to simulate the effects of minimum or maximum shade on water temperatures.

Interactive Learning Modules: Enabling Near Real-Time Oceanographic Data Use In Undergraduate Education

NASA Astrophysics Data System (ADS)

Kilb, D. L.; Fundis, A. T.; Risien, C. M.

2012-12-01

The focus of the Education and Public Engagement (EPE) component of the NSF's Ocean Observatories Initiative (OOI) is to provide a new layer of cyber-interactivity for undergraduate educators to bring near real-time data from the global ocean into learning environments. To accomplish this, we are designing six online services including: 1) visualization tools, 2) a lesson builder, 3) a concept map builder, 4) educational web services (middleware), 5) collaboration tools and 6) an educational resource database. Here, we report on our Fall 2012 release that includes the first four of these services: 1) Interactive visualization tools allow users to interactively select data of interest, display the data in various views (e.g., maps, time-series and scatter plots) and obtain statistical measures such as mean, standard deviation and a regression line fit to select data. Specific visualization tools include a tool to compare different months of data, a time series explorer tool to investigate the temporal evolution of select data parameters (e.g., sea water temperature or salinity), a glider profile tool that displays ocean glider tracks and associated transects, and a data comparison tool that allows users to view the data either in scatter plot view comparing one parameter with another, or in time series view. 2) Our interactive lesson builder tool allows users to develop a library of online lesson units, which are collaboratively editable and sharable and provides starter templates designed from learning theory knowledge. 3) Our interactive concept map tool allows the user to build and use concept maps, a graphical interface to map the connection between concepts and ideas. This tool also provides semantic-based recommendations, and allows for embedding of associated resources such as movies, images and blogs. 4) Education web services (middleware) will provide an educational resource database API.

XVII International Conference on Hadron Spectroscopy and Structure

NASA Astrophysics Data System (ADS)

2017-09-01

The Hadron 2017 Conference is the seventeenth of a series of biennial conferences started in 1985 at Maryland, USA. Its official name, XVII International Conference on Hadron Spectroscopy and Structure, includes for the first time the term structure to emphasize the importance that this issue has acquired in recent editions of the series. The aim of the conference is to provide an overview of the present status and progress in hadron structure and dynamics, as well as a preview of the forthcoming investigations. It will cover lectures on both experimental and theoretical aspects, including in particular the presentation of new results.

Econophysics — complex correlations and trend switchings in financial time series

NASA Astrophysics Data System (ADS)

Preis, T.

2011-03-01

This article focuses on the analysis of financial time series and their correlations. A method is used for quantifying pattern based correlations of a time series. With this methodology, evidence is found that typical behavioral patterns of financial market participants manifest over short time scales, i.e., that reactions to given price patterns are not entirely random, but that similar price patterns also cause similar reactions. Based on the investigation of the complex correlations in financial time series, the question arises, which properties change when switching from a positive trend to a negative trend. An empirical quantification by rescaling provides the result that new price extrema coincide with a significant increase in transaction volume and a significant decrease in the length of corresponding time intervals between transactions. These findings are independent of the time scale over 9 orders of magnitude, and they exhibit characteristics which one can also find in other complex systems in nature (and in physical systems in particular). These properties are independent of the markets analyzed. Trends that exist only for a few seconds show the same characteristics as trends on time scales of several months. Thus, it is possible to study financial bubbles and their collapses in more detail, because trend switching processes occur with higher frequency on small time scales. In addition, a Monte Carlo based simulation of financial markets is analyzed and extended in order to reproduce empirical features and to gain insight into their causes. These causes include both financial market microstructure and the risk aversion of market participants.

76 FR 6646 - Self-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing of Proposed Rule...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-02-07

... Series, adjusted option series and any options series until the time to expiration for such series is... time to expiration for such series is less than nine months be treated differently. Specifically, under... series until the time to expiration for such series is less than nine months. Accordingly, the...

Complexity quantification of cardiac variability time series using improved sample entropy (I-SampEn).

PubMed

Marwaha, Puneeta; Sunkaria, Ramesh Kumar

2016-09-01

The sample entropy (SampEn) has been widely used to quantify the complexity of RR-interval time series. It is a fact that higher complexity, and hence, entropy is associated with the RR-interval time series of healthy subjects. But, SampEn suffers from the disadvantage that it assigns higher entropy to the randomized surrogate time series as well as to certain pathological time series, which is a misleading observation. This wrong estimation of the complexity of a time series may be due to the fact that the existing SampEn technique updates the threshold value as a function of long-term standard deviation (SD) of a time series. However, time series of certain pathologies exhibits substantial variability in beat-to-beat fluctuations. So the SD of the first order difference (short term SD) of the time series should be considered while updating threshold value, to account for period-to-period variations inherited in a time series. In the present work, improved sample entropy (I-SampEn), a new methodology has been proposed in which threshold value is updated by considering the period-to-period variations of a time series. The I-SampEn technique results in assigning higher entropy value to age-matched healthy subjects than patients suffering atrial fibrillation (AF) and diabetes mellitus (DM). Our results are in agreement with the theory of reduction in complexity of RR-interval time series in patients suffering from chronic cardiovascular and non-cardiovascular diseases.

Studying Individual Differences in Predictability with Gamma Regression and Nonlinear Multilevel Models

ERIC Educational Resources Information Center

Culpepper, Steven Andrew

2010-01-01

Statistical prediction remains an important tool for decisions in a variety of disciplines. An equally important issue is identifying factors that contribute to more or less accurate predictions. The time series literature includes well developed methods for studying predictability and volatility over time. This article develops…

38 CFR 1.17 - Evaluation of studies relating to health effects of radiation exposure.

Code of Federal Regulations, 2012 CFR

2012-07-01

... health effects of radiation exposure. (a) From time to time, the Secretary shall publish evaluations of... paragraph a valid study is one which: (i) Has adequately described the study design and methods of data... studies affecting epidemiological assessments including case series, correlational studies and studies...

10 CFR 490.2 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-01-01

... biological materials (including neat biodiesel); three P-series fuels (specifically known as Pure Regular... Centrally Fueled means a vehicle can be refueled at least 75 percent of its time at the location that is... of the time at a location that is owned, operated, or controlled by the fleet or covered person, or...

38 CFR 1.17 - Evaluation of studies relating to health effects of radiation exposure.

Code of Federal Regulations, 2013 CFR

2013-07-01

... health effects of radiation exposure. (a) From time to time, the Secretary shall publish evaluations of... paragraph a valid study is one which: (i) Has adequately described the study design and methods of data... studies affecting epidemiological assessments including case series, correlational studies and studies...

38 CFR 1.17 - Evaluation of studies relating to health effects of radiation exposure.

Code of Federal Regulations, 2014 CFR

2014-07-01

... health effects of radiation exposure. (a) From time to time, the Secretary shall publish evaluations of... paragraph a valid study is one which: (i) Has adequately described the study design and methods of data... studies affecting epidemiological assessments including case series, correlational studies and studies...

38 CFR 1.17 - Evaluation of studies relating to health effects of radiation exposure.

Code of Federal Regulations, 2010 CFR

2010-07-01

... health effects of radiation exposure. (a) From time to time, the Secretary shall publish evaluations of... paragraph a valid study is one which: (i) Has adequately described the study design and methods of data... studies affecting epidemiological assessments including case series, correlational studies and studies...

38 CFR 1.17 - Evaluation of studies relating to health effects of radiation exposure.

Code of Federal Regulations, 2011 CFR

2011-07-01

... health effects of radiation exposure. (a) From time to time, the Secretary shall publish evaluations of... paragraph a valid study is one which: (i) Has adequately described the study design and methods of data... studies affecting epidemiological assessments including case series, correlational studies and studies...

How To Learn More in Less Time.

ERIC Educational Resources Information Center

Shaughnessy, Michael F.

Designed to help poorly prepared students perform better in college and to help prepared students perform at higher levels, this paper presents a series of specific suggestions for students regarding study activities, course choice, thinking behavior, and time allocation. The suggestions include the following: (1) eliminate diversions during study…

GEWEX-RFA Data File Format and File Naming Convention

Atmospheric Science Data Center

2016-05-20

... documentation, will be stored for each data product. Each time data is added to, removed from, or modified in the file set for a product, ... including 29 days in leap-year Februaries. Time series files containing 15-minute data should start at the top of an hour to ...

The genomic response of skeletal muscle to methylprednisolone using microarrays: tailoring data mining to the structure of the pharmacogenomic time series

PubMed Central

DuBois, Debra C; Piel, William H; Jusko, William J

2008-01-01

High-throughput data collection using gene microarrays has great potential as a method for addressing the pharmacogenomics of complex biological systems. Similarly, mechanism-based pharmacokinetic/pharmacodynamic modeling provides a tool for formulating quantitative testable hypotheses concerning the responses of complex biological systems. As the response of such systems to drugs generally entails cascades of molecular events in time, a time series design provides the best approach to capturing the full scope of drug effects. A major problem in using microarrays for high-throughput data collection is sorting through the massive amount of data in order to identify probe sets and genes of interest. Due to its inherent redundancy, a rich time series containing many time points and multiple samples per time point allows for the use of less stringent criteria of expression, expression change and data quality for initial filtering of unwanted probe sets. The remaining probe sets can then become the focus of more intense scrutiny by other methods, including temporal clustering, functional clustering and pharmacokinetic/pharmacodynamic modeling, which provide additional ways of identifying the probes and genes of pharmacological interest. PMID:15212590

Methods for Detecting Early Warnings of Critical Transitions in Time Series Illustrated Using Simulated Ecological Data

PubMed Central

Dakos, Vasilis; Carpenter, Stephen R.; Brock, William A.; Ellison, Aaron M.; Guttal, Vishwesha; Ives, Anthony R.; Kéfi, Sonia; Livina, Valerie; Seekell, David A.; van Nes, Egbert H.; Scheffer, Marten

2012-01-01

Many dynamical systems, including lakes, organisms, ocean circulation patterns, or financial markets, are now thought to have tipping points where critical transitions to a contrasting state can happen. Because critical transitions can occur unexpectedly and are difficult to manage, there is a need for methods that can be used to identify when a critical transition is approaching. Recent theory shows that we can identify the proximity of a system to a critical transition using a variety of so-called ‘early warning signals’, and successful empirical examples suggest a potential for practical applicability. However, while the range of proposed methods for predicting critical transitions is rapidly expanding, opinions on their practical use differ widely, and there is no comparative study that tests the limitations of the different methods to identify approaching critical transitions using time-series data. Here, we summarize a range of currently available early warning methods and apply them to two simulated time series that are typical of systems undergoing a critical transition. In addition to a methodological guide, our work offers a practical toolbox that may be used in a wide range of fields to help detect early warning signals of critical transitions in time series data. PMID:22815897

Model Performance Evaluation and Scenario Analysis ...

EPA Pesticide Factsheets

This tool consists of two parts: model performance evaluation and scenario analysis (MPESA). The model performance evaluation consists of two components: model performance evaluation metrics and model diagnostics. These metrics provides modelers with statistical goodness-of-fit measures that capture magnitude only, sequence only, and combined magnitude and sequence errors. The performance measures include error analysis, coefficient of determination, Nash-Sutcliffe efficiency, and a new weighted rank method. These performance metrics only provide useful information about the overall model performance. Note that MPESA is based on the separation of observed and simulated time series into magnitude and sequence components. The separation of time series into magnitude and sequence components and the reconstruction back to time series provides diagnostic insights to modelers. For example, traditional approaches lack the capability to identify if the source of uncertainty in the simulated data is due to the quality of the input data or the way the analyst adjusted the model parameters. This report presents a suite of model diagnostics that identify if mismatches between observed and simulated data result from magnitude or sequence related errors. MPESA offers graphical and statistical options that allow HSPF users to compare observed and simulated time series and identify the parameter values to adjust or the input data to modify. The scenario analysis part of the too

Monitoring cotton root rot by synthetic Sentinel-2 NDVI time series using improved spatial and temporal data fusion.

PubMed

Wu, Mingquan; Yang, Chenghai; Song, Xiaoyu; Hoffmann, Wesley Clint; Huang, Wenjiang; Niu, Zheng; Wang, Changyao; Li, Wang; Yu, Bo

2018-01-31

To better understand the progression of cotton root rot within the season, time series monitoring is required. In this study, an improved spatial and temporal data fusion approach (ISTDFA) was employed to combine 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Different Vegetation Index (NDVI) and 10-m Sentinetl-2 NDVI data to generate a synthetic Sentinel-2 NDVI time series for monitoring this disease. Then, the phenology of healthy cotton and infected cotton was modeled using a logistic model. Finally, several phenology parameters, including the onset day of greenness minimum (OGM), growing season length (GLS), onset of greenness increase (OGI), max NDVI value, and integral area of the phenology curve, were calculated. The results showed that ISTDFA could be used to combine time series MODIS and Sentinel-2 NDVI data with a correlation coefficient of 0.893. The logistic model could describe the phenology curves with R-squared values from 0.791 to 0.969. Moreover, the phenology curve of infected cotton showed a significant difference from that of healthy cotton. The max NDVI value, OGM, GSL and the integral area of the phenology curve for infected cotton were reduced by 0.045, 30 days, 22 days, and 18.54%, respectively, compared with those for healthy cotton.

Development and application of a modified dynamic time warping algorithm (DTW-S) to analyses of primate brain expression time series

PubMed Central

2011-01-01

Background Comparing biological time series data across different conditions, or different specimens, is a common but still challenging task. Algorithms aligning two time series represent a valuable tool for such comparisons. While many powerful computation tools for time series alignment have been developed, they do not provide significance estimates for time shift measurements. Results Here, we present an extended version of the original DTW algorithm that allows us to determine the significance of time shift estimates in time series alignments, the DTW-Significance (DTW-S) algorithm. The DTW-S combines important properties of the original algorithm and other published time series alignment tools: DTW-S calculates the optimal alignment for each time point of each gene, it uses interpolated time points for time shift estimation, and it does not require alignment of the time-series end points. As a new feature, we implement a simulation procedure based on parameters estimated from real time series data, on a series-by-series basis, allowing us to determine the false positive rate (FPR) and the significance of the estimated time shift values. We assess the performance of our method using simulation data and real expression time series from two published primate brain expression datasets. Our results show that this method can provide accurate and robust time shift estimates for each time point on a gene-by-gene basis. Using these estimates, we are able to uncover novel features of the biological processes underlying human brain development and maturation. Conclusions The DTW-S provides a convenient tool for calculating accurate and robust time shift estimates at each time point for each gene, based on time series data. The estimates can be used to uncover novel biological features of the system being studied. The DTW-S is freely available as an R package TimeShift at http://www.picb.ac.cn/Comparative/data.html. PMID:21851598

Development and application of a modified dynamic time warping algorithm (DTW-S) to analyses of primate brain expression time series.

PubMed

Yuan, Yuan; Chen, Yi-Ping Phoebe; Ni, Shengyu; Xu, Augix Guohua; Tang, Lin; Vingron, Martin; Somel, Mehmet; Khaitovich, Philipp

2011-08-18

Comparing biological time series data across different conditions, or different specimens, is a common but still challenging task. Algorithms aligning two time series represent a valuable tool for such comparisons. While many powerful computation tools for time series alignment have been developed, they do not provide significance estimates for time shift measurements. Here, we present an extended version of the original DTW algorithm that allows us to determine the significance of time shift estimates in time series alignments, the DTW-Significance (DTW-S) algorithm. The DTW-S combines important properties of the original algorithm and other published time series alignment tools: DTW-S calculates the optimal alignment for each time point of each gene, it uses interpolated time points for time shift estimation, and it does not require alignment of the time-series end points. As a new feature, we implement a simulation procedure based on parameters estimated from real time series data, on a series-by-series basis, allowing us to determine the false positive rate (FPR) and the significance of the estimated time shift values. We assess the performance of our method using simulation data and real expression time series from two published primate brain expression datasets. Our results show that this method can provide accurate and robust time shift estimates for each time point on a gene-by-gene basis. Using these estimates, we are able to uncover novel features of the biological processes underlying human brain development and maturation. The DTW-S provides a convenient tool for calculating accurate and robust time shift estimates at each time point for each gene, based on time series data. The estimates can be used to uncover novel biological features of the system being studied. The DTW-S is freely available as an R package TimeShift at http://www.picb.ac.cn/Comparative/data.html.

Extended causal modeling to assess Partial Directed Coherence in multiple time series with significant instantaneous interactions.

PubMed

Faes, Luca; Nollo, Giandomenico

2010-11-01

The Partial Directed Coherence (PDC) and its generalized formulation (gPDC) are popular tools for investigating, in the frequency domain, the concept of Granger causality among multivariate (MV) time series. PDC and gPDC are formalized in terms of the coefficients of an MV autoregressive (MVAR) model which describes only the lagged effects among the time series and forsakes instantaneous effects. However, instantaneous effects are known to affect linear parametric modeling, and are likely to occur in experimental time series. In this study, we investigate the impact on the assessment of frequency domain causality of excluding instantaneous effects from the model underlying PDC evaluation. Moreover, we propose the utilization of an extended MVAR model including both instantaneous and lagged effects. This model is used to assess PDC either in accordance with the definition of Granger causality when considering only lagged effects (iPDC), or with an extended form of causality, when we consider both instantaneous and lagged effects (ePDC). The approach is first evaluated on three theoretical examples of MVAR processes, which show that the presence of instantaneous correlations may produce misleading profiles of PDC and gPDC, while ePDC and iPDC derived from the extended model provide here a correct interpretation of extended and lagged causality. It is then applied to representative examples of cardiorespiratory and EEG MV time series. They suggest that ePDC and iPDC are better interpretable than PDC and gPDC in terms of the known cardiovascular and neural physiologies.

Predictability of monthly temperature and precipitation using automatic time series forecasting methods

NASA Astrophysics Data System (ADS)

Papacharalampous, Georgia; Tyralis, Hristos; Koutsoyiannis, Demetris

2018-02-01

We investigate the predictability of monthly temperature and precipitation by applying automatic univariate time series forecasting methods to a sample of 985 40-year-long monthly temperature and 1552 40-year-long monthly precipitation time series. The methods include a naïve one based on the monthly values of the last year, as well as the random walk (with drift), AutoRegressive Fractionally Integrated Moving Average (ARFIMA), exponential smoothing state-space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components (BATS), simple exponential smoothing, Theta and Prophet methods. Prophet is a recently introduced model inspired by the nature of time series forecasted at Facebook and has not been applied to hydrometeorological time series before, while the use of random walk, BATS, simple exponential smoothing and Theta is rare in hydrology. The methods are tested in performing multi-step ahead forecasts for the last 48 months of the data. We further investigate how different choices of handling the seasonality and non-normality affect the performance of the models. The results indicate that: (a) all the examined methods apart from the naïve and random walk ones are accurate enough to be used in long-term applications; (b) monthly temperature and precipitation can be forecasted to a level of accuracy which can barely be improved using other methods; (c) the externally applied classical seasonal decomposition results mostly in better forecasts compared to the automatic seasonal decomposition used by the BATS and Prophet methods; and (d) Prophet is competitive, especially when it is combined with externally applied classical seasonal decomposition.

A Fresh Look at Spatio-Temporal Remote Sensing Data: Data Formats, Processing Flow, and Visualization

NASA Astrophysics Data System (ADS)

Gens, R.

2017-12-01

With increasing number of experimental and operational satellites in orbit, remote sensing based mapping and monitoring of the dynamic Earth has entered into the realm of `big data'. Just the Landsat series of satellites provide a near continuous archive of 45 years of data. The availability of such spatio-temporal datasets has created opportunities for long-term monitoring diverse features and processes operating on the Earth's terrestrial and aquatic systems. Processes such as erosion, deposition, subsidence, uplift, evapotranspiration, urbanization, land-cover regime shifts can not only be monitored and change can be quantified using time-series data analysis. This unique opportunity comes with new challenges in management, analysis, and visualization of spatio-temporal datasets. Data need to be stored in a user-friendly format, and relevant metadata needs to be recorded, to allow maximum flexibility for data exchange and use. Specific data processing workflows need to be defined to support time-series analysis for specific applications. Value-added data products need to be generated keeping in mind the needs of the end-users, and using best practices in complex data visualization. This presentation systematically highlights the various steps for preparing spatio-temporal remote sensing data for time series analysis. It showcases a prototype workflow for remote sensing based change detection that can be generically applied while preserving the application-specific fidelity of the datasets. The prototype includes strategies for visualizing change over time. This has been exemplified using a time-series of optical and SAR images for visualizing the changing glacial, coastal, and wetland landscapes in parts of Alaska.

Advanced interferometric synthetic aperture radar (InSAR) time series analysis using interferograms of multiple-orbit tracks: A case study on Miyake-jima

NASA Astrophysics Data System (ADS)

Ozawa, Taku; Ueda, Hideki

2011-12-01

InSAR time series analysis is an effective tool for detecting spatially and temporally complicated volcanic deformation. To obtain details of such deformation, we developed an advanced InSAR time series analysis using interferograms of multiple-orbit tracks. Considering only right- (or only left-) looking SAR observations, incidence directions for different orbit tracks are mostly included in a common plane. Therefore, slant-range changes in their interferograms can be expressed by two components in the plane. This approach estimates the time series of their components from interferograms of multiple-orbit tracks by the least squares analysis, and higher accuracy is obtained if many interferograms of different orbit tracks are available. Additionally, this analysis can combine interferograms for different incidence angles. In a case study on Miyake-jima, we obtained a deformation time series corresponding to GPS observations from PALSAR interferograms of six orbit tracks. The obtained accuracy was better than that with the SBAS approach, demonstrating its effectiveness. Furthermore, it is expected that higher accuracy would be obtained if SAR observations were carried out more frequently in all orbit tracks. The deformation obtained in the case study indicates uplift along the west coast and subsidence with contraction around the caldera. The speed of the uplift was almost constant, but the subsidence around the caldera decelerated from 2009. A flat deformation source was estimated near sea level under the caldera, implying that deceleration of subsidence was related to interaction between volcanic thermal activity and the aquifer.

Present-day deformation in Europe, as seen by the EPOS-GNSS prototype solution in double difference, and first co- and post-seismic displacements associated with 2016 Amatrice and Norcia earthquakes (Italy)

NASA Astrophysics Data System (ADS)

Socquet, Anne; Déprez, Aline; Cotte, Nathalie; Maubant, Louise; Walpersdorf, Andrea; Bato, Mary Grace

2017-04-01

We present here a new pan-European velocity field, obtained by processing 500+ cGPS stations in double difference, in the framework of the implementation phase of the European Plate Observing System (EPOS) project. This prototype solution spans the 2000-2016 period, and includes data from RING, NOA, RENAG and European Permanent Network (EPN) cGPS netwprks. The data set is first split into daily sub-networks (between 8 and 14 sub-networks). The sub-networks consist in about 40 stations, with 2 overlapping stations. For each day and for each sub-network, the GAMIT processing is conducted independently. Once each sub-network achieves satisfactory results, a daily combination is performed in order to produce SINEX files. The Chi square value associated with the combination allows us to evaluate its quality. Eventually, a multi year combination generates position time series for each station. Each time series is visualized and the jumps associated with material change (antenna or receiver) are estimated and corrected. This procedure allows us to generate daily solutions and position time series for all stations. The associated "interseismic" velocity field has then been estimated using a times series analysis using MIDAS software, and compared to another independent estimate obtained by Kalman filtering with globk software. In addition to this velocity field we made a specific zoom on Italy and present a strain rate map as well as time series showing co- and post- seismic movements associated with the 2016 Amatrice and Norcia earthquakes.

Interannual Variability of OLR as Observed by AIRS and CERES

NASA Technical Reports Server (NTRS)

Susskind, Joel; Molnar, Gyula; Iredell, Lena; Loeb, Norman G.

2012-01-01

This paper compares spatial anomaly time series of OLR (Outgoing Longwave Radiation) and OLR(sub CLR) (Clear Sky OLR) as determined using observations from CERES Terra and AIRS over the time period September 2002 through June 2011. Both AIRS and CERES show a significant decrease in global mean and tropical mean OLR over this time period. We find excellent agreement of the anomaly time-series of the two OLR data sets in almost every detail, down to 1 deg X 1 deg spatial grid point level. The extremely close agreement of OLR anomaly time series derived from observations by two different instruments implies that both sets of results must be highly stable. This agreement also validates to some extent the anomaly time series of the AIRS derived products used in the computation of the AIRS OLR product. The paper also examines the correlations of anomaly time series of AIRS and CERES OLR, on different spatial scales, as well as those of other AIRS derived products, with that of the NOAA Sea Surface Temperature (SST) product averaged over the NOAA Nino-4 spatial region. We refer to these SST anomalies as the El Nino Index. Large spatially coherent positive and negative correlations of OLR anomaly time series with that of the El Nino Index are found in different spatial regions. Anomalies of global mean, and especially tropical mean, OLR are highly positively correlated with the El Nino Index. These correlations explain that the recent global and tropical mean decreases in OLR over the period September 2002 through June 2011, as observed by both AIRS and CERES, are primarily the result of a transition from an El Nino condition at the beginning of the data record to La Nina conditions toward the end of the data period. We show that the close correlation of global mean, and especially tropical mean, OLR anomalies with the El Nino Index can be well accounted for by temporal changes of OLR within two spatial regions which lie outside the NOAA Nino-4 region, in which anomalies of cloud cover and mid-tropospheric water vapor are both highly negatively correlated with the El Nino Index. Agreement of the AIRS and CERES OLR(sub CLR) anomaly time series is less good, which may be a result of the large sampling differences in the ensemble of cases included in each OLR(sub CLR) data set.

Multivariate time series modeling of short-term system scale irrigation demand

NASA Astrophysics Data System (ADS)

Perera, Kushan C.; Western, Andrew W.; George, Biju; Nawarathna, Bandara

2015-12-01

Travel time limits the ability of irrigation system operators to react to short-term irrigation demand fluctuations that result from variations in weather, including very hot periods and rainfall events, as well as the various other pressures and opportunities that farmers face. Short-term system-wide irrigation demand forecasts can assist in system operation. Here we developed a multivariate time series (ARMAX) model to forecast irrigation demands with respect to aggregated service points flows (IDCGi, ASP) and off take regulator flows (IDCGi, OTR) based across 5 command areas, which included area covered under four irrigation channels and the study area. These command area specific ARMAX models forecast 1-5 days ahead daily IDCGi, ASP and IDCGi, OTR using the real time flow data recorded at the service points and the uppermost regulators and observed meteorological data collected from automatic weather stations. The model efficiency and the predictive performance were quantified using the root mean squared error (RMSE), Nash-Sutcliffe model efficiency coefficient (NSE), anomaly correlation coefficient (ACC) and mean square skill score (MSSS). During the evaluation period, NSE for IDCGi, ASP and IDCGi, OTR across 5 command areas were ranged 0.98-0.78. These models were capable of generating skillful forecasts (MSSS ⩾ 0.5 and ACC ⩾ 0.6) of IDCGi, ASP and IDCGi, OTR for all 5 lead days and IDCGi, ASP and IDCGi, OTR forecasts were better than using the long term monthly mean irrigation demand. Overall these predictive performance from the ARMAX time series models were higher than almost all the previous studies we are aware. Further, IDCGi, ASP and IDCGi, OTR forecasts have improved the operators' ability to react for near future irrigation demand fluctuations as the developed ARMAX time series models were self-adaptive to reflect the short-term changes in the irrigation demand with respect to various pressures and opportunities that farmers' face, such as changing water policy, continued development of water markets, drought and changing technology.

The ANTARES observation network

NASA Astrophysics Data System (ADS)

Dogliotti, Ana I.; Ulloa, Osvaldo; Muller-Karger, Frank; Hu, Chuanmin; Murch, Brock; Taylor, Charles; Yuras, Gabriel; Kampel, Milton; Lutz, Vivian; Gaeta, Salvador; Gagliardini, Domingo A.; Garcia, Carlos A. E.; Klein, Eduardo; Helbling, Walter; Varela, Ramon; Barbieri, Elena; Negri, Ruben; Frouin, Robert; Sathyendranath, Shubha; Platt, Trevor

2005-08-01

The ANTARES network seeks to understand the variability of the coastal environment on a continental scale and the local, regional, and global factors and processes that effect this change. The focus are coastal zones of South America and the Caribbean Sea. The initial approach includes developing time series of in situ and satellite-based environmental observations in coastal and oceanic regions. The network is constituted by experts that seek to exchange ideas, develop an infrastructure for mutual logistical and knowledge support, and link in situ time series of observations located around the Americas with real-time and historical satellite-derived time series of relevant products. A major objective is to generate information that will be distributed publicly and openly in the service of coastal ocean research, resource management, science-based policy making and education in the Americas. As a first stage, the network has linked oceanographic time series located in Argentina, Brazil, Chile and Venezuela. The group has also developed an online tool to examine satellite data collected with sensors such as NASA's MODIS. Specifically, continental-scale high-resolution (1 km) maps of chlorophyll and of sea surface temperature are generated and served daily over the web according to specifications of users within the ANTARES network. Other satellite-derived variables will be added as support for the network is solidified. ANTARES serves data and offers simple analysis tools that anyone can use with the ultimate goal of improving coastal assessments, management and policies.

[Epidemiologic surveillance of contact allergens. The "monitoring series" of IVDK (Information Network ofDermatologic Clinics for Detection and Scientific Evaluation of Contact Allergy].

PubMed

Aberer, W; Komericki, P; Uter, W; Hausen, B M; Lessmann, H; Kränke, B; Geier, J; Schnuch, A

2003-08-01

The selection of the most important contact allergens is subject to a continuous change. Several factors may influence the sensitization rates and thus the decision, which substances to include in the standard series of the most frequent allergens. The Information Network of Departments of Dermatology adds substances of interest for a certain time period to the standard series in order to evaluate parameters such as sensitization rate, grade of reaction, and clinical relevance of positive reactions. In 6 testing periods starting in 1996, 13 test substances were evaluated. Due to the results, propolis, compositae mix, and bufexamac were included in the standard series in 1999, while lyral was added in 2002. Sorbitansesquioleat, dispers blue mix, and iodopropynyl butylcarbamate are under further discussion. Substances such as glutaraldehyde and p-aminoazobenzole should be tested in certain risk groups only, whereas the steroids budesonide and tixocortol should be tested when clinically suspected.

Time series geophysical monitoring of permanganate injections and in situ chemical oxidation of PCE, OU1 area, Savage Superfund Site, Milford, NH, USA

NASA Astrophysics Data System (ADS)

Harte, Philip T.; Smith, Thor E.; Williams, John H.; Degnan, James R.

2012-05-01

In situ chemical oxidation (ISCO) treatment with sodium permanganate, an electrically conductive oxidant, provides a strong electrical signal for tracking of injectate transport using time series geophysical surveys including direct current (DC) resistivity and electromagnetic (EM) methods. Effective remediation is dependent upon placing the oxidant in close contact with the contaminated aquifer. Therefore, monitoring tools that provide enhanced tracking capability of the injectate offer considerable benefit to guide subsequent ISCO injections. Time-series geophysical surveys were performed at a superfund site in New Hampshire, USA over a one-year period to identify temporal changes in the bulk electrical conductivity of a tetrachloroethylene (PCE; also called tetrachloroethene) contaminated, glacially deposited aquifer due to the injection of sodium permanganate. The ISCO treatment involved a series of pulse injections of sodium permanganate from multiple injection wells within a contained area of the aquifer. After the initial injection, the permanganate was allowed to disperse under ambient groundwater velocities. Time series geophysical surveys identified the downward sinking and pooling of the sodium permanganate atop of the underlying till or bedrock surface caused by density-driven flow, and the limited horizontal spread of the sodium permanganate in the shallow parts of the aquifer during this injection period. When coupled with conventional monitoring, the surveys allowed for an assessment of ISCO treatment effectiveness in targeting the PCE plume and helped target areas for subsequent treatment.

Time series geophysical monitoring of permanganate injections and in situ chemical oxidation of PCE, OU1 area, Savage Superfund Site, Milford, NH, USA

USGS Publications Warehouse

Harte, Philip T.; Smith, Thor E.; Williams, John H.; Degnan, James R.

2012-01-01

In situ chemical oxidation (ISCO) treatment with sodium permanganate, an electrically conductive oxidant, provides a strong electrical signal for tracking of injectate transport using time series geophysical surveys including direct current (DC) resistivity and electromagnetic (EM) methods. Effective remediation is dependent upon placing the oxidant in close contact with the contaminated aquifer. Therefore, monitoring tools that provide enhanced tracking capability of the injectate offer considerable benefit to guide subsequent ISCO injections. Time-series geophysical surveys were performed at a superfund site in New Hampshire, USA over a one-year period to identify temporal changes in the bulk electrical conductivity of a tetrachloroethylene (PCE; also called tetrachloroethene) contaminated, glacially deposited aquifer due to the injection of sodium permanganate. The ISCO treatment involved a series of pulse injections of sodium permanganate from multiple injection wells within a contained area of the aquifer. After the initial injection, the permanganate was allowed to disperse under ambient groundwater velocities. Time series geophysical surveys identified the downward sinking and pooling of the sodium permanganate atop of the underlying till or bedrock surface caused by density-driven flow, and the limited horizontal spread of the sodium permanganate in the shallow parts of the aquifer during this injection period. When coupled with conventional monitoring, the surveys allowed for an assessment of ISCO treatment effectiveness in targeting the PCE plume and helped target areas for subsequent treatment.

Time series geophysical monitoring of permanganate injections and in situ chemical oxidation of PCE, OU1 area, Savage Superfund Site, Milford, NH, USA.

PubMed

Harte, Philip T; Smith, Thor E; Williams, John H; Degnan, James R

2012-05-01

In situ chemical oxidation (ISCO) treatment with sodium permanganate, an electrically conductive oxidant, provides a strong electrical signal for tracking of injectate transport using time series geophysical surveys including direct current (DC) resistivity and electromagnetic (EM) methods. Effective remediation is dependent upon placing the oxidant in close contact with the contaminated aquifer. Therefore, monitoring tools that provide enhanced tracking capability of the injectate offer considerable benefit to guide subsequent ISCO injections. Time-series geophysical surveys were performed at a superfund site in New Hampshire, USA over a one-year period to identify temporal changes in the bulk electrical conductivity of a tetrachloroethylene (PCE; also called tetrachloroethene) contaminated, glacially deposited aquifer due to the injection of sodium permanganate. The ISCO treatment involved a series of pulse injections of sodium permanganate from multiple injection wells within a contained area of the aquifer. After the initial injection, the permanganate was allowed to disperse under ambient groundwater velocities. Time series geophysical surveys identified the downward sinking and pooling of the sodium permanganate atop of the underlying till or bedrock surface caused by density-driven flow, and the limited horizontal spread of the sodium permanganate in the shallow parts of the aquifer during this injection period. When coupled with conventional monitoring, the surveys allowed for an assessment of ISCO treatment effectiveness in targeting the PCE plume and helped target areas for subsequent treatment. Published by Elsevier B.V.

EO-1 Hyperion Reflectance Time Series at Calibration and Validation Sites: Stability and Sensitivity to Seasonal Dynamics

NASA Technical Reports Server (NTRS)

Campbell, Petya K. Entcheva; Middleton, Elizabeth M.; Thome, Kurt J.; Kokaly, Raymond F.; Huemmrich, Karl Fred; Lagomasino, David; Novick, Kimberly A.; Brunsell, Nathaniel A.

2013-01-01

This study evaluated Earth Observing 1 (EO-1) Hyperion reflectance time series at established calibration sites to assess the instrument stability and suitability for monitoring vegetation functional parameters. Our analysis using three pseudo-invariant calibration sites in North America indicated that the reflectance time series are devoid of apparent spectral trends and their stability consistently is within 2.5-5 percent throughout most of the spectral range spanning the 12-plus year data record. Using three vegetated sites instrumented with eddy covariance towers, the Hyperion reflectance time series were evaluated for their ability to determine important variables of ecosystem function. A number of narrowband and derivative vegetation indices (VI) closely described the seasonal profiles in vegetation function and ecosystem carbon exchange (e.g., net and gross ecosystem productivity) in three very different ecosystems, including a hardwood forest and tallgrass prairie in North America, and a Miombo woodland in Africa. Our results demonstrate the potential for scaling the carbon flux tower measurements to local and regional landscape levels. The VIs with stronger relationships to the CO2 parameters were derived using continuous reflectance spectra and included wavelengths associated with chlorophyll content and/or chlorophyll fluorescence. Since these indices cannot be calculated from broadband multispectral instrument data, the opportunity to exploit these spectrometer-based VIs in the future will depend on the launch of satellites such as EnMAP and HyspIRI. This study highlights the practical utility of space-borne spectrometers for characterization of the spectral stability and uniformity of the calibration sites in support of sensor cross-comparisons, and demonstrates the potential of narrowband VIs to track and spatially extend ecosystem functional status as well as carbon processes measured at flux towers.

Updating temperature and salinity mean values and trends in the Western Mediterranean: The RADMED project

NASA Astrophysics Data System (ADS)

Vargas-Yáñez, M.; García-Martínez, M. C.; Moya, F.; Balbín, R.; López-Jurado, J. L.; Serra, M.; Zunino, P.; Pascual, J.; Salat, J.

2017-09-01

The RADMED project is devoted to the implementation and maintenance of a multidisciplinary monitoring system around the Spanish Mediterranean waters. This observing system is based on periodic multidisciplinary cruises covering the coastal waters, continental shelf and slope waters and some deep stations (>2000 m) from the Westernmost Alboran Sea to Barcelona in the Catalan Sea, including the Balearic Islands. This project was launched in 2007 unifying and extending some previous monitoring projects which had a more reduced geographical coverage. Some of the time series currently available extend from 1992, while the more recent ones were initiated in 2007. The present work updates the available time series up to 2015 (included) and shows the capability of these time series for two main purposes: the calculation of mean values for the properties of main water masses around the Spanish Mediterranean, and the study of the interannual and decadal variability of such properties. The data set provided by the RADMED project has been merged with historical data from the MEDAR/MEDATLAS data base for the calculation of temperature and salinity trends from 1900 to 2015. The analysis of these time series shows that the intermediate and deep layers of the Western Mediterranean have increased their temperature and salinity with an acceleration of the warming and salting trends from 1943. Trends for the heat absorbed by the water column for the 1943-2015 period, range between 0.2 and 0.6 W/m2 depending on the used methodology. The temperature and salinity trends for the same period and for the intermediate layer are 0.002 °C/yr and 0.001 yr-1 respectively. Deep layers warmed and increased their salinity at a rate of 0.004 °C/yr and 0.001 yr-1.

EO-1 Hyperion reflectance time series at calibration and validation sites: stability and sensitivity to seasonal dynamics

USGS Publications Warehouse

Campbell, P.K.E.; Middleton, E.M.; Thome, K.J.; Kokaly, Raymond F.; Huemmrich, K.F.; Novick, K.A.; Brunsell, N.A.

2013-01-01

This study evaluated Earth Observing 1 (EO-1) Hyperion reflectance time series at established calibration sites to assess the instrument stability and suitability for monitoring vegetation functional parameters. Our analysis using three pseudo-invariant calibration sites in North America indicated that the reflectance time series are devoid of apparent spectral trends and their stability consistently is within 2.5-5 percent throughout most of the spectral range spanning the 12+ year data record. Using three vegetated sites instrumented with eddy covariance towers, the Hyperion reflectance time series were evaluated for their ability to determine important variables of ecosystem function. A number of narrowband and derivative vegetation indices (VI) closely described the seasonal profiles in vegetation function and ecosystem carbon exchange (e.g., net and gross ecosystem productivity) in three very different ecosystems, including a hardwood forest and tallgrass prairie in North America, and a Miombo woodland in Africa. Our results demonstrate the potential for scaling the carbon flux tower measurements to local and regional landscape levels. The VIs with stronger relationships to the CO2 parameters were derived using continuous reflectance spectra and included wavelengths associated with chlorophyll content and/or chlorophyll fluorescence. Since these indices cannot be calculated from broadband multispectral instrument data, the opportunity to exploit these spectrometer-based VIs in the future will depend on the launch of satellites such as EnMAP and HyspIRI. This study highlights the practical utility of space-borne spectrometers for characterization of the spectral stability and uniformity of the calibration sites in support of sensor cross-comparisons, and demonstrates the potential of narrowband VIs to track and spatially extend ecosystem functional status as well as carbon processes measured at flux towers.

ReTrOS: a MATLAB toolbox for reconstructing transcriptional activity from gene and protein expression data.

PubMed

Minas, Giorgos; Momiji, Hiroshi; Jenkins, Dafyd J; Costa, Maria J; Rand, David A; Finkenstädt, Bärbel

2017-06-26

Given the development of high-throughput experimental techniques, an increasing number of whole genome transcription profiling time series data sets, with good temporal resolution, are becoming available to researchers. The ReTrOS toolbox (Reconstructing Transcription Open Software) provides MATLAB-based implementations of two related methods, namely ReTrOS-Smooth and ReTrOS-Switch, for reconstructing the temporal transcriptional activity profile of a gene from given mRNA expression time series or protein reporter time series. The methods are based on fitting a differential equation model incorporating the processes of transcription, translation and degradation. The toolbox provides a framework for model fitting along with statistical analyses of the model with a graphical interface and model visualisation. We highlight several applications of the toolbox, including the reconstruction of the temporal cascade of transcriptional activity inferred from mRNA expression data and protein reporter data in the core circadian clock in Arabidopsis thaliana, and how such reconstructed transcription profiles can be used to study the effects of different cell lines and conditions. The ReTrOS toolbox allows users to analyse gene and/or protein expression time series where, with appropriate formulation of prior information about a minimum of kinetic parameters, in particular rates of degradation, users are able to infer timings of changes in transcriptional activity. Data from any organism and obtained from a range of technologies can be used as input due to the flexible and generic nature of the model and implementation. The output from this software provides a useful analysis of time series data and can be incorporated into further modelling approaches or in hypothesis generation.

The matrix exponential in transient structural analysis

NASA Technical Reports Server (NTRS)

Minnetyan, Levon

1987-01-01

The primary usefulness of the presented theory is in the ability to represent the effects of high frequency linear response with accuracy, without requiring very small time steps in the analysis of dynamic response. The matrix exponential contains a series approximation to the dynamic model. However, unlike the usual analysis procedure which truncates the high frequency response, the approximation in the exponential matrix solution is in the time domain. By truncating the series solution to the matrix exponential short, the solution is made inaccurate after a certain time. Yet, up to that time the solution is extremely accurate, including all high frequency effects. By taking finite time increments, the exponential matrix solution can compute the response very accurately. Use of the exponential matrix in structural dynamics is demonstrated by simulating the free vibration response of multi degree of freedom models of cantilever beams.

Time Series Expression Analyses Using RNA-seq: A Statistical Approach

PubMed Central

Oh, Sunghee; Song, Seongho; Grabowski, Gregory; Zhao, Hongyu; Noonan, James P.

2013-01-01

RNA-seq is becoming the de facto standard approach for transcriptome analysis with ever-reducing cost. It has considerable advantages over conventional technologies (microarrays) because it allows for direct identification and quantification of transcripts. Many time series RNA-seq datasets have been collected to study the dynamic regulations of transcripts. However, statistically rigorous and computationally efficient methods are needed to explore the time-dependent changes of gene expression in biological systems. These methods should explicitly account for the dependencies of expression patterns across time points. Here, we discuss several methods that can be applied to model timecourse RNA-seq data, including statistical evolutionary trajectory index (SETI), autoregressive time-lagged regression (AR(1)), and hidden Markov model (HMM) approaches. We use three real datasets and simulation studies to demonstrate the utility of these dynamic methods in temporal analysis. PMID:23586021

Time series expression analyses using RNA-seq: a statistical approach.

PubMed

Oh, Sunghee; Song, Seongho; Grabowski, Gregory; Zhao, Hongyu; Noonan, James P

2013-01-01

RNA-seq is becoming the de facto standard approach for transcriptome analysis with ever-reducing cost. It has considerable advantages over conventional technologies (microarrays) because it allows for direct identification and quantification of transcripts. Many time series RNA-seq datasets have been collected to study the dynamic regulations of transcripts. However, statistically rigorous and computationally efficient methods are needed to explore the time-dependent changes of gene expression in biological systems. These methods should explicitly account for the dependencies of expression patterns across time points. Here, we discuss several methods that can be applied to model timecourse RNA-seq data, including statistical evolutionary trajectory index (SETI), autoregressive time-lagged regression (AR(1)), and hidden Markov model (HMM) approaches. We use three real datasets and simulation studies to demonstrate the utility of these dynamic methods in temporal analysis.

Spectral-decomposition techniques for the identification of periodic and anomalous phenomena in radon time-series.

NASA Astrophysics Data System (ADS)

Crockett, R. G. M.; Perrier, F.; Richon, P.

2009-04-01

Building on independent investigations by research groups at both IPGP, France, and the University of Northampton, UK, hourly-sampled radon time-series of durations exceeding one year have been investigated for periodic and anomalous phenomena using a variety of established and novel techniques. These time-series have been recorded in locations having no routine human behaviour and thus are effectively free of significant anthropogenic influences. With regard to periodic components, the long durations of these time-series allow, in principle, very high frequency resolutions for established spectral-measurement techniques such as Fourier and maximum-entropy. However, as has been widely observed, the stochastic nature of radon emissions from rocks and soils, coupled with sensitivity to a wide variety influences such as temperature, wind-speed and soil moisture-content has made interpretation of the results obtained by such techniques very difficult, with uncertain results, in many cases. We here report developments in the investigation of radon-time series for periodic and anomalous phenomena using spectral-decomposition techniques. These techniques, in variously separating ‘high', ‘middle' and ‘low' frequency components, effectively ‘de-noise' the data by allowing components of interest to be isolated from others which (might) serve to obscure weaker information-containing components. Once isolated, these components can be investigated using a variety of techniques. Whilst this is very much work in early stages of development, spectral decomposition methods have been used successfully to indicate the presence of diurnal and sub-diurnal cycles in radon concentration which we provisionally attribute to tidal influences. Also, these methods have been used to enhance the identification of short-duration anomalies, attributable to a variety of causes including, for example, earthquakes and rapid large-magnitude changes in weather conditions. Keywords: radon; earthquakes; tidal-influences; anomalies; time series; spectral-decomposition.

Review of current GPS methodologies for producing accurate time series and their error sources

NASA Astrophysics Data System (ADS)

He, Xiaoxing; Montillet, Jean-Philippe; Fernandes, Rui; Bos, Machiel; Yu, Kegen; Hua, Xianghong; Jiang, Weiping

2017-05-01

The Global Positioning System (GPS) is an important tool to observe and model geodynamic processes such as plate tectonics and post-glacial rebound. In the last three decades, GPS has seen tremendous advances in the precision of the measurements, which allow researchers to study geophysical signals through a careful analysis of daily time series of GPS receiver coordinates. However, the GPS observations contain errors and the time series can be described as the sum of a real signal and noise. The signal itself can again be divided into station displacements due to geophysical causes and to disturbing factors. Examples of the latter are errors in the realization and stability of the reference frame and corrections due to ionospheric and tropospheric delays and GPS satellite orbit errors. There is an increasing demand on detecting millimeter to sub-millimeter level ground displacement signals in order to further understand regional scale geodetic phenomena hence requiring further improvements in the sensitivity of the GPS solutions. This paper provides a review spanning over 25 years of advances in processing strategies, error mitigation methods and noise modeling for the processing and analysis of GPS daily position time series. The processing of the observations is described step-by-step and mainly with three different strategies in order to explain the weaknesses and strengths of the existing methodologies. In particular, we focus on the choice of the stochastic model in the GPS time series, which directly affects the estimation of the functional model including, for example, tectonic rates, seasonal signals and co-seismic offsets. Moreover, the geodetic community continues to develop computational methods to fully automatize all phases from analysis of GPS time series. This idea is greatly motivated by the large number of GPS receivers installed around the world for diverse applications ranging from surveying small deformations of civil engineering structures (e.g., subsidence of the highway bridge) to the detection of particular geophysical signals.

Empirical method to measure stochasticity and multifractality in nonlinear time series

NASA Astrophysics Data System (ADS)

Lin, Chih-Hao; Chang, Chia-Seng; Li, Sai-Ping

2013-12-01

An empirical algorithm is used here to study the stochastic and multifractal nature of nonlinear time series. A parameter can be defined to quantitatively measure the deviation of the time series from a Wiener process so that the stochasticity of different time series can be compared. The local volatility of the time series under study can be constructed using this algorithm, and the multifractal structure of the time series can be analyzed by using this local volatility. As an example, we employ this method to analyze financial time series from different stock markets. The result shows that while developed markets evolve very much like an Ito process, the emergent markets are far from efficient. Differences about the multifractal structures and leverage effects between developed and emergent markets are discussed. The algorithm used here can be applied in a similar fashion to study time series of other complex systems.

Data mining on long-term barometric data within the ARISE2 project

NASA Astrophysics Data System (ADS)

Hupe, Patrick; Ceranna, Lars; Pilger, Christoph

2016-04-01

The Comprehensive nuclear-Test-Ban Treaty (CTBT) led to the implementation of an international infrasound array network. The International Monitoring System (IMS) network includes 48 certified stations, each providing data for up to 15 years. As part of work package 3 of the ARISE2 project (Atmospheric dynamics Research InfraStructure in Europe, phase 2) the data sets will be statistically evaluated with regard on atmospheric dynamics. The current study focusses on fluctuations of absolute air pressure. Time series have been analysed for 17 monitoring stations which are located all over the world between Greenland and Antarctica along the latitudes to represent different climate zones and characteristic atmospheric conditions. Hence this enables quantitative comparisons between those regions. Analyses are shown including wavelet power spectra, multi-annual time series of average variances with regard to long-wave scales, and spectral densities to derive characteristics and special events. Evaluations reveal periodicities in average variances on 2 to 20 day scale with a maximum in the winter months and a minimum in summer of the respective hemisphere. This basically applies to time series of IMS stations beyond the tropics where the dominance of cyclones and anticyclones changes with seasons. Furthermore, spectral density analyses illustrate striking signals for several dynamic activities within one day, e.g., the semidiurnal tide.

Connectionist Architectures for Time Series Prediction of Dynamical Systems

NASA Astrophysics Data System (ADS)

Weigend, Andreas Sebastian

We investigate the effectiveness of connectionist networks for predicting the future continuation of temporal sequences. The problem of overfitting, particularly serious for short records of noisy data, is addressed by the method of weight-elimination: a term penalizing network complexity is added to the usual cost function in back-propagation. We describe the dynamics of the procedure and clarify the meaning of the parameters involved. From a Bayesian perspective, the complexity term can be usefully interpreted as an assumption about prior distribution of the weights. We analyze three time series. On the benchmark sunspot series, the networks outperform traditional statistical approaches. We show that the network performance does not deteriorate when there are more input units than needed. In the second example, the notoriously noisy foreign exchange rates series, we pick one weekday and one currency (DM vs. US). Given exchange rate information up to and including a Monday, the task is to predict the rate for the following Tuesday. Weight-elimination manages to extract a significant part of the dynamics and makes the solution interpretable. In the third example, the networks predict the resource utilization of a chaotic computational ecosystem for hundreds of steps forward in time.

Time-series animation techniques for visualizing urban growth

USGS Publications Warehouse

Acevedo, W.; Masuoka, P.

1997-01-01

Time-series animation is a visually intuitive way to display urban growth. Animations of landuse change for the Baltimore-Washington region were generated by showing a series of images one after the other in sequential order. Before creating an animation, various issues which will affect the appearance of the animation should be considered, including the number of original data frames to use, the optimal animation display speed, the number of intermediate frames to create between the known frames, and the output media on which the animations will be displayed. To create new frames between the known years of data, the change in each theme (i.e. urban development, water bodies, transportation routes) must be characterized and an algorithm developed to create the in-between frames. Example time-series animations were created using a temporal GIS database of the Baltimore-Washington area. Creating the animations involved generating raster images of the urban development, water bodies, and principal transportation routes; overlaying the raster images on a background image; and importing the frames to a movie file. Three-dimensional perspective animations were created by draping each image over digital elevation data prior to importing the frames to a movie file. ?? 1997 Elsevier Science Ltd.

Reliability prediction of ontology-based service compositions using Petri net and time series models.

PubMed

Li, Jia; Xia, Yunni; Luo, Xin

2014-01-01

OWL-S, one of the most important Semantic Web service ontologies proposed to date, provides a core ontological framework and guidelines for describing the properties and capabilities of their web services in an unambiguous, computer interpretable form. Predicting the reliability of composite service processes specified in OWL-S allows service users to decide whether the process meets the quantitative quality requirement. In this study, we consider the runtime quality of services to be fluctuating and introduce a dynamic framework to predict the runtime reliability of services specified in OWL-S, employing the Non-Markovian stochastic Petri net (NMSPN) and the time series model. The framework includes the following steps: obtaining the historical response times series of individual service components; fitting these series with a autoregressive-moving-average-model (ARMA for short) and predicting the future firing rates of service components; mapping the OWL-S process into a NMSPN model; employing the predicted firing rates as the model input of NMSPN and calculating the normal completion probability as the reliability estimate. In the case study, a comparison between the static model and our approach based on experimental data is presented and it is shown that our approach achieves higher prediction accuracy.

Understanding the Role of Deterrence in Counterterrorism Security

DTIC Science & Technology

2009-11-01

30, No. 5, pp. 429–443. Enders, W., Sandler, T. (1993). “The Effectiveness of Anti-Terrorism Policies: Vector Autoregression Intervention Analysis ...occasional paper series . RAND occasional papers may include an informed perspective on a timely policy issue, a discussion of new research...United States safe? Are better means available for evaluating what may work or not and why? This series is designed to focus on a small set of

Use of interrupted time series analysis in evaluating health care quality improvements.

PubMed

Penfold, Robert B; Zhang, Fang

2013-01-01

Interrupted time series (ITS) analysis is arguably the strongest quasi-experimental research design. ITS is particularly useful when a randomized trial is infeasible or unethical. The approach usually involves constructing a time series of population-level rates for a particular quality improvement focus (eg, rates of attention-deficit/hyperactivity disorder [ADHD] medication initiation) and testing statistically for a change in the outcome rate in the time periods before and time periods after implementation of a policy/program designed to change the outcome. In parallel, investigators often analyze rates of negative outcomes that might be (unintentionally) affected by the policy/program. We discuss why ITS is a useful tool for quality improvement. Strengths of ITS include the ability to control for secular trends in the data (unlike a 2-period before-and-after t test), ability to evaluate outcomes using population-level data, clear graphical presentation of results, ease of conducting stratified analyses, and ability to evaluate both intended and unintended consequences of interventions. Limitations of ITS include the need for a minimum of 8 time periods before and 8 after an intervention to evaluate changes statistically, difficulty in analyzing the independent impact of separate components of a program that are implemented close together in time, and existence of a suitable control population. Investigators must also be careful not to make individual-level inferences when population-level rates are used to evaluate interventions (though ITS can be used with individual-level data). A brief description of ITS is provided, including a fully implemented (but hypothetical) study of the impact of a program to reduce ADHD medication initiation in children younger than 5 years old and insured by Medicaid in Washington State. An example of the database needed to conduct an ITS is provided, as well as SAS code to implement a difference-in-differences model using preschool-age children in California as a comparison group. Copyright © 2013 Academic Pediatric Association. Published by Elsevier Inc. All rights reserved.

Nonlinear modeling of chaotic time series: Theory and applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Casdagli, M.; Eubank, S.; Farmer, J.D.

1990-01-01

We review recent developments in the modeling and prediction of nonlinear time series. In some cases apparent randomness in time series may be due to chaotic behavior of a nonlinear but deterministic system. In such cases it is possible to exploit the determinism to make short term forecasts that are much more accurate than one could make from a linear stochastic model. This is done by first reconstructing a state space, and then using nonlinear function approximation methods to create a dynamical model. Nonlinear models are valuable not only as short term forecasters, but also as diagnostic tools for identifyingmore » and quantifying low-dimensional chaotic behavior. During the past few years methods for nonlinear modeling have developed rapidly, and have already led to several applications where nonlinear models motivated by chaotic dynamics provide superior predictions to linear models. These applications include prediction of fluid flows, sunspots, mechanical vibrations, ice ages, measles epidemics and human speech. 162 refs., 13 figs.« less

MODIS Interactive Subsetting Tool (MIST)

NASA Astrophysics Data System (ADS)

McAllister, M.; Duerr, R.; Haran, T.; Khalsa, S. S.; Miller, D.

2008-12-01

In response to requests from the user community, NSIDC has teamed with the Oak Ridge National Laboratory Distributive Active Archive Center (ORNL DAAC) and the Moderate Resolution Data Center (MrDC) to provide time series subsets of satellite data covering stations in the Greenland Climate Network (GC-NET) and the International Arctic Systems for Observing the Atmosphere (IASOA) network. To serve these data NSIDC created the MODIS Interactive Subsetting Tool (MIST). MIST works with 7 km by 7 km subset time series of certain Version 5 (V005) MODIS products over GC-Net and IASOA stations. User- selected data are delivered in a text Comma Separated Value (CSV) file format. MIST also provides online analysis capabilities that include generating time series and scatter plots. Currently, MIST is a Beta prototype and NSIDC intends that user requests will drive future development of the tool. The intent of this poster is to introduce MIST to the MODIS data user audience and illustrate some of the online analysis capabilities.

Visibility graph analysis of heart rate time series and bio-marker of congestive heart failure

NASA Astrophysics Data System (ADS)

Bhaduri, Anirban; Bhaduri, Susmita; Ghosh, Dipak

2017-09-01

Study of RR interval time series for Congestive Heart Failure had been an area of study with different methods including non-linear methods. In this article the cardiac dynamics of heart beat are explored in the light of complex network analysis, viz. visibility graph method. Heart beat (RR Interval) time series data taken from Physionet database [46, 47] belonging to two groups of subjects, diseased (congestive heart failure) (29 in number) and normal (54 in number) are analyzed with the technique. The overall results show that a quantitative parameter can significantly differentiate between the diseased subjects and the normal subjects as well as different stages of the disease. Further, the data when split into periods of around 1 hour each and analyzed separately, also shows the same consistent differences. This quantitative parameter obtained using the visibility graph analysis thereby can be used as a potential bio-marker as well as a subsequent alarm generation mechanism for predicting the onset of Congestive Heart Failure.

Automatising the analysis of stochastic biochemical time-series

PubMed Central

2015-01-01

Background Mathematical and computational modelling of biochemical systems has seen a lot of effort devoted to the definition and implementation of high-performance mechanistic simulation frameworks. Within these frameworks it is possible to analyse complex models under a variety of configurations, eventually selecting the best setting of, e.g., parameters for a target system. Motivation This operational pipeline relies on the ability to interpret the predictions of a model, often represented as simulation time-series. Thus, an efficient data analysis pipeline is crucial to automatise time-series analyses, bearing in mind that errors in this phase might mislead the modeller's conclusions. Results For this reason we have developed an intuitive framework-independent Python tool to automate analyses common to a variety of modelling approaches. These include assessment of useful non-trivial statistics for simulation ensembles, e.g., estimation of master equations. Intuitive and domain-independent batch scripts will allow the researcher to automatically prepare reports, thus speeding up the usual model-definition, testing and refinement pipeline. PMID:26051821

41 CFR 51-8.6 - Aggregating requests.

Code of Federal Regulations, 2012 CFR

2012-07-01

..., or a group of requesters acting in concert, is attempting to break a request down into a series of... include the time period in which the requests have occurred and the subject matter involved. ...

41 CFR 51-8.6 - Aggregating requests.

Code of Federal Regulations, 2011 CFR

2011-07-01

..., or a group of requesters acting in concert, is attempting to break a request down into a series of... include the time period in which the requests have occurred and the subject matter involved. ...

41 CFR 51-8.6 - Aggregating requests.

Code of Federal Regulations, 2014 CFR

2014-07-01

..., or a group of requesters acting in concert, is attempting to break a request down into a series of... include the time period in which the requests have occurred and the subject matter involved. ...

An agreement coefficient for image comparison

USGS Publications Warehouse

Ji, Lei; Gallo, Kevin

2006-01-01

Combination of datasets acquired from different sensor systems is necessary to construct a long time-series dataset for remotely sensed land-surface variables. Assessment of the agreement of the data derived from various sources is an important issue in understanding the data continuity through the time-series. Some traditional measures, including correlation coefficient, coefficient of determination, mean absolute error, and root mean square error, are not always optimal for evaluating the data agreement. For this reason, we developed a new agreement coefficient for comparing two different images. The agreement coefficient has the following properties: non-dimensional, bounded, symmetric, and distinguishable between systematic and unsystematic differences. The paper provides examples of agreement analyses for hypothetical data and actual remotely sensed data. The results demonstrate that the agreement coefficient does include the above properties, and therefore is a useful tool for image comparison.

How do output growth-rate distributions look like? Some cross-country, time-series evidence

NASA Astrophysics Data System (ADS)

Fagiolo, G.; Napoletano, M.; Roventini, A.

2007-05-01

This paper investigates the statistical properties of within-country gross domestic product (GDP) and industrial production (IP) growth-rate distributions. Many empirical contributions have recently pointed out that cross-section growth rates of firms, industries and countries all follow Laplace distributions. In this work, we test whether also within-country, time-series GDP and IP growth rates can be approximated by tent-shaped distributions. We fit output growth rates with the exponential-power (Subbotin) family of densities, which includes as particular cases both Gaussian and Laplace distributions. We find that, for a large number of OECD (Organization for Economic Cooperation and Development) countries including the US, both GDP and IP growth rates are Laplace distributed. Moreover, we show that fat-tailed distributions robustly emerge even after controlling for outliers, autocorrelation and heteroscedasticity.

The Impact of the Revised Sunspot Record on Solar Irradiance Reconstructions

NASA Astrophysics Data System (ADS)

Kopp, G.; Krivova, N.; Lean, J.; Wu, C. J.

2015-12-01

We describe the expected effects of the new sunspot number time series on proxy model based reconstructions of the total solar irradiance (TSI), which is largely explained by the opposing effects of dark sunspots and bright faculae. Regressions of indices for facular brightening and sunspot darkening with time series of direct TSI observations during the recent 37-year spacecraft TSI measurement era determine the relative contributions from each. Historical TSI reconstructions are enabled by extending these proxy models back in time prior to the start of the measurement record using a variety of solar activity indices including the sunspot number time series alone prior to 1882. Such reconstructions are critical for Earth climate research, which requires knowledge of the incident energy from the Sun to assess climate sensitivity to the natural influence of solar variability. Two prominent TSI reconstructions that utilize the sunspot record starting in 1610 are the NRLTSI and the SATIRE models. We review the indices that each currently uses and estimate the effects the revised sunspot record has on these reconstructions.

Non-linear motions in reprocessed GPS station position time series

NASA Astrophysics Data System (ADS)

Rudenko, Sergei; Gendt, Gerd

2010-05-01

Global Positioning System (GPS) data of about 400 globally distributed stations obtained at time span from 1998 till 2007 were reprocessed using GFZ Potsdam EPOS (Earth Parameter and Orbit System) software within International GNSS Service (IGS) Tide Gauge Benchmark Monitoring (TIGA) Pilot Project and IGS Data Reprocessing Campaign with the purpose to determine weekly precise coordinates of GPS stations located at or near tide gauges. Vertical motions of these stations are used to correct the vertical motions of tide gauges for local motions and to tie tide gauge measurements to the geocentric reference frame. Other estimated parameters include daily values of the Earth rotation parameters and their rates, as well as satellite antenna offsets. The solution GT1 derived is based on using absolute phase center variation model, ITRF2005 as a priori reference frame, and other new models. The solution contributed also to ITRF2008. The time series of station positions are analyzed to identify non-linear motions caused by different effects. The paper presents the time series of GPS station coordinates and investigates apparent non-linear motions and their influence on GPS station height rates.

Hydrologic data for water years 1933-97 used in the River and Reservoir Operations Model, Truckee River basin, California and Nevada

USGS Publications Warehouse

Berris, Steven N.; Hess, Glen W.; Bohman, Larry R.

2000-01-01

Title II of Public Law 101-618, the Truckee?Carson?Pyramid Lake Water Rights Settlement Act of 1990, provides direction, authority, and a mechanism for resolving conflicts over water rights in the Truckee and Carson River Basins. The Truckee Carson Program of the U.S. Geological Survey, to support implementation of Public Law 101-618, has developed an operations model to simulate lake/reservoir and river operations for the Truckee River Basin including diversion of Truckee River water to the Truckee Canal for transport to the Carson River Basin. Several types of hydrologic data, formatted in a chronological order with a daily time interval called 'time series,' are described in this report. Time series from water years 1933 to 1997 can be used to run the operations model. Auxiliary hydrologic data not currently used by the model are also described. The time series of hydrologic data consist of flow, lake/reservoir elevation and storage, precipitation, evaporation, evapotranspiration, municipal and industrial (M&I) demand, and streamflow and lake/reservoir level forecast data.

An M-estimator for reduced-rank system identification.

PubMed

Chen, Shaojie; Liu, Kai; Yang, Yuguang; Xu, Yuting; Lee, Seonjoo; Lindquist, Martin; Caffo, Brian S; Vogelstein, Joshua T

2017-01-15

High-dimensional time-series data from a wide variety of domains, such as neuroscience, are being generated every day. Fitting statistical models to such data, to enable parameter estimation and time-series prediction, is an important computational primitive. Existing methods, however, are unable to cope with the high-dimensional nature of these data, due to both computational and statistical reasons. We mitigate both kinds of issues by proposing an M-estimator for Reduced-rank System IDentification ( MR. SID). A combination of low-rank approximations, ℓ 1 and ℓ 2 penalties, and some numerical linear algebra tricks, yields an estimator that is computationally efficient and numerically stable. Simulations and real data examples demonstrate the usefulness of this approach in a variety of problems. In particular, we demonstrate that MR. SID can accurately estimate spatial filters, connectivity graphs, and time-courses from native resolution functional magnetic resonance imaging data. MR. SID therefore enables big time-series data to be analyzed using standard methods, readying the field for further generalizations including non-linear and non-Gaussian state-space models.

An M-estimator for reduced-rank system identification

PubMed Central

Chen, Shaojie; Liu, Kai; Yang, Yuguang; Xu, Yuting; Lee, Seonjoo; Lindquist, Martin; Caffo, Brian S.; Vogelstein, Joshua T.

2018-01-01

High-dimensional time-series data from a wide variety of domains, such as neuroscience, are being generated every day. Fitting statistical models to such data, to enable parameter estimation and time-series prediction, is an important computational primitive. Existing methods, however, are unable to cope with the high-dimensional nature of these data, due to both computational and statistical reasons. We mitigate both kinds of issues by proposing an M-estimator for Reduced-rank System IDentification ( MR. SID). A combination of low-rank approximations, ℓ1 and ℓ2 penalties, and some numerical linear algebra tricks, yields an estimator that is computationally efficient and numerically stable. Simulations and real data examples demonstrate the usefulness of this approach in a variety of problems. In particular, we demonstrate that MR. SID can accurately estimate spatial filters, connectivity graphs, and time-courses from native resolution functional magnetic resonance imaging data. MR. SID therefore enables big time-series data to be analyzed using standard methods, readying the field for further generalizations including non-linear and non-Gaussian state-space models. PMID:29391659

Multiscale Poincaré plots for visualizing the structure of heartbeat time series.

PubMed

Henriques, Teresa S; Mariani, Sara; Burykin, Anton; Rodrigues, Filipa; Silva, Tiago F; Goldberger, Ary L

2016-02-09

Poincaré delay maps are widely used in the analysis of cardiac interbeat interval (RR) dynamics. To facilitate visualization of the structure of these time series, we introduce multiscale Poincaré (MSP) plots. Starting with the original RR time series, the method employs a coarse-graining procedure to create a family of time series, each of which represents the system's dynamics in a different time scale. Next, the Poincaré plots are constructed for the original and the coarse-grained time series. Finally, as an optional adjunct, color can be added to each point to represent its normalized frequency. We illustrate the MSP method on simulated Gaussian white and 1/f noise time series. The MSP plots of 1/f noise time series reveal relative conservation of the phase space area over multiple time scales, while those of white noise show a marked reduction in area. We also show how MSP plots can be used to illustrate the loss of complexity when heartbeat time series from healthy subjects are compared with those from patients with chronic (congestive) heart failure syndrome or with atrial fibrillation. This generalized multiscale approach to Poincaré plots may be useful in visualizing other types of time series.

Gulf of California biogeographic regions based on coastal zone color scanner imagery

NASA Astrophysics Data System (ADS)

SantamaríA-Del-Angel, Eduardo; Alvarez-Borrego, Saúl; Müller-Karger, Frank E.

1994-04-01

Topographically, the Gulf of California is divided into a series of basins and trenches that deepen to the south. Maximum depth at the mouth is greater than 3000 m. Most of the northern gulf is less than 200 m deep. The gulf has hydrographic features conducive to high primary productivity. Upwelling events have been described on the basis of temperature distributions at the eastern coast during winter and spring and at the western coast during summer. Tidal amplitude may be as high as 9 m in the upper gulf. On the basis of discrete phytoplankton sampling, the gulf was previously divided into four geographic regions. This division took into consideration only the space distribution, taxonomic composition, and abundance of microphytoplankton. With the availability of the coastal zone color scanner (CZCS) imagery, we were able to include the time variability of pigments to make a more detailed biogeographic division of the gulf. With weekly composites of the imagery, we generated time series of pigment concentrations for 33 locations throughout the gulf and for the whole life span of the CZCS. The time series show a clear seasonal variation, with maxima in winter and spring and minima in summer. The effect of upwelling at the eastern coast is clearly evident, with high pigment concentrations. The effect of the summer upwelling off the Baja California coast is not evident in these time series. Time series from locations on the western side of the gulf also show maxima in winter and spring that are due to the eddy circulation that brings upwelled water from the eastern side. Principal-component analysis was applied to define 14 regions. Ballenas Channel, between Angel de la Guarda and Baja California, and the upper gulf always appeared as very distinct regions. Some of these 14 regions relate to the geographic distributions of important faunal groups, including the benthos, or their life cycles. For example, the upper gulf is a place for reproduction and the nursery of many fish species, marine mammals and birds are specially abundant in Ballenas Channel, sardine spawning mostly occurs in the central gulf in spring, and shrimp are abundant off mainland Mexico.

Multiparametric evaluation of hindlimb ischemia using time-series indocyanine green fluorescence imaging.

PubMed

Guang, Huizhi; Cai, Chuangjian; Zuo, Simin; Cai, Wenjuan; Zhang, Jiulou; Luo, Jianwen

2017-03-01

Peripheral arterial disease (PAD) can further cause lower limb ischemia. Quantitative evaluation of the vascular perfusion in the ischemic limb contributes to diagnosis of PAD and preclinical development of new drug. In vivo time-series indocyanine green (ICG) fluorescence imaging can noninvasively monitor blood flow and has a deep tissue penetration. The perfusion rate estimated from the time-series ICG images is not enough for the evaluation of hindlimb ischemia. The information relevant to the vascular density is also important, because angiogenesis is an essential mechanism for post-ischemic recovery. In this paper, a multiparametric evaluation method is proposed for simultaneous estimation of multiple vascular perfusion parameters, including not only the perfusion rate but also the vascular perfusion density and the time-varying ICG concentration in veins. The target method is based on a mathematical model of ICG pharmacokinetics in the mouse hindlimb. The regression analysis performed on the time-series ICG images obtained from a dynamic reflectance fluorescence imaging system. The results demonstrate that the estimated multiple parameters are effective to quantitatively evaluate the vascular perfusion and distinguish hypo-perfused tissues from well-perfused tissues in the mouse hindlimb. The proposed multiparametric evaluation method could be useful for PAD diagnosis. The estimated perfusion rate and vascular perfusion density maps (left) and the time-varying ICG concentration in veins of the ankle region (right) of the normal and ischemic hindlimbs. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Annual, Seasonal, and Secular Changes in Time-Variable Gravity from GRACE

NASA Astrophysics Data System (ADS)

Lemoine, F. G.; Luthcke, S. B.; Klosko, S. M.; Rowlands, D. D.; Chinn, D. S.; McCarthy, J. J.; Ray, R. D.; Boy, J.

2007-12-01

The NASA/DLR GRACE mission, launched in 2002, has now operated for more than five years, producing monthly and ten-day snapshots of the variations of the gravity field of the Earth. The available solutions, either from spherical harmonics or from mascons, allow us new insights into the variations of surface gravity on the Earth at annual, inter-annual, and secular time scales. Our baseline time series, based on GGM02C, NCEP Atmospheric Gravity with IB, and GOT00 tides now is extended to July 2007, spanning four+ years, and we analyze both mascon and spherical harmonic solutions from this time series with respect to global hydrology variations. Our 4degx4deg mascon solutions are extended to cover all continental regions of the globe. Comparisons with hydrology (land-surface) models can offer insights into how these models might be improved. We compare our baseline time series, with new time series that include an updated Goddard Ocean Tide (GOT) model, ECMWF- 3hr atmosphere de-aliasing data, and the MOG-2D ocean dealiasing product. Finally, we intercompare the spherical harmonic solutions at low degree from GRACE from the various product centers (e.g., GFZ, CSR, GRGS), and look for secular signals in both the GSFC mascon and spherical harmonic solutions, taking care to compare the results for secular gravity field change with independent solutions developed over 25 years of independent tracking to geodetic satellites by Satellite Laser Ranging (SLR) and DORIS.

Non-parametric directionality analysis - Extension for removal of a single common predictor and application to time series.

PubMed

Halliday, David M; Senik, Mohd Harizal; Stevenson, Carl W; Mason, Rob

2016-08-01

The ability to infer network structure from multivariate neuronal signals is central to computational neuroscience. Directed network analyses typically use parametric approaches based on auto-regressive (AR) models, where networks are constructed from estimates of AR model parameters. However, the validity of using low order AR models for neurophysiological signals has been questioned. A recent article introduced a non-parametric approach to estimate directionality in bivariate data, non-parametric approaches are free from concerns over model validity. We extend the non-parametric framework to include measures of directed conditional independence, using scalar measures that decompose the overall partial correlation coefficient summatively by direction, and a set of functions that decompose the partial coherence summatively by direction. A time domain partial correlation function allows both time and frequency views of the data to be constructed. The conditional independence estimates are conditioned on a single predictor. The framework is applied to simulated cortical neuron networks and mixtures of Gaussian time series data with known interactions. It is applied to experimental data consisting of local field potential recordings from bilateral hippocampus in anaesthetised rats. The framework offers a non-parametric approach to estimation of directed interactions in multivariate neuronal recordings, and increased flexibility in dealing with both spike train and time series data. The framework offers a novel alternative non-parametric approach to estimate directed interactions in multivariate neuronal recordings, and is applicable to spike train and time series data. Copyright © 2016 Elsevier B.V. All rights reserved.

A morphological perceptron with gradient-based learning for Brazilian stock market forecasting.

PubMed

Araújo, Ricardo de A

2012-04-01

Several linear and non-linear techniques have been proposed to solve the stock market forecasting problem. However, a limitation arises from all these techniques and is known as the random walk dilemma (RWD). In this scenario, forecasts generated by arbitrary models have a characteristic one step ahead delay with respect to the time series values, so that, there is a time phase distortion in stock market phenomena reconstruction. In this paper, we propose a suitable model inspired by concepts in mathematical morphology (MM) and lattice theory (LT). This model is generically called the increasing morphological perceptron (IMP). Also, we present a gradient steepest descent method to design the proposed IMP based on ideas from the back-propagation (BP) algorithm and using a systematic approach to overcome the problem of non-differentiability of morphological operations. Into the learning process we have included a procedure to overcome the RWD, which is an automatic correction step that is geared toward eliminating time phase distortions that occur in stock market phenomena. Furthermore, an experimental analysis is conducted with the IMP using four complex non-linear problems of time series forecasting from the Brazilian stock market. Additionally, two natural phenomena time series are used to assess forecasting performance of the proposed IMP with other non financial time series. At the end, the obtained results are discussed and compared to results found using models recently proposed in the literature. Copyright © 2011 Elsevier Ltd. All rights reserved.

A literature review of intercostal-to-musculocutaneous-nerve transfers in brachial plexus injury patients: Does body mass index influence results in Eastern versus Western countries?

PubMed

Socolovsky, Mariano; Paez, Miguel Domínguez

2013-11-27

A wide range of results have appeared in the literature for intercostal nerve transfers in brachial plexus patients. Oriental countries generally have a lower body mass index (BMI) than their occidental counterparts. We analyzed published series of intercostal nerve transfers for elbow reinnervation to determine if a difference in outcomes exists between Eastern and Western series that could be inversely related to BMI. A PubMed search was conducted. Inclusion criteria were: (1) time from trauma to surgery <12 months, (2) minimum follow-up one year, (3) intercostal to musculocutaneous nerve transfer the only surgical procedure performed to reestablish elbow flexion, and (4) males comprising more than 75% of cases. Two groups were created: Series from western countries, including America, Europe, and Africa; and series from Asia. Pearson correlation analysis was performed to assess for the degree of correlation between percent responders and mean national BMI. A total of 26 series were included, 14 from western countries and 12 from Eastern countries, encompassing a total of 274 and 432 surgical cases, respectively. The two groups were almost identical in mean age, but quite different in mean national BMI (26.3 vs. 22.5) and in the percentage of patients who achieved at least a Medical Research Council (MRC) level 3 (59.5% vs. 79.3%). Time from trauma to surgery was slightly shorter in Eastern (3.4 months) versus Western countries (5.0 months). The percentage of responders to intercostal to musculocutaneous nerve transfer was inversely correlated with the mean national BMI among male residents of the country where the series was performed.

Degree-Pruning Dynamic Programming Approaches to Central Time Series Minimizing Dynamic Time Warping Distance.

PubMed

Sun, Tao; Liu, Hongbo; Yu, Hong; Chen, C L Philip

2016-06-28

The central time series crystallizes the common patterns of the set it represents. In this paper, we propose a global constrained degree-pruning dynamic programming (g(dp)²) approach to obtain the central time series through minimizing dynamic time warping (DTW) distance between two time series. The DTW matching path theory with global constraints is proved theoretically for our degree-pruning strategy, which is helpful to reduce the time complexity and computational cost. Our approach can achieve the optimal solution between two time series. An approximate method to the central time series of multiple time series [called as m_g(dp)²] is presented based on DTW barycenter averaging and our g(dp)² approach by considering hierarchically merging strategy. As illustrated by the experimental results, our approaches provide better within-group sum of squares and robustness than other relevant algorithms.

TSPP - A Collection of FORTRAN Programs for Processing and Manipulating Time Series

USGS Publications Warehouse

Boore, David M.

2008-01-01

This report lists a number of FORTRAN programs that I have developed over the years for processing and manipulating strong-motion accelerograms. The collection is titled TSPP, which stands for Time Series Processing Programs. I have excluded 'strong-motion accelerograms' from the title, however, as the boundary between 'strong' and 'weak' motion has become blurred with the advent of broadband sensors and high-dynamic range dataloggers, and many of the programs can be used with any evenly spaced time series, not just acceleration time series. This version of the report is relatively brief, consisting primarily of an annotated list of the programs, with two examples of processing, and a few comments on usage. I do not include a parameter-by-parameter guide to the programs. Future versions might include more examples of processing, illustrating the various parameter choices in the programs. Although these programs have been used by the U.S. Geological Survey, no warranty, expressed or implied, is made by the USGS as to the accuracy or functioning of the programs and related program material, nor shall the fact of distribution constitute any such warranty, and no responsibility is assumed by the USGS in connection therewith. The programs are distributed on an 'as is' basis, with no warranty of support from me. These programs were written for my use and are being publically distributed in the hope that others might find them as useful as I have. I would, however, appreciate being informed about bugs, and I always welcome suggestions for improvements to the codes. Please note that I have made little effort to optimize the coding of the programs or to include a user-friendly interface (many of the programs in this collection have been included in the software usdp (Utility Software for Data Processing), being developed by Akkar et al. (personal communication, 2008); usdp includes a graphical user interface). Speed of execution has been sacrificed in favor of a code that is intended to be easy to understand, although on modern computers speed of execution is rarely a problem. I will be pleased if users incorporate portions of my programs into their own applications; I only ask that reference be made to this report as the source of the programs.

SPA- STATISTICAL PACKAGE FOR TIME AND FREQUENCY DOMAIN ANALYSIS

NASA Technical Reports Server (NTRS)

Brownlow, J. D.

1994-01-01

The need for statistical analysis often arises when data is in the form of a time series. This type of data is usually a collection of numerical observations made at specified time intervals. Two kinds of analysis may be performed on the data. First, the time series may be treated as a set of independent observations using a time domain analysis to derive the usual statistical properties including the mean, variance, and distribution form. Secondly, the order and time intervals of the observations may be used in a frequency domain analysis to examine the time series for periodicities. In almost all practical applications, the collected data is actually a mixture of the desired signal and a noise signal which is collected over a finite time period with a finite precision. Therefore, any statistical calculations and analyses are actually estimates. The Spectrum Analysis (SPA) program was developed to perform a wide range of statistical estimation functions. SPA can provide the data analyst with a rigorous tool for performing time and frequency domain studies. In a time domain statistical analysis the SPA program will compute the mean variance, standard deviation, mean square, and root mean square. It also lists the data maximum, data minimum, and the number of observations included in the sample. In addition, a histogram of the time domain data is generated, a normal curve is fit to the histogram, and a goodness-of-fit test is performed. These time domain calculations may be performed on both raw and filtered data. For a frequency domain statistical analysis the SPA program computes the power spectrum, cross spectrum, coherence, phase angle, amplitude ratio, and transfer function. The estimates of the frequency domain parameters may be smoothed with the use of Hann-Tukey, Hamming, Barlett, or moving average windows. Various digital filters are available to isolate data frequency components. Frequency components with periods longer than the data collection interval are removed by least-squares detrending. As many as ten channels of data may be analyzed at one time. Both tabular and plotted output may be generated by the SPA program. This program is written in FORTRAN IV and has been implemented on a CDC 6000 series computer with a central memory requirement of approximately 142K (octal) of 60 bit words. This core requirement can be reduced by segmentation of the program. The SPA program was developed in 1978.

From Networks to Time Series

NASA Astrophysics Data System (ADS)

Shimada, Yutaka; Ikeguchi, Tohru; Shigehara, Takaomi

2012-10-01

In this Letter, we propose a framework to transform a complex network to a time series. The transformation from complex networks to time series is realized by the classical multidimensional scaling. Applying the transformation method to a model proposed by Watts and Strogatz [Nature (London) 393, 440 (1998)], we show that ring lattices are transformed to periodic time series, small-world networks to noisy periodic time series, and random networks to random time series. We also show that these relationships are analytically held by using the circulant-matrix theory and the perturbation theory of linear operators. The results are generalized to several high-dimensional lattices.

76 FR 14111 - Self-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-15

... Options Series, adjusted option series and any options series until the time to expiration for such series... time to expiration for such series is less than nine months be treated differently. Specifically, under... until the time to expiration for such series is less than nine months. Accordingly, the requirement to...

Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance

PubMed Central

Chen, Jingli; Wu, Shuai; Liu, Zhizhong; Chao, Hao

2018-01-01

Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy. PMID:29795600

Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance.

PubMed

Liu, Yongli; Chen, Jingli; Wu, Shuai; Liu, Zhizhong; Chao, Hao

2018-01-01

Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy.

Visibility Graph Based Time Series Analysis.

PubMed

Stephen, Mutua; Gu, Changgui; Yang, Huijie

2015-01-01

Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it's microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq) and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks.

What does the structure of its visibility graph tell us about the nature of the time series?

NASA Astrophysics Data System (ADS)

Franke, Jasper G.; Donner, Reik V.

2017-04-01

Visibility graphs are a recently introduced method to construct complex network representations based upon univariate time series in order to study their dynamical characteristics [1]. In the last years, this approach has been successfully applied to studying a considerable variety of geoscientific research questions and data sets, including non-trivial temporal patterns in complex earthquake catalogs [2] or time-reversibility in climate time series [3]. It has been shown that several characteristic features of the thus constructed networks differ between stochastic and deterministic (possibly chaotic) processes, which is, however, relatively hard to exploit in the case of real-world applications. In this study, we propose studying two new measures related with the network complexity of visibility graphs constructed from time series, one being a special type of network entropy [4] and the other a recently introduced measure of the heterogeneity of the network's degree distribution [5]. For paradigmatic model systems exhibiting bifurcation sequences between regular and chaotic dynamics, both properties clearly trace the transitions between both types of regimes and exhibit marked quantitative differences for regular and chaotic dynamics. Moreover, for dynamical systems with a small amount of additive noise, the considered properties demonstrate gradual changes prior to the bifurcation point. This finding appears closely related to the subsequent loss of stability of the current state known to lead to a critical slowing down as the transition point is approaches. In this spirit, both considered visibility graph characteristics provide alternative tracers of dynamical early warning signals consistent with classical indicators. Our results demonstrate that measures of visibility graph complexity (i) provide a potentially useful means to tracing changes in the dynamical patterns encoded in a univariate time series that originate from increasing autocorrelation and (ii) allow to systematically distinguish regular from deterministic-chaotic dynamics. We demonstrate the application of our method for different model systems as well as selected paleoclimate time series from the North Atlantic region. Notably, visibility graph based methods are particularly suited for studying the latter type of geoscientific data, since they do not impose intrinsic restrictions or assumptions on the nature of the time series under investigation in terms of noise process, linearity and sampling homogeneity. [1] Lacasa, Lucas, et al. "From time series to complex networks: The visibility graph." Proceedings of the National Academy of Sciences 105.13 (2008): 4972-4975. [2] Telesca, Luciano, and Michele Lovallo. "Analysis of seismic sequences by using the method of visibility graph." EPL (Europhysics Letters) 97.5 (2012): 50002. [3] Donges, Jonathan F., Reik V. Donner, and Jürgen Kurths. "Testing time series irreversibility using complex network methods." EPL (Europhysics Letters) 102.1 (2013): 10004. [4] Small, Michael. "Complex networks from time series: capturing dynamics." 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013), Beijing (2013): 2509-2512. [5] Jacob, Rinku, K.P. Harikrishnan, Ranjeev Misra, and G. Ambika. "Measure for degree heterogeneity in complex networks and its application to recurrence network analysis." arXiv preprint 1605.06607 (2016).

Celebrating Einstein A Series of Articles

Science.gov Websites

details may be included from time to time. Usually, the chief significance of these details is simply to which time doesn’t pass at the same rate for everyone, space bends, and chance prevails where we would the time we had no way to see atoms. Another Side of Light - The fact that light is a kind of wave is

A Millennial-length Reconstruction of the Western Pacific Pattern with Associated Paleoclimate

NASA Astrophysics Data System (ADS)

Wright, W. E.; Guan, B. T.; Wei, K.

2010-12-01

The Western Pacific Pattern (WP) is a lesser known 500 hPa pressure pattern similar to the NAO or PNA. As defined, the poles of the WP index are centered on 60°N over the Kamchatka peninsula and the neighboring Pacific and on 32.5°N over the western north Pacific. However, the area of influence for the southern half of the dipole includes a wide swath from East Asia, across Taiwan, through the Philippine Sea, to the western north Pacific. Tree rings of Taiwanese Chamaecyparis obtusa var. formosana in this extended region show significant correlation with the WP, and with local temperature. The WP is also significantly correlated with atmospheric temperatures over Taiwan, especially at 850hPa and 700 hPa, pressure levels that bracket the tree site. Spectral analysis indicates that variations in the WP occur at relatively high frequency, with most power at less than 5 years. Simple linear regression against high frequency variants of the tree-ring chronology yielded the most significant correlation coefficients. Two reconstructions are presented. The first uses a tree-ring time series produced as the first intrinsic mode function (IMF) from an Ensemble Empirical Mode Decomposition (EEMD), based on the Hilbert-Huang Transform. The significance of the regression using the EEMD-derived time series was much more significant than time series produced using traditional high pass filtering. The second also uses the first IMF of a tree-ring time series, but the dataset was first sorted and partitioned at a specified quantile prior to EEMD decomposition, with the mean of the partitioned data forming the input to the EEMD. The partitioning was done to filter out the less climatically sensitive tree rings, a common problem with shade tolerant trees. Time series statistics indicate that the first reconstruction is reliable to 1241 of the Common Era. Reliability of the second reconstruction is dependent on the development of statistics related to the quantile partitioning, and the consequent reduction in sample depth. However, the correlation coefficients from regressions over the instrumental period greatly exceed those from any other method of chronology generation, and so the technique holds promise. Additional atmospheric parameters having significant correlations against the WPO and tree ring time series with similar spatial patterns are also presented. These include vertical wind shear (850hPa-700hPa) over the northern Philippines and the Philippine Sea, surface Omega and 850hPa v-winds over the East China Sea, Japan and Taiwan. Possible links to changes in the subtropical jet stream will also be discussed.

75 FR 51075 - National Registry of Evidence-Based Programs and Practices (NREPP): Open Submission Period for...

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2010-08-18

... and pre- and post-intervention assessments; this category includes longitudinal/multiple time series... Registry of Evidence-based Programs and Practices (NREPP) is a voluntary rating system designed to provide... acceptance status at that time. The number of reviews conducted will depend on the availability of funds...

Chinese Communicating in the Culture Performance 2

ERIC Educational Resources Information Center

Walker, Galal

2005-01-01

This is the second text in a series of Mandarin Chinese learning texts. It continues with the theme of learning to communicate in various forms, especially with time and location, and the hanzi writing system is introduced. An MP3 file accompanies this book. Contents include: (1) Acknowledgments; (2) Introduction; (3) Unit Three, Time When:…

The Hues of English. NCTE Distinguished Lectures 1969.

ERIC Educational Resources Information Center

National Council of Teachers of English, Champaign, IL.

The third volume in the NCTE Distinguished Lectures Series, this collection of papers includes (1) William Stafford on poetry and the language of everyday life, (2) Fred Stocking linking Shakespeare to his time and all time by analysing "temperance" in Sonnet 18, (3) Alan Downer discussing the nature of comedy in drama and the universal…

Time-dependent Reliability of Dynamic Systems using Subset Simulation with Splitting over a Series of Correlated Time Intervals

DTIC Science & Technology

2013-08-01

cost due to potential warranty costs, repairs and loss of market share. Reliability is the probability that the system will perform its intended...MCMC and splitting sampling schemes. Our proposed SS/ STP method is presented in Section 4, including accuracy bounds and computational effort

Applied automatic offset detection using HECTOR within EPOS-IP

NASA Astrophysics Data System (ADS)

Fernandes, R. M. S.; Bos, M. S.

2016-12-01

It is well known that offsets are present in most GNSS coordinate time series. These offsets need to be taken into account in the analysis to avoid incorrect estimation of the tectonic motions. The time of the offsets are normally determined by visual inspection of the time series but with the ever increasing amount of GNSS stations, this is becoming too time consuming and automatic offset detection algorithms are required. This is particularly true in projects like EPOS (European Plate Observing System), where the routinely analysis of thousands of daily time-series will be required. It is also planned to include stations installed for technical applications which metadata is also not always properly maintained. In this research we present an offset detection scheme that uses the Bayesian Information Criterion (BIC) to determine the most likely time of an offset. The novelty of this scheme is that it takes the temporal correlation of the noise into account. This aspect is normally ignored due to the fact that it significantly increases the computation time. However, it needs to be taken into account to ensure that the estimated BIC value is correct. We were able to create a fast algorithm by adopting the methodology implemented in HECTOR (Bos et al., 2013). We evaluate the feasibility of the approach using the core IGS network, where most of the offsets have been accurately determined, which permit to have an external evaluation of this new outlier detection approach to be included in HECTOR. We also apply the scheme to regional networks in Iberia where such offsets are often not documented properly in order to compare the normal manual approach with the new automatic approach. Finally, we also compare the optimal approach used by HECTOR with other algorithms such as MIDAS and STARS.

EMC: Air Quality Forecast Home page

Science.gov Websites

archive NAM Verification Meteorology Error Time Series EMC NAM Spatial Maps Real Time Mesoscale Analysis Precipitation verification NAQFC VERIFICATION CMAQ Ozone & PM Error Time Series AOD Error Time Series HYSPLIT Smoke forecasts vs GASP satellite Dust and Smoke Error Time Series HYSPLIT WCOSS Upgrade (July

Time Series Remote Sensing in Monitoring the Spatio-Temporal Dynamics of Plant Invasions: A Study of Invasive Saltcedar (Tamarix Spp.)

NASA Astrophysics Data System (ADS)

Diao, Chunyuan

In today's big data era, the increasing availability of satellite and airborne platforms at various spatial and temporal scales creates unprecedented opportunities to understand the complex and dynamic systems (e.g., plant invasion). Time series remote sensing is becoming more and more important to monitor the earth system dynamics and interactions. To date, most of the time series remote sensing studies have been conducted with the images acquired at coarse spatial scale, due to their relatively high temporal resolution. The construction of time series at fine spatial scale, however, is limited to few or discrete images acquired within or across years. The objective of this research is to advance the time series remote sensing at fine spatial scale, particularly to shift from discrete time series remote sensing to continuous time series remote sensing. The objective will be achieved through the following aims: 1) Advance intra-annual time series remote sensing under the pure-pixel assumption; 2) Advance intra-annual time series remote sensing under the mixed-pixel assumption; 3) Advance inter-annual time series remote sensing in monitoring the land surface dynamics; and 4) Advance the species distribution model with time series remote sensing. Taking invasive saltcedar as an example, four methods (i.e., phenological time series remote sensing model, temporal partial unmixing method, multiyear spectral angle clustering model, and time series remote sensing-based spatially explicit species distribution model) were developed to achieve the objectives. Results indicated that the phenological time series remote sensing model could effectively map saltcedar distributions through characterizing the seasonal phenological dynamics of plant species throughout the year. The proposed temporal partial unmixing method, compared to conventional unmixing methods, could more accurately estimate saltcedar abundance within a pixel by exploiting the adequate temporal signatures of saltcedar. The multiyear spectral angle clustering model could guide the selection of the most representative remotely sensed image for repetitive saltcedar mapping over space and time. Through incorporating spatial autocorrelation, the species distribution model developed in the study could identify the suitable habitats of saltcedar at a fine spatial scale and locate appropriate areas at high risk of saltcedar infestation. Among 10 environmental variables, the distance to the river and the phenological attributes summarized by the time series remote sensing were regarded as the most important. These methods developed in the study provide new perspectives on how the continuous time series can be leveraged under various conditions to investigate the plant invasion dynamics.

A hybrid approach EMD-HW for short-term forecasting of daily stock market time series data

NASA Astrophysics Data System (ADS)

Awajan, Ahmad Mohd; Ismail, Mohd Tahir

2017-08-01

Recently, forecasting time series has attracted considerable attention in the field of analyzing financial time series data, specifically within the stock market index. Moreover, stock market forecasting is a challenging area of financial time-series forecasting. In this study, a hybrid methodology between Empirical Mode Decomposition with the Holt-Winter method (EMD-HW) is used to improve forecasting performances in financial time series. The strength of this EMD-HW lies in its ability to forecast non-stationary and non-linear time series without a need to use any transformation method. Moreover, EMD-HW has a relatively high accuracy and offers a new forecasting method in time series. The daily stock market time series data of 11 countries is applied to show the forecasting performance of the proposed EMD-HW. Based on the three forecast accuracy measures, the results indicate that EMD-HW forecasting performance is superior to traditional Holt-Winter forecasting method.

Planning and conducting meetings effectively, part I: planning a meeting.

PubMed

Harolds, Jay

2011-12-01

Meetings are held by leaders for many purposes, including conveying information, raising morale, asking for opinions, brain storming, making people part of the problem-solving process, building trust, getting to a consensus, and making decisions. However, many meetings waste time, some undermine the leader's power, and some decrease morale. Part I of this series of articles gives some tips on basic planning for decision-making meetings. Part II of this series of articles analyzes selected components of decision-making meetings. Part III of this series will be on how the chairperson keeps decision-making meetings on track to make them efficient and productive.

MAKING THE WEASELS WILD AGAIN: ENSURING FUTURE AIR DOMINANCE THROUGH EFFECTIVE SEAD TRAINING

DTIC Science & Technology

2016-06-01

both multi-mission design series (MMDS) and joint SEAD training as well as improve the capabilities of its electronic warfare (EW) ranges in order...USAF units to train for multi-mission design series (MMDS) SEAD operations.14 MMDS training includes the use of multiple USAF airborne platforms...not provided SEAD aircrews with either the quantity or quality of training required to conduct effective operations.2 At that time , Major Jon Norman

Time series momentum and contrarian effects in the Chinese stock market

NASA Astrophysics Data System (ADS)

Shi, Huai-Long; Zhou, Wei-Xing

2017-10-01

This paper concentrates on the time series momentum or contrarian effects in the Chinese stock market. We evaluate the performance of the time series momentum strategy applied to major stock indices in mainland China and explore the relation between the performance of time series momentum strategies and some firm-specific characteristics. Our findings indicate that there is a time series momentum effect in the short run and a contrarian effect in the long run in the Chinese stock market. The performances of the time series momentum and contrarian strategies are highly dependent on the look-back and holding periods and firm-specific characteristics.

A Multitaper, Causal Decomposition for Stochastic, Multivariate Time Series: Application to High-Frequency Calcium Imaging Data.

PubMed

Sornborger, Andrew T; Lauderdale, James D

2016-11-01

Neural data analysis has increasingly incorporated causal information to study circuit connectivity. Dimensional reduction forms the basis of most analyses of large multivariate time series. Here, we present a new, multitaper-based decomposition for stochastic, multivariate time series that acts on the covariance of the time series at all lags, C ( τ ), as opposed to standard methods that decompose the time series, X ( t ), using only information at zero-lag. In both simulated and neural imaging examples, we demonstrate that methods that neglect the full causal structure may be discarding important dynamical information in a time series.

Graphical Data Analysis on the Circle: Wrap-Around Time Series Plots for (Interrupted) Time Series Designs.

PubMed

Rodgers, Joseph Lee; Beasley, William Howard; Schuelke, Matthew

2014-01-01

Many data structures, particularly time series data, are naturally seasonal, cyclical, or otherwise circular. Past graphical methods for time series have focused on linear plots. In this article, we move graphical analysis onto the circle. We focus on 2 particular methods, one old and one new. Rose diagrams are circular histograms and can be produced in several different forms using the RRose software system. In addition, we propose, develop, illustrate, and provide software support for a new circular graphical method, called Wrap-Around Time Series Plots (WATS Plots), which is a graphical method useful to support time series analyses in general but in particular in relation to interrupted time series designs. We illustrate the use of WATS Plots with an interrupted time series design evaluating the effect of the Oklahoma City bombing on birthrates in Oklahoma County during the 10 years surrounding the bombing of the Murrah Building in Oklahoma City. We compare WATS Plots with linear time series representations and overlay them with smoothing and error bands. Each method is shown to have advantages in relation to the other; in our example, the WATS Plots more clearly show the existence and effect size of the fertility differential.

Modeling Individual Cyclic Variation in Human Behavior.

PubMed

Pierson, Emma; Althoff, Tim; Leskovec, Jure

2018-04-01

Cycles are fundamental to human health and behavior. Examples include mood cycles, circadian rhythms, and the menstrual cycle. However, modeling cycles in time series data is challenging because in most cases the cycles are not labeled or directly observed and need to be inferred from multidimensional measurements taken over time. Here, we present Cyclic Hidden Markov Models (CyH-MMs) for detecting and modeling cycles in a collection of multidimensional heterogeneous time series data. In contrast to previous cycle modeling methods, CyHMMs deal with a number of challenges encountered in modeling real-world cycles: they can model multivariate data with both discrete and continuous dimensions; they explicitly model and are robust to missing data; and they can share information across individuals to accommodate variation both within and between individual time series. Experiments on synthetic and real-world health-tracking data demonstrate that CyHMMs infer cycle lengths more accurately than existing methods, with 58% lower error on simulated data and 63% lower error on real-world data compared to the best-performing baseline. CyHMMs can also perform functions which baselines cannot: they can model the progression of individual features/symptoms over the course of the cycle, identify the most variable features, and cluster individual time series into groups with distinct characteristics. Applying CyHMMs to two real-world health-tracking datasets-of human menstrual cycle symptoms and physical activity tracking data-yields important insights including which symptoms to expect at each point during the cycle. We also find that people fall into several groups with distinct cycle patterns, and that these groups differ along dimensions not provided to the model. For example, by modeling missing data in the menstrual cycles dataset, we are able to discover a medically relevant group of birth control users even though information on birth control is not given to the model.

Modeling Individual Cyclic Variation in Human Behavior

PubMed Central

Pierson, Emma; Althoff, Tim; Leskovec, Jure

2018-01-01

Cycles are fundamental to human health and behavior. Examples include mood cycles, circadian rhythms, and the menstrual cycle. However, modeling cycles in time series data is challenging because in most cases the cycles are not labeled or directly observed and need to be inferred from multidimensional measurements taken over time. Here, we present Cyclic Hidden Markov Models (CyH-MMs) for detecting and modeling cycles in a collection of multidimensional heterogeneous time series data. In contrast to previous cycle modeling methods, CyHMMs deal with a number of challenges encountered in modeling real-world cycles: they can model multivariate data with both discrete and continuous dimensions; they explicitly model and are robust to missing data; and they can share information across individuals to accommodate variation both within and between individual time series. Experiments on synthetic and real-world health-tracking data demonstrate that CyHMMs infer cycle lengths more accurately than existing methods, with 58% lower error on simulated data and 63% lower error on real-world data compared to the best-performing baseline. CyHMMs can also perform functions which baselines cannot: they can model the progression of individual features/symptoms over the course of the cycle, identify the most variable features, and cluster individual time series into groups with distinct characteristics. Applying CyHMMs to two real-world health-tracking datasets—of human menstrual cycle symptoms and physical activity tracking data—yields important insights including which symptoms to expect at each point during the cycle. We also find that people fall into several groups with distinct cycle patterns, and that these groups differ along dimensions not provided to the model. For example, by modeling missing data in the menstrual cycles dataset, we are able to discover a medically relevant group of birth control users even though information on birth control is not given to the model. PMID:29780976

Evaluating a community-based exercise intervention with adults living with HIV: protocol for an interrupted time series study

PubMed Central

O'Brien, Kelly K; Bayoumi, Ahmed M; Solomon, Patricia; Tang, Ada; Murzin, Kate; Chan Carusone, Soo; Zobeiry, Mehdi; Nayar, Ayesha; Davis, Aileen M

2016-01-01

Introduction Our aim was to evaluate a community-based exercise (CBE) intervention with the goal of reducing disability and enhancing health for community-dwelling people living with HIV (PLWH). Methods and analysis We will use a mixed-methods implementation science study design, including a prospective longitudinal interrupted time series study, to evaluate a CBE intervention with PLWH in Toronto, Canada. We will recruit PLWH who consider themselves medically stable and safe to participate in exercise. In the baseline phase (0–8 months), participants will be monitored bimonthly. In the intervention phase (8–14 months), participants will take part in a 24-week CBE intervention that includes aerobic, resistance, balance and flexibility exercise at the YMCA 3 times per week, with weekly supervision by a fitness instructor, and monthly educational sessions. In the follow-up phase (14–22 months), participants will be encouraged to continue to engage in unsupervised exercise 3 times per week. Quantitative assessment: We will assess cardiopulmonary fitness, strength, weight, body composition and flexibility outcomes followed by the administration of self-reported questionnaires to assess disability and contextual factor outcomes (coping, mastery, stigma, social support) bimonthly. We will use time series regression analysis to determine the level and trend of outcomes across each phase in relation to the intervention. Qualitative assessment: We will conduct a series of face-to-face interviews with a subsample of participants and recreation providers at initiation, midpoint and completion of the 24-week CBE intervention. We will explore experiences and anticipated benefits with exercise, perceived impact of CBE for PLWH and the strengths and challenges of implementing a CBE intervention. Interviews will be audio recorded and analysed thematically. Ethics and dissemination Protocol approved by the University of Toronto HIV/AIDS Research Ethics Board. Knowledge translation will occur with stakeholders in the form of presentations and publications in open access peer-reviewed journals. Trial registration number NCT02794415; Pre-results. PMID:27798038

Dynamic Black-Level Correction and Artifact Flagging for Kepler Pixel Time Series

NASA Technical Reports Server (NTRS)

Kolodziejczak, J. J.; Clarke, B. D.; Caldwell, D. A.

2011-01-01

Methods applied to the calibration stage of Kepler pipeline data processing [1] (CAL) do not currently use all of the information available to identify and correct several instrument-induced artifacts. These include time-varying crosstalk from the fine guidance sensor (FGS) clock signals, and manifestations of drifting moire pattern as locally correlated nonstationary noise, and rolling bands in the images which find their way into the time series [2], [3]. As the Kepler Mission continues to improve the fidelity of its science data products, we are evaluating the benefits of adding pipeline steps to more completely model and dynamically correct the FGS crosstalk, then use the residuals from these model fits to detect and flag spatial regions and time intervals of strong time-varying black-level which may complicate later processing or lead to misinterpretation of instrument behavior as stellar activity.

Part-time careers in academic internal medicine: a report from the association of specialty professors part-time careers task force on behalf of the alliance for academic internal medicine.

PubMed

Linzer, Mark; Warde, Carole; Alexander, R Wayne; Demarco, Deborah M; Haupt, Allison; Hicks, Leroi; Kutner, Jean; Mangione, Carol M; Mechaber, Hilit; Rentz, Meridith; Riley, Joanne; Schuster, Barbara; Solomon, Glen D; Volberding, Paul; Ibrahim, Tod

2009-10-01

To establish guidelines for more effectively incorporating part-time faculty into departments of internal medicine, a task force was convened in early 2007 by the Association of Specialty Professors. The task force used informal surveys, current literature, and consensus building among members of the Alliance for Academic Internal Medicine to produce a consensus statement and a series of recommendations. The task force agreed that part-time faculty could enrich a department of medicine, enhance workforce flexibility, and provide high-quality research, patient care, and education in a cost-effective manner. The task force provided a series of detailed steps for operationalizing part-time practice; to do so, key issues were addressed, such as fixed costs, malpractice insurance, space, cross-coverage, mentoring, career development, productivity targets, and flexible scheduling. Recommendations included (1) increasing respect for work-family balance, (2) allowing flexible time as well as part-time employment, (3) directly addressing negative perceptions about part-time faculty, (4) developing policies to allow flexibility in academic advancement, (5) considering part-time faculty as candidates for leadership positions, (6) encouraging granting agencies, including the National Institutes of Health and Veterans Administration, to consider part-time faculty as eligible for research career development awards, and (7) supporting future research in "best practices" for incorporating part-time faculty into academic departments of medicine.

Spectral entropy as a mean to quantify water stress history for natural vegetation and irrigated agriculture in a water-stressed tropical environment

NASA Astrophysics Data System (ADS)

Kim, Y.; Johnson, M. S.

2017-12-01

Spectral entropy (Hs) is an index which can be used to measure the structural complexity of time series data. When a time series is made up of one periodic function, the Hs value becomes smaller, while Hs becomes larger when a time series is composed of several periodic functions. We hypothesized that this characteristic of the Hs could be used to quantify the water stress history of vegetation. For the ideal condition for which sufficient water is supplied to an agricultural crop or natural vegetation, there should be a single distinct phenological cycle represented in a vegetation index time series (e.g., NDVI and EVI). However, time series data for a vegetation area that repeatedly experiences water stress may include several fluctuations that can be observed in addition to the predominant phenological cycle. This is because the process of experiencing water stress and recovering from it generates small fluctuations in phenological characteristics. Consequently, the value of Hs increases when vegetation experiences several water shortages. Therefore, the Hs could be used as an indicator for water stress history. To test this hypothesis, we analyzed Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data for a natural area in comparison to a nearby sugarcane area in seasonally-dry western Costa Rica. In this presentation we will illustrate the use of spectral entropy to evaluate the vegetative responses of natural vegetation (dry tropical forest) and sugarcane under three different irrigation techniques (center pivot irrigation, drip irrigation and flood irrigation). Through this comparative analysis, the utility of Hs as an indicator will be tested. Furthermore, crop response to the different irrigation methods will be discussed in terms of Hs, NDVI and yield.

Multi-scale clustering of functional data with application to hydraulic gradients in wetlands

USGS Publications Warehouse

Greenwood, Mark C.; Sojda, Richard S.; Sharp, Julia L.; Peck, Rory G.; Rosenberry, Donald O.

2011-01-01

A new set of methods are developed to perform cluster analysis of functions, motivated by a data set consisting of hydraulic gradients at several locations distributed across a wetland complex. The methods build on previous work on clustering of functions, such as Tarpey and Kinateder (2003) and Hitchcock et al. (2007), but explore functions generated from an additive model decomposition (Wood, 2006) of the original time se- ries. Our decomposition targets two aspects of the series, using an adaptive smoother for the trend and circular spline for the diurnal variation in the series. Different measures for comparing locations are discussed, including a method for efficiently clustering time series that are of different lengths using a functional data approach. The complicated nature of these wetlands are highlighted by the shifting group memberships depending on which scale of variation and year of the study are considered.

Clustering Financial Time Series by Network Community Analysis

NASA Astrophysics Data System (ADS)

Piccardi, Carlo; Calatroni, Lisa; Bertoni, Fabio

In this paper, we describe a method for clustering financial time series which is based on community analysis, a recently developed approach for partitioning the nodes of a network (graph). A network with N nodes is associated to the set of N time series. The weight of the link (i, j), which quantifies the similarity between the two corresponding time series, is defined according to a metric based on symbolic time series analysis, which has recently proved effective in the context of financial time series. Then, searching for network communities allows one to identify groups of nodes (and then time series) with strong similarity. A quantitative assessment of the significance of the obtained partition is also provided. The method is applied to two distinct case-studies concerning the US and Italy Stock Exchange, respectively. In the US case, the stability of the partitions over time is also thoroughly investigated. The results favorably compare with those obtained with the standard tools typically used for clustering financial time series, such as the minimal spanning tree and the hierarchical tree.

Multiple-time scales analysis of physiological time series under neural control

NASA Technical Reports Server (NTRS)

Peng, C. K.; Hausdorff, J. M.; Havlin, S.; Mietus, J. E.; Stanley, H. E.; Goldberger, A. L.

1998-01-01

We discuss multiple-time scale properties of neurophysiological control mechanisms, using heart rate and gait regulation as model systems. We find that scaling exponents can be used as prognostic indicators. Furthermore, detection of more subtle degradation of scaling properties may provide a novel early warning system in subjects with a variety of pathologies including those at high risk of sudden death.

Solar Environmental Disturbances

DTIC Science & Technology

2007-11-02

like stars were examined, extending the previous 7–12 year time series to 13–20 years by combining Strömgren b, y photometry from Lowell Observatory...per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and...explanations for how these physical processes affect the production of solar activity, both on short and long time scales. Solar cycle variation

A perturbative approach for enhancing the performance of time series forecasting.

PubMed

de Mattos Neto, Paulo S G; Ferreira, Tiago A E; Lima, Aranildo R; Vasconcelos, Germano C; Cavalcanti, George D C

2017-04-01

This paper proposes a method to perform time series prediction based on perturbation theory. The approach is based on continuously adjusting an initial forecasting model to asymptotically approximate a desired time series model. First, a predictive model generates an initial forecasting for a time series. Second, a residual time series is calculated as the difference between the original time series and the initial forecasting. If that residual series is not white noise, then it can be used to improve the accuracy of the initial model and a new predictive model is adjusted using residual series. The whole process is repeated until convergence or the residual series becomes white noise. The output of the method is then given by summing up the outputs of all trained predictive models in a perturbative sense. To test the method, an experimental investigation was conducted on six real world time series. A comparison was made with six other methods experimented and ten other results found in the literature. Results show that not only the performance of the initial model is significantly improved but also the proposed method outperforms the other results previously published. Copyright © 2017 Elsevier Ltd. All rights reserved.

Extensive mapping of coastal change in Alaska by Landsat time-series analysis, 1972-2013 (Invited)

NASA Astrophysics Data System (ADS)

Macander, M. J.; Swingley, C. S.; Reynolds, J.

2013-12-01

The landscape-scale effects of coastal storms on Alaska's Bering Sea and Gulf of Alaska coasts includes coastal erosion, migration of spits and barrier islands, breaching of coastal lakes and lagoons, and inundation and salt-kill of vegetation. Large changes in coastal storm frequency and intensity are expected due to climate change and reduced sea-ice extent. Storms have a wide range of impacts on carbon fluxes and on fish and wildlife resources, infrastructure siting and operation, and emergency response planning. In areas experiencing moderate to large effects, changes can be mapped by analyzing trends in time series of Landsat imagery from Landsat 1 through Landsat 8. ABR, Inc.--Environmental Research & Services and the Western Alaska Landscape Conservation Cooperative are performing a time-series trend analysis for over 22,000 kilometers of coastline along the Bering Sea and Gulf of Alaska. The archive of Landsat imagery covers the time period 1972-present. For a pilot study area in Kotzebue Sound, we conducted a regression analysis of changes in near-infrared reflectance to identify areas with significant changes in coastal features, 1972-2011. Suitable ice- and cloud-free Landsat imagery was obtained for 28 of the 40 years during the period. The approach captured several coastal changes over the 40-year study period, including coastal erosion exceeding the 60-m pixel resolution of the Multispectral Scanner (MSS) data and migrations of coastal spits and estuarine channels. In addition several lake drainage events were identified, mostly inland from the coastal zone. Analysis of shorter, decadal time periods produced noisier results that were generally consistent with the long-term trend analysis. Unusual conditions at the start or end of the time-series can strongly influence decadal results. Based on these results the study is being scaled up to map coastal change for over 22,000 kilometers of coastline along the Bering Sea and Gulf of Alaska coast. The Landsat imagery archive compiled to perform the coastal change analysis can also be used for other applications including monitoring lake drainage, fire, and vegetation transitions; and characterizing snow persistence patterns and seasonal water level changes. Landsat trend analysis results (1972-2011) for pilot study area in Kotzebue Sound, Alaska.

A 40 Year Time Series of SBUV Observations: the Version 8.6 Processing

NASA Technical Reports Server (NTRS)

McPeters, Richard; Bhartia, P. K.; Flynn, L.

2012-01-01

Under a NASA program to produce long term data records from instruments on multiple satellites (MEaSUREs), data from a series of eight SBUV and SBUV 12 instruments have been reprocessed to create a 40 year long ozone time series. Data from the Nimbus 4 BUV, Nimbus 7 SBUV, and SBUV/2 instruments on NOAA 9, 11, 14, 16, 17, and 18 were used covering the period 1970 to 1972 and 1979 to the present. In past analyses an ozone time series was created from these instruments by adjusting ozone itself, instrument by instrument, for consistency during overlap periods. In the version 8.6 processing adjustments were made to the radiance calibration of each instrument to maintain a consistent calibration over the entire time series. Data for all eight instruments were then reprocessed using the adjusted radiances. Reprocessing is necessary to produce an accurate latitude dependence. Other improvements incorporated in version 8.6 included the use of the ozone cross sections of Brion, Daumont, and Malicet, and the use of a cloud height climatology derived from Aura OMI measurements. The new cross sections have a more accurate temperature dependence than the cross sections previously used. The OMI-based cloud heights account for the penetration of UV into the upper layers of clouds. The consistency of the version 8.6 time series was evaluated by intra-instrument comparisons during overlap periods, comparisons with ground-based instruments, and comparisons with measurements made by instruments on other satellites such as SAGE II and UARS MLS. These comparisons show that for the instruments on NOAA 16, 17 and 18, the instrument calibrations were remarkably stable and consistent from instrument to instrument. The data record from the Nimbus 7 SBUV was also very stable, and SAGE and ground-based comparisons show that the' calibration was consistent with measurements made years laterby the NOAA 16 instrument. The calibrations of the SBUV/2 instruments on NOAA 9, 11, and 14 were more of a problem. The rapidly drifting orbits of these satellites resulted in relative time and altitude dependent differences that are significant. Despite these problems, total column ozone appears to be consistent to better than 1% over the entire time series, while the ozone vertical distribution is consistent to approximately 5%.

Hazard function theory for nonstationary natural hazards

NASA Astrophysics Data System (ADS)

Read, L. K.; Vogel, R. M.

2015-11-01

Impact from natural hazards is a shared global problem that causes tremendous loss of life and property, economic cost, and damage to the environment. Increasingly, many natural processes show evidence of nonstationary behavior including wind speeds, landslides, wildfires, precipitation, streamflow, sea levels, and earthquakes. Traditional probabilistic analysis of natural hazards based on peaks over threshold (POT) generally assumes stationarity in the magnitudes and arrivals of events, i.e. that the probability of exceedance of some critical event is constant through time. Given increasing evidence of trends in natural hazards, new methods are needed to characterize their probabilistic behavior. The well-developed field of hazard function analysis (HFA) is ideally suited to this problem because its primary goal is to describe changes in the exceedance probability of an event over time. HFA is widely used in medicine, manufacturing, actuarial statistics, reliability engineering, economics, and elsewhere. HFA provides a rich theory to relate the natural hazard event series (X) with its failure time series (T), enabling computation of corresponding average return periods, risk and reliabilities associated with nonstationary event series. This work investigates the suitability of HFA to characterize nonstationary natural hazards whose POT magnitudes are assumed to follow the widely applied Generalized Pareto (GP) model. We derive the hazard function for this case and demonstrate how metrics such as reliability and average return period are impacted by nonstationarity and discuss the implications for planning and design. Our theoretical analysis linking hazard event series X, with corresponding failure time series T, should have application to a wide class of natural hazards with rich opportunities for future extensions.

Time series analysis of contaminant transport in the subsurface: applications to conservative tracer and engineered nanomaterials.

PubMed

Bai, Chunmei; Li, Yusong

2014-08-01

Accurately predicting the transport of contaminants in the field is subject to multiple sources of uncertainty due to the variability of geological settings, the complexity of field measurements, and the scarcity of data. Such uncertainties can be amplified when modeling some emerging contaminants, such as engineered nanomaterials, when a fundamental understanding of their fate and transport is lacking. Typical field work includes collecting concentration at a certain location for an extended period of time, or measuring the movement of plume for an extended period time, which would result in a time series of observation data. This work presents an effort to evaluate the possibility of applying time series analysis, particularly, autoregressive integrated moving average (ARIMA) models, to forecast contaminant transport and distribution in the subsurface environment. ARIMA modeling was first assessed in terms of its capability to forecast tracer transport at two field sites, which had different levels of heterogeneity. After that, this study evaluated the applicability of ARIMA modeling to predict the transport of engineered nanomaterials at field sites, including field measured data of nanoscale zero valent iron and (nZVI) and numerically generated data for the transport of nano-fullerene aggregates (nC60). This proof-of-concept effort demonstrates the possibility of applying ARIMA to predict the contaminant transport in the subsurface environment. Like many other statistical models, ARIMA modeling is only descriptive and not explanatory. The limitation and the challenge associated with applying ARIMA modeling to contaminant transport in the subsurface are also discussed. Copyright © 2014 Elsevier B.V. All rights reserved.

Time series analysis of contaminant transport in the subsurface: Applications to conservative tracer and engineered nanomaterials

NASA Astrophysics Data System (ADS)

Bai, Chunmei; Li, Yusong

2014-08-01

Accurately predicting the transport of contaminants in the field is subject to multiple sources of uncertainty due to the variability of geological settings, the complexity of field measurements, and the scarcity of data. Such uncertainties can be amplified when modeling some emerging contaminants, such as engineered nanomaterials, when a fundamental understanding of their fate and transport is lacking. Typical field work includes collecting concentration at a certain location for an extended period of time, or measuring the movement of plume for an extended period time, which would result in a time series of observation data. This work presents an effort to evaluate the possibility of applying time series analysis, particularly, autoregressive integrated moving average (ARIMA) models, to forecast contaminant transport and distribution in the subsurface environment. ARIMA modeling was first assessed in terms of its capability to forecast tracer transport at two field sites, which had different levels of heterogeneity. After that, this study evaluated the applicability of ARIMA modeling to predict the transport of engineered nanomaterials at field sites, including field measured data of nanoscale zero valent iron and (nZVI) and numerically generated data for the transport of nano-fullerene aggregates (nC60). This proof-of-concept effort demonstrates the possibility of applying ARIMA to predict the contaminant transport in the subsurface environment. Like many other statistical models, ARIMA modeling is only descriptive and not explanatory. The limitation and the challenge associated with applying ARIMA modeling to contaminant transport in the subsurface are also discussed.

Time-series analysis of monthly age-specific numbers of newly registered cases of active tuberculosis in Japan from 1998 to 2013.

PubMed

Kohei, Y; Sumi, A; Kobayashi, N

2016-08-01

We investigated the seasonality of age-specific tuberculosis (TB) in Japan. To allow the development of TB control strategies for different age groups we used a time-series analysis, including a spectral analysis and least squares method, to analyse the monthly age-specific numbers of newly registered cases of all forms of active TB in Japan from January 1998 to December 2013. The time-series data are reported in 10-year age groups: 0-9, 10-19, …, 70-79, and ⩾80 years. We defined the contribution ratio of the 1-year cycle, Q 1, as the contribution of the amplitude of a 1-year cycle to the whole amplitude of the time-series data. The Q 1 values in the age groups corresponding to adolescence and middle life (10-39 years) and old age (⩾70 years) were high. The peaks in the active TB epidemics for the ⩾70 years age group occurred in August and September, 1-2 months behind the peaks for the 10-39 years age group (June and July). An active TB epidemic might be attributable to travel by public transport and irregular employment in the 10-39 years age group and immune system suppression by low winter temperatures in the ⩾70 years age group.

Application of information theory methods to food web reconstruction

USGS Publications Warehouse

Moniz, L.J.; Cooch, E.G.; Ellner, S.P.; Nichols, J.D.; Nichols, J.M.

2007-01-01

In this paper we use information theory techniques on time series of abundances to determine the topology of a food web. At the outset, the food web participants (two consumers, two resources) are known; in addition we know that each consumer prefers one of the resources over the other. However, we do not know which consumer prefers which resource, and if this preference is absolute (i.e., whether or not the consumer will consume the non-preferred resource). Although the consumers and resources are identified at the beginning of the experiment, we also provide evidence that the consumers are not resources for each other, and the resources do not consume each other. We do show that there is significant mutual information between resources; the model is seasonally forced and some shared information between resources is expected. Similarly, because the model is seasonally forced, we expect shared information between consumers as they respond to the forcing of the resources. The model that we consider does include noise, and in an effort to demonstrate that these methods may be of some use in other than model data, we show the efficacy of our methods with decreasing time series size; in this particular case we obtain reasonably clear results with a time series length of 400 points. This approaches ecological time series lengths from real systems.

Work-related accidents among the Iranian population: a time series analysis, 2000–2011

PubMed Central

Karimlou, Masoud; Imani, Mehdi; Hosseini, Agha-Fatemeh; Dehnad, Afsaneh; Vahabi, Nasim; Bakhtiyari, Mahmood

2015-01-01

Background Work-related accidents result in human suffering and economic losses and are considered as a major health problem worldwide, especially in the economically developing world. Objectives To introduce seasonal autoregressive moving average (ARIMA) models for time series analysis of work-related accident data for workers insured by the Iranian Social Security Organization (ISSO) between 2000 and 2011. Methods In this retrospective study, all insured people experiencing at least one work-related accident during a 10-year period were included in the analyses. We used Box–Jenkins modeling to develop a time series model of the total number of accidents. Results There was an average of 1476 accidents per month (1476·05±458·77, mean±SD). The final ARIMA (p,d,q) (P,D,Q)s model for fitting to data was: ARIMA(1,1,1)×(0,1,1)12 consisting of the first ordering of the autoregressive, moving average and seasonal moving average parameters with 20·942 mean absolute percentage error (MAPE). Conclusions The final model showed that time series analysis of ARIMA models was useful for forecasting the number of work-related accidents in Iran. In addition, the forecasted number of work-related accidents for 2011 explained the stability of occurrence of these accidents in recent years, indicating a need for preventive occupational health and safety policies such as safety inspection. PMID:26119774

Work-related accidents among the Iranian population: a time series analysis, 2000-2011.

PubMed

Karimlou, Masoud; Salehi, Masoud; Imani, Mehdi; Hosseini, Agha-Fatemeh; Dehnad, Afsaneh; Vahabi, Nasim; Bakhtiyari, Mahmood

2015-01-01

Work-related accidents result in human suffering and economic losses and are considered as a major health problem worldwide, especially in the economically developing world. To introduce seasonal autoregressive moving average (ARIMA) models for time series analysis of work-related accident data for workers insured by the Iranian Social Security Organization (ISSO) between 2000 and 2011. In this retrospective study, all insured people experiencing at least one work-related accident during a 10-year period were included in the analyses. We used Box-Jenkins modeling to develop a time series model of the total number of accidents. There was an average of 1476 accidents per month (1476·05±458·77, mean±SD). The final ARIMA (p,d,q) (P,D,Q)s model for fitting to data was: ARIMA(1,1,1)×(0,1,1)12 consisting of the first ordering of the autoregressive, moving average and seasonal moving average parameters with 20·942 mean absolute percentage error (MAPE). The final model showed that time series analysis of ARIMA models was useful for forecasting the number of work-related accidents in Iran. In addition, the forecasted number of work-related accidents for 2011 explained the stability of occurrence of these accidents in recent years, indicating a need for preventive occupational health and safety policies such as safety inspection.

Between a Map and a Data Rod

NASA Technical Reports Server (NTRS)

Teng, William; Rui, Hualan; Strub, Richard; Vollmer, Bruce

2015-01-01

A Digital Divide has long stood between how NASA and other satellite-derived data are typically archived (time-step arrays or maps) and how hydrology and other point-time series oriented communities prefer to access those data. In essence, the desired method of data access is orthogonal to the way the data are archived. Our approach to bridging the Divide is part of a larger NASA-supported data rods project to enhance access to and use of NASA and other data by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Hydrologic Information System (HIS) and the larger hydrology community. Our main objective was to determine a way to reorganize data that is optimal for these communities. Two related objectives were to optimally reorganize data in a way that (1) is operational and fits in and leverages the existing Goddard Earth Sciences Data and Information Services Center (GES DISC) operational environment and (2) addresses the scaling up of data sets available as time series from those archived at the GES DISC to potentially include those from other Earth Observing System Data and Information System (EOSDIS) data archives. Through several prototype efforts and lessons learned, we arrived at a non-database solution that satisfied our objectivesconstraints. We describe, in this presentation, how we implemented the operational production of pre-generated data rods and, considering the tradeoffs between length of time series (or number of time steps), resources needed, and performance, how we implemented the operational production of on-the-fly (virtual) data rods. For the virtual data rods, we leveraged a number of existing resources, including the NASA Giovanni Cache and NetCDF Operators (NCO) and used data cubes processed in parallel. Our current benchmark performance for virtual generation of data rods is about a years worth of time series for hourly data (9,000 time steps) in 90 seconds. Our approach is a specific implementation of the general optimal strategy of reorganizing data to match the desired means of access. Results from our project have already significantly extended NASA data to the large and important hydrology user community that has been, heretofore, mostly unable to easily access and use NASA data.

Between a Map and a Data Rod

NASA Astrophysics Data System (ADS)

Teng, W. L.; Rui, H.; Strub, R. F.; Vollmer, B.

2015-12-01

A "Digital Divide" has long stood between how NASA and other satellite-derived data are typically archived (time-step arrays or "maps") and how hydrology and other point-time series oriented communities prefer to access those data. In essence, the desired method of data access is orthogonal to the way the data are archived. Our approach to bridging the Divide is part of a larger NASA-supported "data rods" project to enhance access to and use of NASA and other data by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Hydrologic Information System (HIS) and the larger hydrology community. Our main objective was to determine a way to reorganize data that is optimal for these communities. Two related objectives were to optimally reorganize data in a way that (1) is operational and fits in and leverages the existing Goddard Earth Sciences Data and Information Services Center (GES DISC) operational environment and (2) addresses the scaling up of data sets available as time series from those archived at the GES DISC to potentially include those from other Earth Observing System Data and Information System (EOSDIS) data archives. Through several prototype efforts and lessons learned, we arrived at a non-database solution that satisfied our objectives/constraints. We describe, in this presentation, how we implemented the operational production of pre-generated data rods and, considering the tradeoffs between length of time series (or number of time steps), resources needed, and performance, how we implemented the operational production of on-the-fly ("virtual") data rods. For the virtual data rods, we leveraged a number of existing resources, including the NASA Giovanni Cache and NetCDF Operators (NCO) and used data cubes processed in parallel. Our current benchmark performance for virtual generation of data rods is about a year's worth of time series for hourly data (~9,000 time steps) in ~90 seconds. Our approach is a specific implementation of the general optimal strategy of reorganizing data to match the desired means of access. Results from our project have already significantly extended NASA data to the large and important hydrology user community that has been, heretofore, mostly unable to easily access and use NASA data.

Visibility Graph Based Time Series Analysis

PubMed Central

Stephen, Mutua; Gu, Changgui; Yang, Huijie

2015-01-01

Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it’s microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq) and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks. PMID:26571115

Improving Photometry and Stellar Signal Preservation with Pixel-Level Systematic Error Correction

NASA Technical Reports Server (NTRS)

Kolodzijczak, Jeffrey J.; Smith, Jeffrey C.; Jenkins, Jon M.

2013-01-01

The Kepler Mission has demonstrated that excellent stellar photometric performance can be achieved using apertures constructed from optimally selected CCD pixels. The clever methods used to correct for systematic errors, while very successful, still have some limitations in their ability to extract long-term trends in stellar flux. They also leave poorly correlated bias sources, such as drifting moiré pattern, uncorrected. We will illustrate several approaches where applying systematic error correction algorithms to the pixel time series, rather than the co-added raw flux time series, provide significant advantages. Examples include, spatially localized determination of time varying moiré pattern biases, greater sensitivity to radiation-induced pixel sensitivity drops (SPSDs), improved precision of co-trending basis vectors (CBV), and a means of distinguishing the stellar variability from co-trending terms even when they are correlated. For the last item, the approach enables physical interpretation of appropriately scaled coefficients derived in the fit of pixel time series to the CBV as linear combinations of various spatial derivatives of the pixel response function (PRF). We demonstrate that the residuals of a fit of soderived pixel coefficients to various PRF-related components can be deterministically interpreted in terms of physically meaningful quantities, such as the component of the stellar flux time series which is correlated with the CBV, as well as, relative pixel gain, proper motion and parallax. The approach also enables us to parameterize and assess the limiting factors in the uncertainties in these quantities.

40 CFR 205.55-2 - Compliance with standards.

Code of Federal Regulations, 2013 CFR

2013-07-01

... configuration (e.g., L-6, V-8, etc.). (e) Series (i.e., cab design) including but not limited to conventional... of this subpart. (2) [Reserved] (3) At any time following receipt of notice under this section with...

Identification and Inference for Econometric Models

NASA Astrophysics Data System (ADS)

Andrews, Donald W. K.; Stock, James H.

2005-07-01

This volume contains the papers presented in honor of the lifelong achievements of Thomas J. Rothenberg on the occasion of his retirement. The authors of the chapters include many of the leading econometricians of our day, and the chapters address topics of current research significance in econometric theory. The chapters cover four themes: identification and efficient estimation in econometrics, asymptotic approximations to the distributions of econometric estimators and tests, inference involving potentially nonstationary time series, such as processes that might have a unit autoregressive root, and nonparametric and semiparametric inference. Several of the chapters provide overviews and treatments of basic conceptual issues, while others advance our understanding of the properties of existing econometric procedures and/or propose new ones. Specific topics include identification in nonlinear models, inference with weak instruments, tests for nonstationary in time series and panel data, generalized empirical likelihood estimation, and the bootstrap.

Electrical Evaluation of RCA MWS5501D Random Access Memory, Volume 2, Appendix a

NASA Technical Reports Server (NTRS)

Klute, A.

1979-01-01

The electrical characterization and qualification test results are presented for the RCA MWS5001D random access memory. The tests included functional tests, AC and DC parametric tests, AC parametric worst-case pattern selection test, determination of worst-case transition for setup and hold times, and a series of schmoo plots. The address access time, address readout time, the data hold time, and the data setup time are some of the results surveyed.

An Anomaly in the Inglis-Teller Limits of the C VI Lyman and Balmer Series in Laser-Produced Plasmas

NASA Astrophysics Data System (ADS)

Elton, R.; Iglesias, E.; Griem, H.; Weaver, J.; Pien, G.; Mancini, R.

2002-11-01

Soft x-ray spectra from thin carbon layers heated by the OMEGA and NIKE lasers have been obtained with both spherical and planar targets, respectively, using a flat-field grazing incidence spectrograph equipped with a gated microchannel plate for temporal resolution. In both experiments, late-time (recombining) hydrogenic C VI spectra show an n-to-1 Lyman spectral series blending with the continuum at n=4, contrary to n=9 in the n-to-2 Balmer series. It appears unlikely that plasma inhomogeneities are the sole cause of this anomaly, given the difference in the experimental configurations. Other explanations for the line-to-continuum merging (other than the usual Stark-broadened Inglis-Teller effect) under consideration include non-thermal Doppler broadening, deviations from statistical sublevel population distributions, and opacity effects. Collisional-radiative and hydrodynamic modeling, including cascades, is employed to further understand this phenomenon.

High gradient lens for charged particle beam

DOEpatents

Chen, Yu-Jiuan

2014-04-29

Methods and devices enable shaping of a charged particle beam. A dynamically adjustable electric lens includes a series of alternating a series of alternating layers of insulators and conductors with a hollow center. The series of alternating layers when stacked together form a high gradient insulator (HGI) tube to allow propagation of the charged particle beam through the hollow center of the HGI tube. A plurality of transmission lines are connected to a plurality of sections of the HGI tube, and one or more voltage sources are provided to supply an adjustable voltage value to each transmission line of the plurality of transmission lines. By changing the voltage values supplied to each section of the HGI tube, any desired electric field can be established across the HGI tube. This way various functionalities including focusing, defocusing, acceleration, deceleration, intensity modulation and others can be effectuated on a time varying basis.

Quantifying memory in complex physiological time-series.

PubMed

Shirazi, Amir H; Raoufy, Mohammad R; Ebadi, Haleh; De Rui, Michele; Schiff, Sami; Mazloom, Roham; Hajizadeh, Sohrab; Gharibzadeh, Shahriar; Dehpour, Ahmad R; Amodio, Piero; Jafari, G Reza; Montagnese, Sara; Mani, Ali R

2013-01-01

In a time-series, memory is a statistical feature that lasts for a period of time and distinguishes the time-series from a random, or memory-less, process. In the present study, the concept of "memory length" was used to define the time period, or scale over which rare events within a physiological time-series do not appear randomly. The method is based on inverse statistical analysis and provides empiric evidence that rare fluctuations in cardio-respiratory time-series are 'forgotten' quickly in healthy subjects while the memory for such events is significantly prolonged in pathological conditions such as asthma (respiratory time-series) and liver cirrhosis (heart-beat time-series). The memory length was significantly higher in patients with uncontrolled asthma compared to healthy volunteers. Likewise, it was significantly higher in patients with decompensated cirrhosis compared to those with compensated cirrhosis and healthy volunteers. We also observed that the cardio-respiratory system has simple low order dynamics and short memory around its average, and high order dynamics around rare fluctuations.

Quantifying Memory in Complex Physiological Time-Series

PubMed Central

Shirazi, Amir H.; Raoufy, Mohammad R.; Ebadi, Haleh; De Rui, Michele; Schiff, Sami; Mazloom, Roham; Hajizadeh, Sohrab; Gharibzadeh, Shahriar; Dehpour, Ahmad R.; Amodio, Piero; Jafari, G. Reza; Montagnese, Sara; Mani, Ali R.

2013-01-01

In a time-series, memory is a statistical feature that lasts for a period of time and distinguishes the time-series from a random, or memory-less, process. In the present study, the concept of “memory length” was used to define the time period, or scale over which rare events within a physiological time-series do not appear randomly. The method is based on inverse statistical analysis and provides empiric evidence that rare fluctuations in cardio-respiratory time-series are ‘forgotten’ quickly in healthy subjects while the memory for such events is significantly prolonged in pathological conditions such as asthma (respiratory time-series) and liver cirrhosis (heart-beat time-series). The memory length was significantly higher in patients with uncontrolled asthma compared to healthy volunteers. Likewise, it was significantly higher in patients with decompensated cirrhosis compared to those with compensated cirrhosis and healthy volunteers. We also observed that the cardio-respiratory system has simple low order dynamics and short memory around its average, and high order dynamics around rare fluctuations. PMID:24039811

Scale-dependent intrinsic entropies of complex time series.

PubMed

Yeh, Jia-Rong; Peng, Chung-Kang; Huang, Norden E

2016-04-13

Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal's complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease. © 2016 The Author(s).

Using in-situ Glider Data to Improve the Interpretation of Time-Series Data in the San Pedro Channel

NASA Astrophysics Data System (ADS)

Teel, E.; Liu, X.; Seegers, B. N.; Ragan, M. A.; Jones, B. H.; Levine, N. M.

2016-02-01

Oceanic time-series have provided insight into biological, physical, and chemical processes and how these processes change over time. However, time-series data collected near coastal zones have not been used as broadly because of regional features that may prevent extrapolation of local results. Though these sites are inherently more affected by local processes, broadening the application of coastal data is crucial for improved modeling of processes such as total carbon drawdown and the development of oxygen minimum zones. Slocum gliders were deployed off the coast of Los Angeles from February to July of 2013 and 2014 providing high temporal and spatial resolution data of the San Pedro Channel (SPC), which includes the San Pedro Ocean Time Series (SPOT). The data were collapsed onto a standardized grid and primary and secondary characteristics of glider profiles were analyzed by principal component analysis to determine the processes impacting SPC and SPOT. The data fell into four categories: active upwelling, offshore intrusion, subsurface bloom, and surface bloom. Waters across the SPC were most similar to offshore water masses, even during the upwelling season when near-shore blooms are commonly observed. The SPOT site was found to be representative of the SPC 86% of the time, suggesting that the findings from SPOT are applicable for the entire SPC. Subsurface blooms were common in both years with co-located chlorophyll and particle maxima, and results suggested that these subsurface blooms contribute significantly to the local primary production. Satellite estimation of integrated chlorophyll was poor, possibly due to the prevalence of subsurface blooms and shallow optical depths during surface blooms. These results indicate that high resolution in-situ glider deployments can be used to determine the spatial domain of coastal time-series data, allowing for broader application of these datasets and greater integration into modeling efforts.

A non linear analysis of human gait time series based on multifractal analysis and cross correlations

NASA Astrophysics Data System (ADS)

Muñoz-Diosdado, A.

2005-01-01

We analyzed databases with gait time series of adults and persons with Parkinson, Huntington and amyotrophic lateral sclerosis (ALS) diseases. We obtained the staircase graphs of accumulated events that can be bounded by a straight line whose slope can be used to distinguish between gait time series from healthy and ill persons. The global Hurst exponent of these series do not show tendencies, we intend that this is because some gait time series have monofractal behavior and others have multifractal behavior so they cannot be characterized with a single Hurst exponent. We calculated the multifractal spectra, obtained the spectra width and found that the spectra of the healthy young persons are almost monofractal. The spectra of ill persons are wider than the spectra of healthy persons. In opposition to the interbeat time series where the pathology implies loss of multifractality, in the gait time series the multifractal behavior emerges with the pathology. Data were collected from healthy and ill subjects as they walked in a roughly circular path and they have sensors in both feet, so we have one time series for the left foot and other for the right foot. First, we analyzed these time series separately, and then we compared both results, with direct comparison and with a cross correlation analysis. We tried to find differences in both time series that can be used as indicators of equilibrium problems.

The examination of headache activity using time-series research designs.

PubMed

Houle, Timothy T; Remble, Thomas A; Houle, Thomas A

2005-05-01

The majority of research conducted on headache has utilized cross-sectional designs which preclude the examination of dynamic factors and principally rely on group-level effects. The present article describes the application of an individual-oriented process model using time-series analytical techniques. The blending of a time-series approach with an interactive process model allows consideration of the relationships of intra-individual dynamic processes, while not precluding the researcher to examine inter-individual differences. The authors explore the nature of time-series data and present two necessary assumptions underlying the time-series approach. The concept of shock and its contribution to headache activity is also presented. The time-series approach is not without its problems and two such problems are specifically reported: autocorrelation and the distribution of daily observations. The article concludes with the presentation of several analytical techniques suited to examine the time-series interactive process model.

Long-range correlations in time series generated by time-fractional diffusion: A numerical study

NASA Astrophysics Data System (ADS)

Barbieri, Davide; Vivoli, Alessandro

2005-09-01

Time series models showing power law tails in autocorrelation functions are common in econometrics. A special non-Markovian model for such kind of time series is provided by the random walk introduced by Gorenflo et al. as a discretization of time fractional diffusion. The time series so obtained are analyzed here from a numerical point of view in terms of autocorrelations and covariance matrices.

Insights on correlation dimension from dynamics mapping of three experimental nonlinear laser systems.

PubMed

McMahon, Christopher J; Toomey, Joshua P; Kane, Deb M

2017-01-01

We have analysed large data sets consisting of tens of thousands of time series from three Type B laser systems: a semiconductor laser in a photonic integrated chip, a semiconductor laser subject to optical feedback from a long free-space-external-cavity, and a solid-state laser subject to optical injection from a master laser. The lasers can deliver either constant, periodic, pulsed, or chaotic outputs when parameters such as the injection current and the level of external perturbation are varied. The systems represent examples of experimental nonlinear systems more generally and cover a broad range of complexity including systematically varying complexity in some regions. In this work we have introduced a new procedure for semi-automatically interrogating experimental laser system output power time series to calculate the correlation dimension (CD) using the commonly adopted Grassberger-Proccacia algorithm. The new CD procedure is called the 'minimum gradient detection algorithm'. A value of minimum gradient is returned for all time series in a data set. In some cases this can be identified as a CD, with uncertainty. Applying the new 'minimum gradient detection algorithm' CD procedure, we obtained robust measurements of the correlation dimension for many of the time series measured from each laser system. By mapping the results across an extended parameter space for operation of each laser system, we were able to confidently identify regions of low CD (CD < 3) and assign these robust values for the correlation dimension. However, in all three laser systems, we were not able to measure the correlation dimension at all parts of the parameter space. Nevertheless, by mapping the staged progress of the algorithm, we were able to broadly classify the dynamical output of the lasers at all parts of their respective parameter spaces. For two of the laser systems this included displaying regions of high-complexity chaos and dynamic noise. These high-complexity regions are differentiated from regions where the time series are dominated by technical noise. This is the first time such differentiation has been achieved using a CD analysis approach. More can be known of the CD for a system when it is interrogated in a mapping context, than from calculations using isolated time series. This has been shown for three laser systems and the approach is expected to be useful in other areas of nonlinear science where large data sets are available and need to be semi-automatically analysed to provide real dimensional information about the complex dynamics. The CD/minimum gradient algorithm measure provides additional information that complements other measures of complexity and relative complexity, such as the permutation entropy; and conventional physical measurements.

Insights on correlation dimension from dynamics mapping of three experimental nonlinear laser systems

PubMed Central

McMahon, Christopher J.; Toomey, Joshua P.

2017-01-01

Background We have analysed large data sets consisting of tens of thousands of time series from three Type B laser systems: a semiconductor laser in a photonic integrated chip, a semiconductor laser subject to optical feedback from a long free-space-external-cavity, and a solid-state laser subject to optical injection from a master laser. The lasers can deliver either constant, periodic, pulsed, or chaotic outputs when parameters such as the injection current and the level of external perturbation are varied. The systems represent examples of experimental nonlinear systems more generally and cover a broad range of complexity including systematically varying complexity in some regions. Methods In this work we have introduced a new procedure for semi-automatically interrogating experimental laser system output power time series to calculate the correlation dimension (CD) using the commonly adopted Grassberger-Proccacia algorithm. The new CD procedure is called the ‘minimum gradient detection algorithm’. A value of minimum gradient is returned for all time series in a data set. In some cases this can be identified as a CD, with uncertainty. Findings Applying the new ‘minimum gradient detection algorithm’ CD procedure, we obtained robust measurements of the correlation dimension for many of the time series measured from each laser system. By mapping the results across an extended parameter space for operation of each laser system, we were able to confidently identify regions of low CD (CD < 3) and assign these robust values for the correlation dimension. However, in all three laser systems, we were not able to measure the correlation dimension at all parts of the parameter space. Nevertheless, by mapping the staged progress of the algorithm, we were able to broadly classify the dynamical output of the lasers at all parts of their respective parameter spaces. For two of the laser systems this included displaying regions of high-complexity chaos and dynamic noise. These high-complexity regions are differentiated from regions where the time series are dominated by technical noise. This is the first time such differentiation has been achieved using a CD analysis approach. Conclusions More can be known of the CD for a system when it is interrogated in a mapping context, than from calculations using isolated time series. This has been shown for three laser systems and the approach is expected to be useful in other areas of nonlinear science where large data sets are available and need to be semi-automatically analysed to provide real dimensional information about the complex dynamics. The CD/minimum gradient algorithm measure provides additional information that complements other measures of complexity and relative complexity, such as the permutation entropy; and conventional physical measurements. PMID:28837602

Improving estimates of ecosystem metabolism by reducing effects of tidal advection on dissolved oxygen time series

EPA Science Inventory

In aquatic systems, time series of dissolved oxygen (DO) have been used to compute estimates of ecosystem metabolism. Central to this open-water method is the assumption that the DO time series is a Lagrangian specification of the flow field. However, most DO time series are coll...

76 FR 28897 - Magnuson-Stevens Act Provisions; Fisheries Off West Coast States; Pacific Coast Groundfish...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-19

... time series became closer (while depletion at the end of the time series became more divergent); (4) the agreement in the recruitment time series was much improved; (5) recruitment deviations in log space showed much closer agreement; and (6) the fishing intensity time series showed much closer...

75 FR 4570 - Government-Owned Inventions; Availability for Licensing

Federal Register 2010, 2011, 2012, 2013, 2014

2010-01-28

... applications. Signal-to-Noise Enhancement in Imaging Applications Using a Time-Series of Images Description of... applications that use a time-series of images. In one embodiment of the invention, a time-series of images is... Imaging Applications Using a Time-Series of Images'' (HHS Reference No. E-292- 2009/0-US-01). Related...

Nonlinear analysis and dynamic structure in the energy market

NASA Astrophysics Data System (ADS)

Aghababa, Hajar

This research assesses the dynamic structure of the energy sector of the aggregate economy in the context of nonlinear mechanisms. Earlier studies have focused mainly on the price of the energy products when detecting nonlinearities in time series data of the energy market, and there is little mention of the production side of the market. Moreover, there is a lack of exploration about the implication of high dimensionality and time aggregation when analyzing the market's fundamentals. This research will address these gaps by including the quantity side of the market in addition to the price and by systematically incorporating various frequencies for sample sizes in three essays. The goal of this research is to provide an inclusive and exhaustive examination of the dynamics in the energy markets. The first essay begins with the application of statistical techniques, and it incorporates the most well-known univariate tests for nonlinearity with distinct power functions over alternatives and tests different null hypotheses. It utilizes the daily spot price observations on five major products in the energy market. The results suggest that the time series daily spot prices of the energy products are highly nonlinear in their nature. They demonstrate apparent evidence of general nonlinear serial dependence in each individual series, as well as nonlinearity in the first, second, and third moments of the series. The second essay examines the underlying mechanism of crude oil production and identifies the nonlinear structure of the production market by utilizing various monthly time series observations of crude oil production: the U.S. field, Organization of the Petroleum Exporting Countries (OPEC), non-OPEC, and the world production of crude oil. The finding implies that the time series data of the U.S. field, OPEC, and the world production of crude oil exhibit deep nonlinearity in their structure and are generated by nonlinear mechanisms. However, the dynamics of the non-OPEC production time series data does not reveal signs of nonlinearity. The third essay explores nonlinear structure in the case of high dimensionality of the observations, different frequencies of sample sizes, and division of the samples into sub-samples. It systematically examines the robustness of the inference methods at various levels of time aggregation by employing daily spot prices on crude oil for 26 years as well as monthly spot price index on crude oil for 41 years. The daily and monthly samples are divided into sub-samples as well. All the tests detect strong evidence of nonlinear structure in the daily spot price of crude oil; whereas in monthly observations the evidence of nonlinear dependence is less dramatic, indicating that the nonlinear serial dependence will not be as intense when the time aggregation increase in time series observations.

78 FR 15385 - Self-Regulatory Organizations; NASDAQ OMX BX, Inc.; Notice of Filing of Proposed Rule Change To...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-03-11

... Series, any adjusted option series, and any option series until the time to expiration for such series is... existing requirement may at times discourage liquidity in particular options series because a market maker... the option is subject to the Price/Time execution algorithm, the Directed Market Maker shall receive...

A novel water quality data analysis framework based on time-series data mining.

PubMed

Deng, Weihui; Wang, Guoyin

2017-07-01

The rapid development of time-series data mining provides an emerging method for water resource management research. In this paper, based on the time-series data mining methodology, we propose a novel and general analysis framework for water quality time-series data. It consists of two parts: implementation components and common tasks of time-series data mining in water quality data. In the first part, we propose to granulate the time series into several two-dimensional normal clouds and calculate the similarities in the granulated level. On the basis of the similarity matrix, the similarity search, anomaly detection, and pattern discovery tasks in the water quality time-series instance dataset can be easily implemented in the second part. We present a case study of this analysis framework on weekly Dissolve Oxygen time-series data collected from five monitoring stations on the upper reaches of Yangtze River, China. It discovered the relationship of water quality in the mainstream and tributary as well as the main changing patterns of DO. The experimental results show that the proposed analysis framework is a feasible and efficient method to mine the hidden and valuable knowledge from water quality historical time-series data. Copyright © 2017 Elsevier Ltd. All rights reserved.

Atmospheric extinction in simulation tools for solar tower plants

NASA Astrophysics Data System (ADS)

Hanrieder, Natalie; Wilbert, Stefan; Schroedter-Homscheidt, Marion; Schnell, Franziska; Guevara, Diana Mancera; Buck, Reiner; Giuliano, Stefano; Pitz-Paal, Robert

2017-06-01

Atmospheric extinction causes significant radiation losses between the heliostat field and the receiver in a solar tower plants. These losses vary with site and time. State of the art is that in ray-tracing and plant optimization tools, atmospheric extinction is included by choosing between few constant standard atmospheric conditions. Even though some tools allow the consideration of site and time dependent extinction data, such data sets are nearly never available. This paper summarizes and compares the most common model equations implemented in several ray-tracing tools. There are already several methods developed and published to measure extinction on-site. An overview of the existing methods is also given here. Ray-tracing simulations of one exemplary tower plant at the Plataforma Solar de Almería (PSA) are presented to estimate the plant yield deviations between simulations using standard model equations instead of extinction time series. For PSA, the effect of atmospheric extinction accounts for losses between 1.6 and 7 %. This range is caused by considering overload dumping or not. Applying standard clear or hazy model equations instead of extinction time series lead to an underestimation of the annual plant yield at PSA. The discussion of the effect of extinction in tower plants has to include overload dumping. Situations in which overload dumping occurs are mostly connected to high radiation levels and low atmospheric extinction. Therefore it can be recommended that project developers should consider site and time dependent extinction data especially on hazy sites. A reduced uncertainty of the plant yield prediction can significantly reduce costs due to smaller risk margins for financing and EPCs. The generation of extinction data for several locations in form of representative yearly time series or geographical maps should be further elaborated.

Analysis of Parametric Adaptive Signal Detection with Applications to Radars and Hyperspectral Imaging

DTIC Science & Technology

2010-02-01

98 8.4.5 Training Screening ............................. .................................................................99 8.5 Experimental...associated with the proposed parametric model. Several im- portant issues are discussed, including model order selection, training screening , and time...parameters associated with the NS-AR model. In addition, we develop model order selection, training screening , and time-series based whitening and

Time and Learning: Scheduling for Success. Hot Topics Series.

ERIC Educational Resources Information Center

Kennedy, Robert L., Ed.; Witcher, Ann E., Ed.

This book provides information for educators considering ways to make the best use of time available for learning. Twenty-one articles are divided into 5 chapters. Chapter 1: "How Can We Make the Most of the School Day?" includes an overview and 6 articles: (1) "Block Scheduling" (Karen Irmsher); (2)"The Hybrid Schedule: Scheduling to the…

Comprehensive Bibliography of Pakistan Archaeology: Paleolithic to Historic Times. South Asia Series, Occasional Paper No. 24.

ERIC Educational Resources Information Center

King, Denise E.

The comprehensive bibliography is a compilation of twentieth century documents about Pakistan prehistory from Paleolithic times to the arrival of the Greeks in approximately 330 B.C., also includes some of the major archaeological studies in adjacent countries which have a bearing on the interpretation and comparative analysis of Pakistan…

Geophysical parameters from the analysis of laser ranging to Starlette

NASA Technical Reports Server (NTRS)

Schutz, B. E.; Shum, C. K.; Tapley, B. D.

1991-01-01

The University of Texas Center for Space Research (UT/CSR) research efforts covering the time period from August 1, 1990 through January 31, 1991 have concentrated on the following areas: (1) Laser Data Processing (more than 15 years of Starlette data (1975-90) have been processed and cataloged); (2) Seasonal Variation of Zonal Tides (observed Starlette time series has been compared with meteorological data-derived time series); (3) Ocean Tide Solutions . (error analysis has been performed using Starlette and other tide solutions); and (4) Lunar Deceleration (formulation to compute theoretical lunar deceleration has been verified and applied to several tidal solutions). Concise descriptions of research achievement for each of the above areas are given. Copies of abstracts for some of the publications and conference presentations are included in the appendices.

Mixture Hidden Markov Models in Finance Research

NASA Astrophysics Data System (ADS)

Dias, José G.; Vermunt, Jeroen K.; Ramos, Sofia

Finite mixture models have proven to be a powerful framework whenever unobserved heterogeneity cannot be ignored. We introduce in finance research the Mixture Hidden Markov Model (MHMM) that takes into account time and space heterogeneity simultaneously. This approach is flexible in the sense that it can deal with the specific features of financial time series data, such as asymmetry, kurtosis, and unobserved heterogeneity. This methodology is applied to model simultaneously 12 time series of Asian stock markets indexes. Because we selected a heterogeneous sample of countries including both developed and emerging countries, we expect that heterogeneity in market returns due to country idiosyncrasies will show up in the results. The best fitting model was the one with two clusters at country level with different dynamics between the two regimes.

TIDE TOOL: Open-Source Sea-Level Monitoring Software for Tsunami Warning Systems

NASA Astrophysics Data System (ADS)

Weinstein, S. A.; Kong, L. S.; Becker, N. C.; Wang, D.

2012-12-01

A tsunami warning center (TWC) typically decides to issue a tsunami warning bulletin when initial estimates of earthquake source parameters suggest it may be capable of generating a tsunami. A TWC, however, relies on sea-level data to provide prima facie evidence for the existence or non-existence of destructive tsunami waves and to constrain tsunami wave height forecast models. In the aftermath of the 2004 Sumatra disaster, the International Tsunami Information Center asked the Pacific Tsunami Warning Center (PTWC) to develop a platform-independent, easy-to-use software package to give nascent TWCs the ability to process WMO Global Telecommunications System (GTS) sea-level messages and to analyze the resulting sea-level curves (marigrams). In response PTWC developed TIDE TOOL that has since steadily grown in sophistication to become PTWC's operational sea-level processing system. TIDE TOOL has two main parts: a decoder that reads GTS sea-level message logs, and a graphical user interface (GUI) written in the open-source platform-independent graphical toolkit scripting language Tcl/Tk. This GUI consists of dynamic map-based clients that allow the user to select and analyze a single station or groups of stations by displaying their marigams in strip-chart or screen-tiled forms. TIDE TOOL also includes detail maps of each station to show each station's geographical context and reverse tsunami travel time contours to each station. TIDE TOOL can also be coupled to the GEOWARE™ TTT program to plot tsunami travel times and to indicate the expected tsunami arrival time on the marigrams. Because sea-level messages are structured in a rich variety of formats TIDE TOOL includes a metadata file, COMP_META, that contains all of the information needed by TIDE TOOL to decode sea-level data as well as basic information such as the geographical coordinates of each station. TIDE TOOL can therefore continuously decode theses sea-level messages in real-time and display the time-series data in the GUI as well. This GUI also includes mouse-clickable functions such as zooming or expanding the time-series display, measuring tsunami signal characteristics (arrival time, wave period and amplitude, etc.), and removing the tide signal from the time-series data. De-tiding of the time series is necessary to obtain accurate measurements of tsunami wave parameters and to maintain accurate historical tsunami databases. With TIDE TOOL, de-tiding is accomplished with a set of tide harmonic coefficients routinely computed and updated at PTWC for many of the stations in PTWC's inventory (~570). PTWC also uses the decoded time series files (previous 3-5 days' worth) to compute on-the-fly tide coefficients. The latter is useful in cases where the station is new and a long-term stable set of tide coefficients are not available or cannot be easily obtained due to various non-astronomical effects. The international tsunami warning system is coordinated globally by the UNESCO IOC, and a number of countries in the Pacific and Indian Ocean, and Caribbean depend on Tide Tool to monitor tsunamis in real time.

Fiber laser platform for highest flexibility and reliability in industrial femtosecond micromachining: TruMicro Series 2000

NASA Astrophysics Data System (ADS)

Jansen, Florian; Kanal, Florian; Kahmann, Max; Tan, Chuong; Diekamp, Holger; Scelle, Raphael; Budnicki, Aleksander; Sutter, Dirk

2018-02-01

In this work we present an ultrafast laser system distinguished by its industry-ready reliability and its outstanding flexibility that allows for real-time process-inherent parameter. The robust system design and linear amplifier architecture make the all-fiber series TruMicro 2000 ideally suited for passive coupling to hollow-core delivery fibers. In addition to details on the laser system itself, various application examples are shown, including welding of different glasses and ablation of silicon carbide and silicon.

Practical aspects of telehealth: financial considerations.

PubMed

Loh, P K; Sabesan, S; Allen, D; Caldwell, P; Mozer, R; Komesaroff, P A; Talman, P; Williams, M; Shaheen, N; Grabinski, O; Withnall, D

2013-07-01

The second in a series of articles about the practical aspects of telehealth, this paper includes information and a case history on the cost-benefits for patients and practitioners using telehealth. The case history demonstrates that telehealth can save travel time for patients, carers and specialists, and can reduce out-of-pocket expenses. The practical aspects of telehealth article series considers the contextual, clinical, technical and ethical components of online video consultations. © 2013 The Authors; Internal Medicine Journal © 2013 Royal Australasian College of Physicians.

KSC-2011-4343

NASA Image and Video Library

2011-06-10

CAPE CANAVERAL, Fla. -- At NASA Kennedy Space Center's Visitor Complex, Star Trek memorabilia is being displayed this summer through Sept. 5. Appearing for the first time in the southeast, "Star Trek: The Exhibition" is an interactive exhibit of authentic Star Trek artifacts from the past 40 years, featuring one-of-a-kind costumes, props and filming models from every Star Trek television series and feature film, including a recreation of the U.S.S. Enterprise Bridge from the original series. Photo credit: NASA/Jack Pfaller

KSC-2011-4336

NASA Image and Video Library

2011-06-10

CAPE CANAVERAL, Fla. -- At NASA Kennedy Space Center's Visitor Complex, Star Trek memorabilia is being displayed this summer through Sept. 5. Appearing for the first time in the southeast, "Star Trek: The Exhibition" is an interactive exhibit of authentic Star Trek artifacts from the past 40 years, featuring one-of-a-kind costumes, props and filming models from every Star Trek television series and feature film, including a recreation of the U.S.S. Enterprise Bridge from the original series. Photo credit: NASA/Jack Pfaller

KSC-2011-4341

NASA Image and Video Library

2011-06-10

CAPE CANAVERAL, Fla. -- At NASA Kennedy Space Center's Visitor Complex, Star Trek memorabilia is being displayed this summer through Sept. 5. Appearing for the first time in the southeast, "Star Trek: The Exhibition" is an interactive exhibit of authentic Star Trek artifacts from the past 40 years, featuring one-of-a-kind costumes, props and filming models from every Star Trek television series and feature film, including a recreation of the U.S.S. Enterprise Bridge from the original series. Photo credit: NASA/Jack Pfaller

KSC-2011-4338

NASA Image and Video Library

2011-06-10

CAPE CANAVERAL, Fla. -- At NASA Kennedy Space Center's Visitor Complex, Star Trek memorabilia is being displayed this summer through Sept. 5. Appearing for the first time in the southeast, "Star Trek: The Exhibition" is an interactive exhibit of authentic Star Trek artifacts from the past 40 years, featuring one-of-a-kind costumes, props and filming models from every Star Trek television series and feature film, including a recreation of the U.S.S. Enterprise Bridge from the original series. Photo credit: NASA/Jack Pfaller