Permutations and time series analysis.
Cánovas, Jose S; Guillamón, Antonio
2009-12-01
The main aim of this paper is to show how the use of permutations can be useful in the study of time series analysis. In particular, we introduce a test for checking the independence of a time series which is based on the number of admissible permutations on it. The main improvement in our tests is that we are able to give a theoretical distribution for independent time series. PMID:20059199
NASA Astrophysics Data System (ADS)
Kosmidis, Kosmas; Kalampokis, Alkiviadis; Argyrakis, Panos
2006-10-01
We use the detrended fluctuation analysis (DFA) and the Grassberger-Proccacia analysis (GP) methods in order to study language characteristics. Despite that we construct our signals using only word lengths or word frequencies, excluding in this way huge amount of information from language, the application of GP analysis indicates that linguistic signals may be considered as the manifestation of a complex system of high dimensionality, different from random signals or systems of low dimensionality such as the Earth climate. The DFA method is additionally able to distinguish a natural language signal from a computer code signal. This last result may be useful in the field of cryptography.
NASA Astrophysics Data System (ADS)
Allan, Alasdair
2014-06-01
FROG performs time series analysis and display. It provides a simple user interface for astronomers wanting to do time-domain astrophysics but still offers the powerful features found in packages such as PERIOD (ascl:1406.005). FROG includes a number of tools for manipulation of time series. Among other things, the user can combine individual time series, detrend series (multiple methods) and perform basic arithmetic functions. The data can also be exported directly into the TOPCAT (ascl:1101.010) application for further manipulation if needed.
NASA Astrophysics Data System (ADS)
Visser, M.; Batten, S.; Becker, G.; Bot, P.; Colijn, F.; Damm, P.; Danielssen, D.; van den Eynde, D.; Føyn, L.; Frohse, A.; Groeneveld, G.; Laane, R.; van Raaphorst, W.; Radach, G.; Schultz, H.; Sündermann, J.
1996-09-01
In this study an overview is given of the time series analysis of monthly mean data of physical, chemical and biological parameters. The time series are available at eight locations on the Northwest European Shelf. The integrated evaluation of those time series gives the opportunity to look for connections between the different parts of the shelf. Temperature and salinity seem to be externally forced. For the nutrients and biological parameters the local forcing is dominating the time series. It is concluded that there are areas that are comparable to each other (freshwater dominated boxes along the Belgian and Dutch coasts and German Bight; Atlantic dominated boxes in the English Channel and off the Scottish coast), although significant cross-correlations are hardly found. The Irish Sea can be regarded as a separate ecosystem.
Introduction to Time Series Analysis
NASA Technical Reports Server (NTRS)
Hardin, J. C.
1986-01-01
The field of time series analysis is explored from its logical foundations to the most modern data analysis techniques. The presentation is developed, as far as possible, for continuous data, so that the inevitable use of discrete mathematics is postponed until the reader has gained some familiarity with the concepts. The monograph seeks to provide the reader with both the theoretical overview and the practical details necessary to correctly apply the full range of these powerful techniques. In addition, the last chapter introduces many specialized areas where research is currently in progress.
Analysis of time series from stochastic processes
Gradisek; Siegert; Friedrich; Grabec
2000-09-01
Analysis of time series from stochastic processes governed by a Langevin equation is discussed. Several applications for the analysis are proposed based on estimates of drift and diffusion coefficients of the Fokker-Planck equation. The coefficients are estimated directly from a time series. The applications are illustrated by examples employing various synthetic time series and experimental time series from metal cutting. PMID:11088809
Hydrodynamic analysis of time series
NASA Astrophysics Data System (ADS)
Suciu, N.; Vamos, C.; Vereecken, H.; Vanderborght, J.
2003-04-01
It was proved that balance equations for systems with corpuscular structure can be derived if a kinematic description by piece-wise analytic functions is available [1]. For example, the hydrodynamic equations for one-dimensional systems of inelastic particles, derived in [2], were used to prove the inconsistency of the Fourier law of heat with the microscopic structure of the system. The hydrodynamic description is also possible for single particle systems. In this case, averages of physical quantities associated with the particle, over a space-time window, generalizing the usual ``moving averages'' which are performed on time intervals only, were shown to be almost everywhere continuous space-time functions. Moreover, they obey balance partial differential equations (continuity equation for the 'concentration', Navier-Stokes equation, a. s. o.) [3]. Time series can be interpreted as trajectories in the space of the recorded parameter. Their hydrodynamic interpretation is expected to enable deterministic predictions, when closure relations can be obtained for the balance equations. For the time being, a first result is the estimation of the probability density for the occurrence of a given parameter value, by the normalized concentration field from the hydrodynamic description. The method is illustrated by hydrodynamic analysis of three types of time series: white noise, stock prices from financial markets and groundwater levels recorded at Krauthausen experimental field of Forschungszentrum Jülich (Germany). [1] C. Vamoş, A. Georgescu, N. Suciu, I. Turcu, Physica A 227, 81-92, 1996. [2] C. Vamoş, N. Suciu, A. Georgescu, Phys. Rev E 55, 5, 6277-6280, 1997. [3] C. Vamoş, N. Suciu, W. Blaj, Physica A, 287, 461-467, 2000.
Nonlinear Analysis of Surface EMG Time Series
NASA Astrophysics Data System (ADS)
Zurcher, Ulrich; Kaufman, Miron; Sung, Paul
2004-04-01
Applications of nonlinear analysis of surface electromyography time series of patients with and without low back pain are presented. Limitations of the standard methods based on the power spectrum are discussed.
Entropic Analysis of Electromyography Time Series
NASA Astrophysics Data System (ADS)
Kaufman, Miron; Sung, Paul
2005-03-01
We are in the process of assessing the effectiveness of fractal and entropic measures for the diagnostic of low back pain from surface electromyography (EMG) time series. Surface electromyography (EMG) is used to assess patients with low back pain. In a typical EMG measurement, the voltage is measured every millisecond. We observed back muscle fatiguing during one minute, which results in a time series with 60,000 entries. We characterize the complexity of time series by computing the Shannon entropy time dependence. The analysis of the time series from different relevant muscles from healthy and low back pain (LBP) individuals provides evidence that the level of variability of back muscle activities is much larger for healthy individuals than for individuals with LBP. In general the time dependence of the entropy shows a crossover from a diffusive regime to a regime characterized by long time correlations (self organization) at about 0.01s.
Visibility Graph Based Time Series Analysis
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
Climate Time Series Analysis and Forecasting
NASA Astrophysics Data System (ADS)
Young, P. C.; Fildes, R.
2009-04-01
This paper will discuss various aspects of climate time series data analysis, modelling and forecasting being carried out at Lancaster. This will include state-dependent parameter, nonlinear, stochastic modelling of globally averaged atmospheric carbon dioxide; the computation of emission strategies based on modern control theory; and extrapolative time series benchmark forecasts of annual average temperature, both global and local. The key to the forecasting evaluation will be the iterative estimation of forecast error based on rolling origin comparisons, as recommended in the forecasting research literature. The presentation will conclude with with a comparison of the time series forecasts with forecasts produced from global circulation models and a discussion of the implications for climate modelling research.
Integrated method for chaotic time series analysis
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.
Integrated method for chaotic time series analysis
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.
Topological analysis of chaotic time series
NASA Astrophysics Data System (ADS)
Gilmore, Robert
1997-10-01
Topological methods have recently been developed for the classification, analysis, and synthesis of chaotic time series. These methods can be applied to time series with a Lyapunov dimension less than three. The procedure determines the stretching and squeezing mechanisms which operate to create a strange attractor and organize all the unstable periodic orbits in the attractor in a unique way. Strange attractors are identified by a set of integers. These are topological invariants for a two dimensional branched manifold, which is the infinite dissipation limit of the strange attractor. It is remarkable that this topological information can be extracted from chaotic time series. The data required for this analysis need not be extensive or exceptionally clean. The topological invariants: (1) are subject to validation/invalidation tests; (2) describe how to model the data; and (3) do not change as control parameters change. Topological analysis is the first step in a doubly discrete classification scheme for strange attractors. The second discrete classification involves specification of a 'basis set' set of periodic orbits whose presence forces the existence of all other periodic orbits in the strange attractor. The basis set of orbits does change as control parameters change. Quantitative models developed to describe time series data are tested by the methods of entrainment. This analysis procedure has been applied to analyze a number of data sets. Several analyses are described.
Nonlinear time-series analysis revisited
NASA Astrophysics Data System (ADS)
Bradley, Elizabeth; Kantz, Holger
2015-09-01
In 1980 and 1981, two pioneering papers laid the foundation for what became known as nonlinear time-series analysis: the analysis of observed data—typically univariate—via dynamical systems theory. Based on the concept of state-space reconstruction, this set of methods allows us to compute characteristic quantities such as Lyapunov exponents and fractal dimensions, to predict the future course of the time series, and even to reconstruct the equations of motion in some cases. In practice, however, there are a number of issues that restrict the power of this approach: whether the signal accurately and thoroughly samples the dynamics, for instance, and whether it contains noise. Moreover, the numerical algorithms that we use to instantiate these ideas are not perfect; they involve approximations, scale parameters, and finite-precision arithmetic, among other things. Even so, nonlinear time-series analysis has been used to great advantage on thousands of real and synthetic data sets from a wide variety of systems ranging from roulette wheels to lasers to the human heart. Even in cases where the data do not meet the mathematical or algorithmic requirements to assure full topological conjugacy, the results of nonlinear time-series analysis can be helpful in understanding, characterizing, and predicting dynamical systems.
Nonlinear time-series analysis revisited.
Bradley, Elizabeth; Kantz, Holger
2015-09-01
In 1980 and 1981, two pioneering papers laid the foundation for what became known as nonlinear time-series analysis: the analysis of observed data-typically univariate-via dynamical systems theory. Based on the concept of state-space reconstruction, this set of methods allows us to compute characteristic quantities such as Lyapunov exponents and fractal dimensions, to predict the future course of the time series, and even to reconstruct the equations of motion in some cases. In practice, however, there are a number of issues that restrict the power of this approach: whether the signal accurately and thoroughly samples the dynamics, for instance, and whether it contains noise. Moreover, the numerical algorithms that we use to instantiate these ideas are not perfect; they involve approximations, scale parameters, and finite-precision arithmetic, among other things. Even so, nonlinear time-series analysis has been used to great advantage on thousands of real and synthetic data sets from a wide variety of systems ranging from roulette wheels to lasers to the human heart. Even in cases where the data do not meet the mathematical or algorithmic requirements to assure full topological conjugacy, the results of nonlinear time-series analysis can be helpful in understanding, characterizing, and predicting dynamical systems. PMID:26428563
Nonlinear Time Series Analysis via Neural Networks
NASA Astrophysics Data System (ADS)
Volná, Eva; Janošek, Michal; Kocian, Václav; Kotyrba, Martin
This article deals with a time series analysis based on neural networks in order to make an effective forex market [Moore and Roche, J. Int. Econ. 58, 387-411 (2002)] pattern recognition. Our goal is to find and recognize important patterns which repeatedly appear in the market history to adapt our trading system behaviour based on them.
Multifractal Analysis of Sunspot Number Time Series
NASA Astrophysics Data System (ADS)
Kasde, Satish Kumar; Gwal, Ashok Kumar; Sondhiya, Deepak Kumar
2016-07-01
Multifractal analysis based approaches have been recently developed as an alternative framework to study the complex dynamical fluctuations in sunspot numbers data including solar cycles 20 to 23 and ascending phase of current solar cycle 24.To reveal the multifractal nature, the time series data of monthly sunspot number are analyzed by singularity spectrum and multi resolution wavelet analysis. Generally, the multifractility in sunspot number generate turbulence with the typical characteristics of the anomalous process governing the magnetosphere and interior of Sun. our analysis shows that singularities spectrum of sunspot data shows well Gaussian shape spectrum, which clearly establishes the fact that monthly sunspot number has multifractal character. The multifractal analysis is able to provide a local and adaptive description of the cyclic components of sunspot number time series, which are non-stationary and result of nonlinear processes. Keywords: Sunspot Numbers, Magnetic field, Multifractal analysis and wavelet Transform Techniques.
Delay Differential Analysis of Time Series
Lainscsek, Claudia; Sejnowski, Terrence J.
2015-01-01
Nonlinear dynamical system analysis based on embedding theory has been used for modeling and prediction, but it also has applications to signal detection and classification of time series. An embedding creates a multidimensional geometrical object from a single time series. Traditionally either delay or derivative embeddings have been used. The delay embedding is composed of delayed versions of the signal, and the derivative embedding is composed of successive derivatives of the signal. The delay embedding has been extended to nonuniform embeddings to take multiple timescales into account. Both embeddings provide information on the underlying dynamical system without having direct access to all the system variables. Delay differential analysis is based on functional embeddings, a combination of the derivative embedding with nonuniform delay embeddings. Small delay differential equation (DDE) models that best represent relevant dynamic features of time series data are selected from a pool of candidate models for detection or classification. We show that the properties of DDEs support spectral analysis in the time domain where nonlinear correlation functions are used to detect frequencies, frequency and phase couplings, and bispectra. These can be efficiently computed with short time windows and are robust to noise. For frequency analysis, this framework is a multivariate extension of discrete Fourier transform (DFT), and for higher-order spectra, it is a linear and multivariate alternative to multidimensional fast Fourier transform of multidimensional correlations. This method can be applied to short or sparse time series and can be extended to cross-trial and cross-channel spectra if multiple short data segments of the same experiment are available. Together, this time-domain toolbox provides higher temporal resolution, increased frequency and phase coupling information, and it allows an easy and straightforward implementation of higher-order spectra across time
Time-Series Analysis: A Cautionary Tale
NASA Technical Reports Server (NTRS)
Damadeo, Robert
2015-01-01
Time-series analysis has often been a useful tool in atmospheric science for deriving long-term trends in various atmospherically important parameters (e.g., temperature or the concentration of trace gas species). In particular, time-series analysis has been repeatedly applied to satellite datasets in order to derive the long-term trends in stratospheric ozone, which is a critical atmospheric constituent. However, many of the potential pitfalls relating to the non-uniform sampling of the datasets were often ignored and the results presented by the scientific community have been unknowingly biased. A newly developed and more robust application of this technique is applied to the Stratospheric Aerosol and Gas Experiment (SAGE) II version 7.0 ozone dataset and the previous biases and newly derived trends are presented.
Analysis of Polyphonic Musical Time Series
NASA Astrophysics Data System (ADS)
Sommer, Katrin; Weihs, Claus
A general model for pitch tracking of polyphonic musical time series will be introduced. Based on a model of Davy and Godsill (Bayesian harmonic models for musical pitch estimation and analysis, Technical Report 431, Cambridge University Engineering Department, 2002) Davy and Godsill (2002) the different pitches of the musical sound are estimated with MCMC methods simultaneously. Additionally a preprocessing step is designed to improve the estimation of the fundamental frequencies (A comparative study on polyphonic musical time series using MCMC methods. In C. Preisach et al., editors, Data Analysis, Machine Learning, and Applications, Springer, Berlin, 2008). The preprocessing step compares real audio data with an alphabet constructed from the McGill Master Samples (Opolko and Wapnick, McGill University Master Samples [Compact disc], McGill University, Montreal, 1987) and consists of tones of different instruments. The tones with minimal Itakura-Saito distortion (Gray et al., Transactions on Acoustics, Speech, and Signal Processing ASSP-28(4):367-376, 1980) are chosen as first estimates and as starting points for the MCMC algorithms. Furthermore the implementation of the alphabet is an approach for the recognition of the instruments generating the musical time series. Results are presented for mixed monophonic data from McGill and for self recorded polyphonic audio data.
Sliced Inverse Regression for Time Series Analysis
NASA Astrophysics Data System (ADS)
Chen, Li-Sue
1995-11-01
In this thesis, general nonlinear models for time series data are considered. A basic form is x _{t} = f(beta_sp{1} {T}X_{t-1},beta_sp {2}{T}X_{t-1},... , beta_sp{k}{T}X_ {t-1},varepsilon_{t}), where x_{t} is an observed time series data, X_{t } is the first d time lag vector, (x _{t},x_{t-1},... ,x _{t-d-1}), f is an unknown function, beta_{i}'s are unknown vectors, varepsilon_{t }'s are independent distributed. Special cases include AR and TAR models. We investigate the feasibility applying SIR/PHD (Li 1990, 1991) (the sliced inverse regression and principal Hessian methods) in estimating beta _{i}'s. PCA (Principal component analysis) is brought in to check one critical condition for SIR/PHD. Through simulation and a study on 3 well -known data sets of Canadian lynx, U.S. unemployment rate and sunspot numbers, we demonstrate how SIR/PHD can effectively retrieve the interesting low-dimension structures for time series data.
Singular spectrum analysis for time series with missing data
Schoellhamer, D.H.
2001-01-01
Geophysical time series often contain missing data, which prevents analysis with many signal processing and multivariate tools. A modification of singular spectrum analysis for time series with missing data is developed and successfully tested with synthetic and actual incomplete time series of suspended-sediment concentration from San Francisco Bay. This method also can be used to low pass filter incomplete time series.
Multifractal analysis of polyalanines time series
NASA Astrophysics Data System (ADS)
Figueirêdo, P. H.; Nogueira, E.; Moret, M. A.; Coutinho, Sérgio
2010-05-01
Multifractal properties of the energy time series of short α-helix structures, specifically from a polyalanine family, are investigated through the MF-DFA technique ( multifractal detrended fluctuation analysis). Estimates for the generalized Hurst exponent h(q) and its associated multifractal exponents τ(q) are obtained for several series generated by numerical simulations of molecular dynamics in different systems from distinct initial conformations. All simulations were performed using the GROMOS force field, implemented in the program THOR. The main results have shown that all series exhibit multifractal behavior depending on the number of residues and temperature. Moreover, the multifractal spectra reveal important aspects of the time evolution of the system and suggest that the nucleation process of the secondary structures during the visits on the energy hyper-surface is an essential feature of the folding process.
Time series analysis of temporal networks
NASA Astrophysics Data System (ADS)
Sikdar, Sandipan; Ganguly, Niloy; Mukherjee, Animesh
2016-01-01
A common but an important feature of all real-world networks is that they are temporal in nature, i.e., the network structure changes over time. Due to this dynamic nature, it becomes difficult to propose suitable growth models that can explain the various important characteristic properties of these networks. In fact, in many application oriented studies only knowing these properties is sufficient. For instance, if one wishes to launch a targeted attack on a network, this can be done even without the knowledge of the full network structure; rather an estimate of some of the properties is sufficient enough to launch the attack. We, in this paper show that even if the network structure at a future time point is not available one can still manage to estimate its properties. We propose a novel method to map a temporal network to a set of time series instances, analyze them and using a standard forecast model of time series, try to predict the properties of a temporal network at a later time instance. To our aim, we consider eight properties such as number of active nodes, average degree, clustering coefficient etc. and apply our prediction framework on them. We mainly focus on the temporal network of human face-to-face contacts and observe that it represents a stochastic process with memory that can be modeled as Auto-Regressive-Integrated-Moving-Average (ARIMA). We use cross validation techniques to find the percentage accuracy of our predictions. An important observation is that the frequency domain properties of the time series obtained from spectrogram analysis could be used to refine the prediction framework by identifying beforehand the cases where the error in prediction is likely to be high. This leads to an improvement of 7.96% (for error level ≤20%) in prediction accuracy on an average across all datasets. As an application we show how such prediction scheme can be used to launch targeted attacks on temporal networks. Contribution to the Topical Issue
Three Analysis Examples for Time Series Data
Technology Transfer Automated Retrieval System (TEKTRAN)
With improvements in instrumentation and the automation of data collection, plot level repeated measures and time series data are increasingly available to monitor and assess selected variables throughout the duration of an experiment or project. Records and metadata on variables of interest alone o...
Time Series Analysis Using Geometric Template Matching.
Frank, Jordan; Mannor, Shie; Pineau, Joelle; Precup, Doina
2013-03-01
We present a novel framework for analyzing univariate time series data. At the heart of the approach is a versatile algorithm for measuring the similarity of two segments of time series called geometric template matching (GeTeM). First, we use GeTeM to compute a similarity measure for clustering and nearest-neighbor classification. Next, we present a semi-supervised learning algorithm that uses the similarity measure with hierarchical clustering in order to improve classification performance when unlabeled training data are available. Finally, we present a boosting framework called TDEBOOST, which uses an ensemble of GeTeM classifiers. TDEBOOST augments the traditional boosting approach with an additional step in which the features used as inputs to the classifier are adapted at each step to improve the training error. We empirically evaluate the proposed approaches on several datasets, such as accelerometer data collected from wearable sensors and ECG data. PMID:22641699
Singular spectrum analysis and forecasting of hydrological time series
NASA Astrophysics Data System (ADS)
Marques, C. A. F.; Ferreira, J. A.; Rocha, A.; Castanheira, J. M.; Melo-Gonçalves, P.; Vaz, N.; Dias, J. M.
The singular spectrum analysis (SSA) technique is applied to some hydrological univariate time series to assess its ability to uncover important information from those series, and also its forecast skill. The SSA is carried out on annual precipitation, monthly runoff, and hourly water temperature time series. Information is obtained by extracting important components or, when possible, the whole signal from the time series. The extracted components are then subject to forecast by the SSA algorithm. It is illustrated the SSA ability to extract a slowly varying component (i.e. the trend) from the precipitation time series, the trend and oscillatory components from the runoff time series, and the whole signal from the water temperature time series. The SSA was also able to accurately forecast the extracted components of these time series.
Evolutionary factor analysis of replicated time series.
Motta, Giovanni; Ombao, Hernando
2012-09-01
In this article, we develop a novel method that explains the dynamic structure of multi-channel electroencephalograms (EEGs) recorded from several trials in a motor-visual task experiment. Preliminary analyses of our data suggest two statistical challenges. First, the variance at each channel and cross-covariance between each pair of channels evolve over time. Moreover, the cross-covariance profiles display a common structure across all pairs, and these features consistently appear across all trials. In the light of these features, we develop a novel evolutionary factor model (EFM) for multi-channel EEG data that systematically integrates information across replicated trials and allows for smoothly time-varying factor loadings. The individual EEGs series share common features across trials, thus, suggesting the need to pool information across trials, which motivates the use of the EFM for replicated time series. We explain the common co-movements of EEG signals through the existence of a small number of common factors. These latent factors are primarily responsible for processing the visual-motor task which, through the loadings, drive the behavior of the signals observed at different channels. The estimation of the time-varying loadings is based on the spectral decomposition of the estimated time-varying covariance matrix. PMID:22364516
Apparatus for statistical time-series analysis of electrical signals
NASA Technical Reports Server (NTRS)
Stewart, C. H. (Inventor)
1973-01-01
An apparatus for performing statistical time-series analysis of complex electrical signal waveforms, permitting prompt and accurate determination of statistical characteristics of the signal is presented.
Statistical Evaluation of Time Series Analysis Techniques
NASA Technical Reports Server (NTRS)
Benignus, V. A.
1973-01-01
The performance of a modified version of NASA's multivariate spectrum analysis program is discussed. A multiple regression model was used to make the revisions. Performance improvements were documented and compared to the standard fast Fourier transform by Monte Carlo techniques.
Multifractal Analysis of Aging and Complexity in Heartbeat Time Series
NASA Astrophysics Data System (ADS)
Muñoz D., Alejandro; Almanza V., Victor H.; del Río C., José L.
2004-09-01
Recently multifractal analysis has been used intensively in the analysis of physiological time series. In this work we apply the multifractal analysis to the study of heartbeat time series from healthy young subjects and other series obtained from old healthy subjects. We show that this multifractal formalism could be a useful tool to discriminate these two kinds of series. We used the algorithm proposed by Chhabra and Jensen that provides a highly accurate, practical and efficient method for the direct computation of the singularity spectrum. Aging causes loss of multifractality in the heartbeat time series, it means that heartbeat time series of elderly persons are less complex than the time series of young persons. This analysis reveals a new level of complexity characterized by the wide range of necessary exponents to characterize the dynamics of young people.
Analysis of Time-Series Quasi-Experiments. Final Report.
ERIC Educational Resources Information Center
Glass, Gene V.; Maguire, Thomas O.
The objective of this project was to investigate the adequacy of statistical models developed by G. E. P. Box and G. C. Tiao for the analysis of time-series quasi-experiments: (1) The basic model developed by Box and Tiao is applied to actual time-series experiment data from two separate experiments, one in psychology and one in educational…
Time series data analysis using DFA
NASA Astrophysics Data System (ADS)
Okumoto, A.; Akiyama, T.; Sekino, H.; Sumi, T.
2014-02-01
Detrended fluctuation analysis (DFA) was originally developed for the evaluation of DNA sequence and interval for heart rate variability (HRV), but it is now used to obtain various biological information. In this study we perform DFA on artificially generated data where we already know the relationship between signal and the physical event causing the signal. We generate artificial data using molecular dynamics. The Brownian motion of a polymer under an external force is investigated. In order to generate artificial fluctuation in the physical properties, we introduce obstacle pillars fixed to nanostructures. Using different conditions such as presence or absence of obstacles, external field, and the polymer length, we perform DFA on energies and positions of the polymer.
Time series analysis of air pollutants in Beirut, Lebanon.
Farah, Wehbeh; Nakhlé, Myriam Mrad; Abboud, Maher; Annesi-Maesano, Isabella; Zaarour, Rita; Saliba, Nada; Germanos, Georges; Gerard, Jocelyne
2014-12-01
This study reports for the first time a time series analysis of daily urban air pollutant levels (CO, NO, NO2, O3, PM10, and SO2) in Beirut, Lebanon. The study examines data obtained between September 2005 and July 2006, and their descriptive analysis shows long-term variations of daily levels of air pollution concentrations. Strong persistence of these daily levels is identified in the time series using an autocorrelation function, except for SO2. Time series of standardized residual values (SRVs) are also calculated to compare fluctuations of the time series with different levels. Time series plots of the SRVs indicate that NO and NO2 had similar temporal fluctuations. However, NO2 and O3 had opposite temporal fluctuations, attributable to weather conditions and the accumulation of vehicular emissions. The effects of both desert dust storms and airborne particulate matter resulting from the Lebanon War in July 2006 are also discernible in the SRV plots. PMID:25150052
Fractal and natural time analysis of geoelectrical time series
NASA Astrophysics Data System (ADS)
Ramirez Rojas, A.; Moreno-Torres, L. R.; Cervantes, F.
2013-05-01
In this work we show the analysis of geoelectric time series linked with two earthquakes of M=6.6 and M=7.4. That time series were monitored at the South Pacific Mexican coast, which is the most important active seismic subduction zone in México. The geolectric time series were analyzed by using two complementary methods: a fractal analysis, by means of the detrended fluctuation analysis (DFA) in the conventional time, and the power spectrum defined in natural time domain (NTD). In conventional time we found long-range correlations prior to the EQ-occurrences and simultaneously in NTD, the behavior of the power spectrum suggest the possible existence of seismo electric signals (SES) similar with the previously reported in equivalent time series monitored in Greece prior to earthquakes of relevant magnitude.
Time Series Analysis of Insar Data: Methods and Trends
NASA Technical Reports Server (NTRS)
Osmanoglu, Batuhan; Sunar, Filiz; Wdowinski, Shimon; Cano-Cabral, Enrique
2015-01-01
Time series analysis of InSAR data has emerged as an important tool for monitoring and measuring the displacement of the Earth's surface. Changes in the Earth's surface can result from a wide range of phenomena such as earthquakes, volcanoes, landslides, variations in ground water levels, and changes in wetland water levels. Time series analysis is applied to interferometric phase measurements, which wrap around when the observed motion is larger than one-half of the radar wavelength. Thus, the spatio-temporal ''unwrapping" of phase observations is necessary to obtain physically meaningful results. Several different algorithms have been developed for time series analysis of InSAR data to solve for this ambiguity. These algorithms may employ different models for time series analysis, but they all generate a first-order deformation rate, which can be compared to each other. However, there is no single algorithm that can provide optimal results in all cases. Since time series analyses of InSAR data are used in a variety of applications with different characteristics, each algorithm possesses inherently unique strengths and weaknesses. In this review article, following a brief overview of InSAR technology, we discuss several algorithms developed for time series analysis of InSAR data using an example set of results for measuring subsidence rates in Mexico City.
Nonstationary time series prediction combined with slow feature analysis
NASA Astrophysics Data System (ADS)
Wang, G.; Chen, X.
2015-07-01
Almost all climate time series have some degree of nonstationarity due to external driving forces perturbing the observed system. Therefore, these external driving forces should be taken into account when constructing the climate dynamics. This paper presents a new technique of obtaining the driving forces of a time series from the slow feature analysis (SFA) approach, and then introduces them into a predictive model to predict nonstationary time series. The basic theory of the technique is to consider the driving forces as state variables and to incorporate them into the predictive model. Experiments using a modified logistic time series and winter ozone data in Arosa, Switzerland, were conducted to test the model. The results showed improved prediction skills.
Predicting long-term catchment nutrient export: the use of nonlinear time series models
NASA Astrophysics Data System (ADS)
Valent, Peter; Howden, Nicholas J. K.; Szolgay, Jan; Komornikova, Magda
2010-05-01
After the Second World War the nitrate concentrations in European water bodies changed significantly as the result of increased nitrogen fertilizer use and changes in land use. However, in the last decades, as a consequence of the implementation of nitrate-reducing measures in Europe, the nitrate concentrations in water bodies slowly decrease. This causes that the mean and variance of the observed time series also changes with time (nonstationarity and heteroscedascity). In order to detect changes and properly describe the behaviour of such time series by time series analysis, linear models (such as autoregressive (AR), moving average (MA) and autoregressive moving average models (ARMA)), are no more suitable. Time series with sudden changes in statistical characteristics can cause various problems in the calibration of traditional water quality models and thus give biased predictions. Proper statistical analysis of these non-stationary and heteroscedastic time series with the aim of detecting and subsequently explaining the variations in their statistical characteristics requires the use of nonlinear time series models. This information can be then used to improve the model building and calibration of conceptual water quality model or to select right calibration periods in order to produce reliable predictions. The objective of this contribution is to analyze two long time series of nitrate concentrations of the rivers Ouse and Stour with advanced nonlinear statistical modelling techniques and compare their performance with traditional linear models of the ARMA class in order to identify changes in the time series characteristics. The time series were analysed with nonlinear models with multiple regimes represented by self-exciting threshold autoregressive (SETAR) and Markov-switching models (MSW). The analysis showed that, based on the value of residual sum of squares (RSS) in both datasets, SETAR and MSW models described the time-series better than models of the
Wavelet analysis for non-stationary, nonlinear time series
NASA Astrophysics Data System (ADS)
Schulte, Justin A.
2016-08-01
Methods for detecting and quantifying nonlinearities in nonstationary time series are introduced and developed. In particular, higher-order wavelet analysis was applied to an ideal time series and the quasi-biennial oscillation (QBO) time series. Multiple-testing problems inherent in wavelet analysis were addressed by controlling the false discovery rate. A new local autobicoherence spectrum facilitated the detection of local nonlinearities and the quantification of cycle geometry. The local autobicoherence spectrum of the QBO time series showed that the QBO time series contained a mode with a period of 28 months that was phase coupled to a harmonic with a period of 14 months. An additional nonlinearly interacting triad was found among modes with periods of 10, 16 and 26 months. Local biphase spectra determined that the nonlinear interactions were not quadratic and that the effect of the nonlinearities was to produce non-smoothly varying oscillations. The oscillations were found to be skewed so that negative QBO regimes were preferred, and also asymmetric in the sense that phase transitions between the easterly and westerly phases occurred more rapidly than those from westerly to easterly regimes.
Improvements in Accurate GPS Positioning Using Time Series Analysis
NASA Astrophysics Data System (ADS)
Koyama, Yuichiro; Tanaka, Toshiyuki
Although the Global Positioning System (GPS) is used widely in car navigation systems, cell phones, surveying, and other areas, several issues still exist. We focus on the continuous data received in public use of GPS, and propose a new positioning algorithm that uses time series analysis. By fitting an autoregressive model to the time series model of the pseudorange, we propose an appropriate state-space model. We apply the Kalman filter to the state-space model and use the pseudorange estimated by the filter in our positioning calculations. The results of the authors' positioning experiment show that the accuracy of the proposed method is much better than that of the standard method. In addition, as we can obtain valid values estimated by time series analysis using the state-space model, the proposed state-space model can be applied to several other fields.
Multifractal Time Series Analysis Based on Detrended Fluctuation Analysis
NASA Astrophysics Data System (ADS)
Kantelhardt, Jan; Stanley, H. Eugene; Zschiegner, Stephan; Bunde, Armin; Koscielny-Bunde, Eva; Havlin, Shlomo
2002-03-01
In order to develop an easily applicable method for the multifractal characterization of non-stationary time series, we generalize the detrended fluctuation analysis (DFA), which is a well-established method for the determination of the monofractal scaling properties and the detection of long-range correlations. We relate the new multifractal DFA method to the standard partition function-based multifractal formalism, and compare it to the wavelet transform modulus maxima (WTMM) method which is a well-established, but more difficult procedure for this purpose. We employ the multifractal DFA method to determine if the heartrhythm during different sleep stages is characterized by different multifractal properties.
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
NASA Astrophysics Data System (ADS)
Pasqueron de Fommervault, Orens; Migon, Christophe; D`Ortenzio, Fabrizio; Ribera d'Alcalà, Maurizio; Coppola, Laurent
2015-06-01
Nitrate, phosphate, and silicate concentration profiles were measured at monthly frequency at the DYFAMED time-series station (central Ligurian Sea) between 1991 and 2011. The resulting data set, which constitutes the longest open-ocean time-series in the Mediterranean Sea, underwent quality control. A reproducible climatological pattern was observed with an unprecedented resolution, confirming the typical seasonal cycle of mid-latitudes. In summer and autumn, when the water mass is well stratified, i.e. the mixed layer depth (MLD) is shallow, nutrient concentrations in surface are very low or under the detection limit. In winter, as a result of the MLD extent, nutrients are supplied to the surface layer. Then, nutrient concentrations progressively decrease during spring. MLD appears to play a key role in controlling nutrient availability in the surface layer, but a direct, quantitative relationship between MLD and nutrient concentrations is difficult to establish due to undersampling. Regarding nutrient molar ratios (N:P, Si:N, and Si:P), results show anomalous values compared to those of other oceanic regions, presumably due to strong influence of external sources. As a consequence, nutrient molar ratios exhibit a seasonal pattern, with, in particular, an increase of the N:P ratio in condition of stratification. Over the period 1991-2011, the DYFAMED data set reveals decadal trends in nitrate and phosphate concentrations in deep waters (+0.23% and -0.62%, respectively) resulting in increasing N:P and Si:P ratios (+1.14% and +0.85% per year, respectively). Such a long-term variability is presumably related to changes in water mass and/or changes in external sources, even if it is difficult to assess due to not enough concomitant data from atmospheric and riverine inputs.
Scaling analysis of multi-variate intermittent time series
NASA Astrophysics Data System (ADS)
Kitt, Robert; Kalda, Jaan
2005-08-01
The scaling properties of the time series of asset prices and trading volumes of stock markets are analysed. It is shown that similar to the asset prices, the trading volume data obey multi-scaling length-distribution of low-variability periods. In the case of asset prices, such scaling behaviour can be used for risk forecasts: the probability of observing next day a large price movement is (super-universally) inversely proportional to the length of the ongoing low-variability period. Finally, a method is devised for a multi-factor scaling analysis. We apply the simplest, two-factor model to equity index and trading volume time series.
Analysis of Complex Intervention Effects in Time-Series Experiments.
ERIC Educational Resources Information Center
Bower, Cathleen
An iterative least squares procedure for analyzing the effect of various kinds of intervention in time-series data is described. There are numerous applications of this design in economics, education, and psychology, although until recently, no appropriate analysis techniques had been developed to deal with the model adequately. This paper…
Time Series Analysis Based on Running Mann Whitney Z Statistics
Technology Transfer Automated Retrieval System (TEKTRAN)
A sensitive and objective time series analysis method based on the calculation of Mann Whitney U statistics is described. This method samples data rankings over moving time windows, converts those samples to Mann-Whitney U statistics, and then normalizes the U statistics to Z statistics using Monte-...
ADAPTIVE DATA ANALYSIS OF COMPLEX FLUCTUATIONS IN PHYSIOLOGIC TIME SERIES
PENG, C.-K.; COSTA, MADALENA; GOLDBERGER, ARY L.
2009-01-01
We introduce a generic framework of dynamical complexity to understand and quantify fluctuations of physiologic time series. In particular, we discuss the importance of applying adaptive data analysis techniques, such as the empirical mode decomposition algorithm, to address the challenges of nonlinearity and nonstationarity that are typically exhibited in biological fluctuations. PMID:20041035
Nonlinear Analysis of Surface EMG Time Series of Back Muscles
NASA Astrophysics Data System (ADS)
Dolton, Donald C.; Zurcher, Ulrich; Kaufman, Miron; Sung, Paul
2004-10-01
A nonlinear analysis of surface electromyography time series of subjects with and without low back pain is presented. The mean-square displacement and entropy shows anomalous diffusive behavior on intermediate time range 10 ms < t < 1 s. This behavior implies the presence of correlations in the signal. We discuss the shape of the power spectrum of the signal.
Identification of human operator performance models utilizing time series analysis
NASA Technical Reports Server (NTRS)
Holden, F. M.; Shinners, S. M.
1973-01-01
The results of an effort performed by Sperry Systems Management Division for AMRL in applying time series analysis as a tool for modeling the human operator are presented. This technique is utilized for determining the variation of the human transfer function under various levels of stress. The human operator's model is determined based on actual input and output data from a tracking experiment.
Mixed Spectrum Analysis on fMRI Time-Series.
Kumar, Arun; Lin, Feng; Rajapakse, Jagath C
2016-06-01
Temporal autocorrelation present in functional magnetic resonance image (fMRI) data poses challenges to its analysis. The existing approaches handling autocorrelation in fMRI time-series often presume a specific model of autocorrelation such as an auto-regressive model. The main limitation here is that the correlation structure of voxels is generally unknown and varies in different brain regions because of different levels of neurogenic noises and pulsatile effects. Enforcing a universal model on all brain regions leads to bias and loss of efficiency in the analysis. In this paper, we propose the mixed spectrum analysis of the voxel time-series to separate the discrete component corresponding to input stimuli and the continuous component carrying temporal autocorrelation. A mixed spectral analysis technique based on M-spectral estimator is proposed, which effectively removes autocorrelation effects from voxel time-series and identify significant peaks of the spectrum. As the proposed method does not assume any prior model for the autocorrelation effect in voxel time-series, varying correlation structure among the brain regions does not affect its performance. We have modified the standard M-spectral method for an application on a spatial set of time-series by incorporating the contextual information related to the continuous spectrum of neighborhood voxels, thus reducing considerably the computation cost. Likelihood of the activation is predicted by comparing the amplitude of discrete component at stimulus frequency of voxels across the brain by using normal distribution and modeling spatial correlations among the likelihood with a conditional random field. We also demonstrate the application of the proposed method in detecting other desired frequencies. PMID:26800533
Time Series Analysis of 3D Coordinates Using Nonstochastic Observations
NASA Astrophysics Data System (ADS)
Velsink, Hiddo
2016-03-01
Adjustment and testing of a combination of stochastic and nonstochastic observations is applied to the deformation analysis of a time series of 3D coordinates. Nonstochastic observations are constant values that are treated as if they were observations. They are used to formulate constraints on the unknown parameters of the adjustment problem. Thus they describe deformation patterns. If deformation is absent, the epochs of the time series are supposed to be related via affine, similarity or congruence transformations. S-basis invariant testing of deformation patterns is treated. The model is experimentally validated by showing the procedure for a point set of 3D coordinates, determined from total station measurements during five epochs. The modelling of two patterns, the movement of just one point in several epochs, and of several points, is shown. Full, rank deficient covariance matrices of the 3D coordinates, resulting from free network adjustments of the total station measurements of each epoch, are used in the analysis.
Ensemble vs. time averages in financial time series analysis
NASA Astrophysics Data System (ADS)
Seemann, Lars; Hua, Jia-Chen; McCauley, Joseph L.; Gunaratne, Gemunu H.
2012-12-01
Empirical analysis of financial time series suggests that the underlying stochastic dynamics are not only non-stationary, but also exhibit non-stationary increments. However, financial time series are commonly analyzed using the sliding interval technique that assumes stationary increments. We propose an alternative approach that is based on an ensemble over trading days. To determine the effects of time averaging techniques on analysis outcomes, we create an intraday activity model that exhibits periodic variable diffusion dynamics and we assess the model data using both ensemble and time averaging techniques. We find that ensemble averaging techniques detect the underlying dynamics correctly, whereas sliding intervals approaches fail. As many traded assets exhibit characteristic intraday volatility patterns, our work implies that ensemble averages approaches will yield new insight into the study of financial markets’ dynamics.
Performance of multifractal detrended fluctuation analysis on short time series
NASA Astrophysics Data System (ADS)
López, Juan Luis; Contreras, Jesús Guillermo
2013-02-01
The performance of the multifractal detrended analysis on short time series is evaluated for synthetic samples of several mono- and multifractal models. The reconstruction of the generalized Hurst exponents is used to determine the range of applicability of the method and the precision of its results as a function of the decreasing length of the series. As an application the series of the daily exchange rate between the U.S. dollar and the euro is studied.
Mode Analysis with Autocorrelation Method (Single Time Series) in Tokamak
NASA Astrophysics Data System (ADS)
Saadat, Shervin; Salem, Mohammad K.; Goranneviss, Mahmoud; Khorshid, Pejman
2010-08-01
In this paper plasma mode analyzed with statistical method that designated Autocorrelation function. Auto correlation function used from one time series, so for this purpose we need one Minov coil. After autocorrelation analysis on mirnov coil data, spectral density diagram is plotted. Spectral density diagram from symmetries and trends can analyzed plasma mode. RHF fields effects with this method ate investigated in IR-T1 tokamak and results corresponded with multichannel methods such as SVD and FFT.
Satellite time series analysis using Empirical Mode Decomposition
NASA Astrophysics Data System (ADS)
Pannimpullath, R. Renosh; Doolaeghe, Diane; Loisel, Hubert; Vantrepotte, Vincent; Schmitt, Francois G.
2016-04-01
Geophysical fields possess large fluctuations over many spatial and temporal scales. Satellite successive images provide interesting sampling of this spatio-temporal multiscale variability. Here we propose to consider such variability by performing satellite time series analysis, pixel by pixel, using Empirical Mode Decomposition (EMD). EMD is a time series analysis technique able to decompose an original time series into a sum of modes, each one having a different mean frequency. It can be used to smooth signals, to extract trends. It is built in a data-adaptative way, and is able to extract information from nonlinear signals. Here we use MERIS Suspended Particulate Matter (SPM) data, on a weekly basis, during 10 years. There are 458 successive time steps. We have selected 5 different regions of coastal waters for the present study. They are Vietnam coastal waters, Brahmaputra region, St. Lawrence, English Channel and McKenzie. These regions have high SPM concentrations due to large scale river run off. Trend and Hurst exponents are derived for each pixel in each region. The energy also extracted using Hilberts Spectral Analysis (HSA) along with EMD method. Normalised energy computed for each mode for each region with the total energy. The total energy computed using all the modes are extracted using EMD method.
The multiscale analysis between stock market time series
NASA Astrophysics Data System (ADS)
Shi, Wenbin; Shang, Pengjian
2015-11-01
This paper is devoted to multiscale cross-correlation analysis on stock market time series, where multiscale DCCA cross-correlation coefficient as well as multiscale cross-sample entropy (MSCE) is applied. Multiscale DCCA cross-correlation coefficient is a realization of DCCA cross-correlation coefficient on multiple scales. The results of this method present a good scaling characterization. More significantly, this method is able to group stock markets by areas. Compared to multiscale DCCA cross-correlation coefficient, MSCE presents a more remarkable scaling characterization and the value of each log return of financial time series decreases with the increasing of scale factor. But the results of grouping is not as good as multiscale DCCA cross-correlation coefficient.
Time series analysis for psychological research: examining and forecasting change
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
Time series analysis for psychological research: examining and forecasting change.
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
Stratospheric ozone time series analysis using dynamical linear models
NASA Astrophysics Data System (ADS)
Laine, Marko; Kyrölä, Erkki
2013-04-01
We describe a hierarchical statistical state space model for ozone profile time series. The time series are from satellite measurements by the SAGE II and GOMOS instruments spanning years 1984-2012. The original data sets are combined and gridded monthly using 10 degree latitude bands, and covering 20-60 km with 1 km vertical spacing. Model components include level, trend, seasonal effect with solar activity, and quasi biennial oscillations as proxy variables. A typical feature of an atmospheric time series is that they are not stationary but exhibit both slowly varying and abrupt changes in the distributional properties. These are caused by external forcing such as changes in the solar activity or volcanic eruptions. Further, the data sampling is often nonuniform, there are data gaps, and the uncertainty of the observations can vary. When observations are combined from various sources there will be instrument and retrieval method related biases. The differences in sampling lead also to uncertainties. Standard classical ARIMA type of statistical time series methods are mostly useless for atmospheric data. A more general approach makes use of dynamical linear models and Kalman filter type of sequential algorithms. These state space models assume a linear relationship between the unknown state of the system and the observations and for the process evolution of the hidden states. They are still flexible enough to model both smooth trends and sudden changes. The above mentioned methodological challenges are discussed, together with analysis of change points in trends related to recovery of stratospheric ozone. This work is part of the ESA SPIN and ozone CCI projects.
Time series analysis using semiparametric regression on oil palm production
NASA Astrophysics Data System (ADS)
Yundari, Pasaribu, U. S.; Mukhaiyar, U.
2016-04-01
This paper presents semiparametric kernel regression method which has shown its flexibility and easiness in mathematical calculation, especially in estimating density and regression function. Kernel function is continuous and it produces a smooth estimation. The classical kernel density estimator is constructed by completely nonparametric analysis and it is well reasonable working for all form of function. Here, we discuss about parameter estimation in time series analysis. First, we consider the parameters are exist, then we use nonparametrical estimation which is called semiparametrical. The selection of optimum bandwidth is obtained by considering the approximation of Mean Integrated Square Root Error (MISE).
Chaotic time series analysis in economics: Balance and perspectives
Faggini, Marisa
2014-12-15
The aim of the paper is not to review the large body of work concerning nonlinear time series analysis in economics, about which much has been written, but rather to focus on the new techniques developed to detect chaotic behaviours in economic data. More specifically, our attention will be devoted to reviewing some of these techniques and their application to economic and financial data in order to understand why chaos theory, after a period of growing interest, appears now not to be such an interesting and promising research area.
Diagnosis of nonlinear systems using time series analysis
Hunter, N.F. Jr.
1991-01-01
Diagnosis and analysis techniques for linear systems have been developed and refined to a high degree of precision. In contrast, techniques for the analysis of data from nonlinear systems are in the early stages of development. This paper describes a time series technique for the analysis of data from nonlinear systems. The input and response time series resulting from excitation of the nonlinear system are embedded in a state space. The form of the embedding is optimized using local canonical variate analysis and singular value decomposition techniques. From the state space model, future system responses are estimated. The expected degree of predictability of the system is investigated using the state transition matrix. The degree of nonlinearity present is quantified using the geometry of the transfer function poles in the z plane. Examples of application to a linear single-degree-of-freedom system, a single-degree-of-freedom Duffing Oscillator, and linear and nonlinear three degree of freedom oscillators are presented. 11 refs., 9 figs.
Analysis of Multipsectral Time Series for supporting Forest Management Plans
NASA Astrophysics Data System (ADS)
Simoniello, T.; Carone, M. T.; Costantini, G.; Frattegiani, M.; Lanfredi, M.; Macchiato, M.
2010-05-01
Adequate forest management requires specific plans based on updated and detailed mapping. Multispectral satellite time series have been largely applied to forest monitoring and studies at different scales tanks to their capability of providing synoptic information on some basic parameters descriptive of vegetation distribution and status. As a low expensive tool for supporting forest management plans in operative context, we tested the use of Landsat-TM/ETM time series (1987-2006) in the high Agri Valley (Southern Italy) for planning field surveys as well as for the integration of existing cartography. As preliminary activity to make all scenes radiometrically consistent the no-change regression normalization was applied to the time series; then all the data concerning available forest maps, municipal boundaries, water basins, rivers, and roads were overlapped in a GIS environment. From the 2006 image we elaborated the NDVI map and analyzed the distribution for each land cover class. To separate the physiological variability and identify the anomalous areas, a threshold on the distributions was applied. To label the non homogenous areas, a multitemporal analysis was performed by separating heterogeneity due to cover changes from that linked to basilar unit mapping and classification labelling aggregations. Then a map of priority areas was produced to support the field survey plan. To analyze the territorial evolution, the historical land cover maps were elaborated by adopting a hybrid classification approach based on a preliminary segmentation, the identification of training areas, and a subsequent maximum likelihood categorization. Such an analysis was fundamental for the general assessment of the territorial dynamics and in particular for the evaluation of the efficacy of past intervention activities.
Remote-Sensing Time Series Analysis, a Vegetation Monitoring Tool
NASA Technical Reports Server (NTRS)
McKellip, Rodney; Prados, Donald; Ryan, Robert; Ross, Kenton; Spruce, Joseph; Gasser, Gerald; Greer, Randall
2008-01-01
The Time Series Product Tool (TSPT) is software, developed in MATLAB , which creates and displays high signal-to- noise Vegetation Indices imagery and other higher-level products derived from remotely sensed data. This tool enables automated, rapid, large-scale regional surveillance of crops, forests, and other vegetation. TSPT temporally processes high-revisit-rate satellite imagery produced by the Moderate Resolution Imaging Spectroradiometer (MODIS) and by other remote-sensing systems. Although MODIS imagery is acquired daily, cloudiness and other sources of noise can greatly reduce the effective temporal resolution. To improve cloud statistics, the TSPT combines MODIS data from multiple satellites (Aqua and Terra). The TSPT produces MODIS products as single time-frame and multitemporal change images, as time-series plots at a selected location, or as temporally processed image videos. Using the TSPT program, MODIS metadata is used to remove and/or correct bad and suspect data. Bad pixel removal, multiple satellite data fusion, and temporal processing techniques create high-quality plots and animated image video sequences that depict changes in vegetation greenness. This tool provides several temporal processing options not found in other comparable imaging software tools. Because the framework to generate and use other algorithms is established, small modifications to this tool will enable the use of a large range of remotely sensed data types. An effective remote-sensing crop monitoring system must be able to detect subtle changes in plant health in the earliest stages, before the effects of a disease outbreak or other adverse environmental conditions can become widespread and devastating. The integration of the time series analysis tool with ground-based information, soil types, crop types, meteorological data, and crop growth models in a Geographic Information System, could provide the foundation for a large-area crop-surveillance system that could identify
Time series clustering analysis of health-promoting behavior
NASA Astrophysics Data System (ADS)
Yang, Chi-Ta; Hung, Yu-Shiang; Deng, Guang-Feng
2013-10-01
Health promotion must be emphasized to achieve the World Health Organization goal of health for all. Since the global population is aging rapidly, ComCare elder health-promoting service was developed by the Taiwan Institute for Information Industry in 2011. Based on the Pender health promotion model, ComCare service offers five categories of health-promoting functions to address the everyday needs of seniors: nutrition management, social support, exercise management, health responsibility, stress management. To assess the overall ComCare service and to improve understanding of the health-promoting behavior of elders, this study analyzed health-promoting behavioral data automatically collected by the ComCare monitoring system. In the 30638 session records collected for 249 elders from January, 2012 to March, 2013, behavior patterns were identified by fuzzy c-mean time series clustering algorithm combined with autocorrelation-based representation schemes. The analysis showed that time series data for elder health-promoting behavior can be classified into four different clusters. Each type reveals different health-promoting needs, frequencies, function numbers and behaviors. The data analysis result can assist policymakers, health-care providers, and experts in medicine, public health, nursing and psychology and has been provided to Taiwan National Health Insurance Administration to assess the elder health-promoting behavior.
Time series analysis of electron flux at geostationary orbit
Szita, S.; Rodgers, D.J.; Johnstone, A.D.
1996-07-01
Time series of energetic (42.9{endash}300 keV) electron flux data from the geostationary satellite Meteosat-3 shows variability over various timescales. Of particular interest are the strong local time dependence of the flux data and the large flux peaks associated with particle injection events which occur over a timescale of a few hours. Fourier analysis has shown that for this energy range, the average electron flux diurnal variation can be approximated by a combination of two sine waves with periods of 12 and 24 hours. The data have been further examined using wavelet analysis, which shows how the diurnal variation changes and where it appears most significant. The injection events have a characteristic appearance but do not occur in phase with one another and therefore do not show up in a Fourier spectrum. Wavelet analysis has been used to look for characteristic time scales for these events. {copyright} {ital 1996 American Institute of Physics.}
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.
A Multiscale Approach to InSAR Time Series Analysis
NASA Astrophysics Data System (ADS)
Hetland, E. A.; Muse, P.; Simons, M.; Lin, N.; Dicaprio, C. J.
2010-12-01
We present a technique to constrain time-dependent deformation from repeated satellite-based InSAR observations of a given region. This approach, which we call MInTS (Multiscale InSAR Time Series analysis), relies on a spatial wavelet decomposition to permit the inclusion of distance based spatial correlations in the observations while maintaining computational tractability. As opposed to single pixel InSAR time series techniques, MInTS takes advantage of both spatial and temporal characteristics of the deformation field. We use a weighting scheme which accounts for the presence of localized holes due to decorrelation or unwrapping errors in any given interferogram. We represent time-dependent deformation using a dictionary of general basis functions, capable of detecting both steady and transient processes. The estimation is regularized using a model resolution based smoothing so as to be able to capture rapid deformation where there are temporally dense radar acquisitions and to avoid oscillations during time periods devoid of acquisitions. MInTS also has the flexibility to explicitly parametrize known time-dependent processes that are expected to contribute to a given set of observations (e.g., co-seismic steps and post-seismic transients, secular variations, seasonal oscillations, etc.). We use cross validation to choose the regularization penalty parameter in the inversion of for the time-dependent deformation field. We demonstrate MInTS using a set of 63 ERS-1/2 and 29 Envisat interferograms for Long Valley Caldera.
STUDIES IN ASTRONOMICAL TIME SERIES ANALYSIS. VI. BAYESIAN BLOCK REPRESENTATIONS
Scargle, Jeffrey D.; Norris, Jay P.; Jackson, Brad; Chiang, James
2013-02-20
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-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 piecewise 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 et al. In the spirit of Reproducible Research all of the code and data necessary to reproduce all of the figures in this paper are included as supplementary material.
Time series analysis of waterfowl species number change
NASA Astrophysics Data System (ADS)
Mengjung Chou, Caroline; Da-Wei Tsai, David; Honglay Chen, Paris
2014-05-01
The objective of this study is to analyze the time series of waterfowl species numbers in Da-du estuary which was set up as Important Bird Areas (IBAs) from birdlife international in 2004. The multiplicative decomposition method has been adapted to determine the species variations, including long-term (T), seasonal (S), circular (C), and irregular (I). The results indicated: (1) The long-term trend decreased with time from 1989 to 2012; (2) There were two seasonal high peaks in April and November each year with the lowest peak in June. Moreover, since the winter visitors had the dominant numbers in total species numbers, the seasonal changes were mainly depended on the winter birds' migration. (3) The waterfowl was gradually restored back from lowest point in 1996, but the difference between 1989 and 2003 indicated the irreversible effect existed already. (4) The irregular variation was proved as a random distribution by several statistical tests including normality test, homogeneity of variance, independence test and variation probability method to portray the characteristics of the distributions and to demonstrate its randomness. Consequently, this study exhibited the time series analysis methods were reasonable well to present the waterfowl species changes numerically. And those results could be the precious data for the researches of ecosystem succession and anthropogenic impacts in the estuary.
Automatising the analysis of stochastic biochemical time-series
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
Time-series analysis of Campylobacter incidence in Switzerland.
Wei, W; Schüpbach, G; Held, L
2015-07-01
Campylobacteriosis has been the most common food-associated notifiable infectious disease in Switzerland since 1995. Contact with and ingestion of raw or undercooked broilers are considered the dominant risk factors for infection. In this study, we investigated the temporal relationship between the disease incidence in humans and the prevalence of Campylobacter in broilers in Switzerland from 2008 to 2012. We use a time-series approach to describe the pattern of the disease by incorporating seasonal effects and autocorrelation. The analysis shows that prevalence of Campylobacter in broilers, with a 2-week lag, has a significant impact on disease incidence in humans. Therefore Campylobacter cases in humans can be partly explained by contagion through broiler meat. We also found a strong autoregressive effect in human illness, and a significant increase of illness during Christmas and New Year's holidays. In a final analysis, we corrected for the sampling error of prevalence in broilers and the results gave similar conclusions. PMID:25400006
Feature extraction for change analysis in SAR time series
NASA Astrophysics Data System (ADS)
Boldt, Markus; Thiele, Antje; Schulz, Karsten; Hinz, Stefan
2015-10-01
In remote sensing, the change detection topic represents a broad field of research. If time series data is available, change detection can be used for monitoring applications. These applications require regular image acquisitions at identical time of day along a defined period. Focusing on remote sensing sensors, radar is especially well-capable for applications requiring regularity, since it is independent from most weather and atmospheric influences. Furthermore, regarding the image acquisitions, the time of day plays no role due to the independence from daylight. Since 2007, the German SAR (Synthetic Aperture Radar) satellite TerraSAR-X (TSX) permits the acquisition of high resolution radar images capable for the analysis of dense built-up areas. In a former study, we presented the change analysis of the Stuttgart (Germany) airport. The aim of this study is the categorization of detected changes in the time series. This categorization is motivated by the fact that it is a poor statement only to describe where and when a specific area has changed. At least as important is the statement about what has caused the change. The focus is set on the analysis of so-called high activity areas (HAA) representing areas changing at least four times along the investigated period. As first step for categorizing these HAAs, the matching HAA changes (blobs) have to be identified. Afterwards, operating in this object-based blob level, several features are extracted which comprise shape-based, radiometric, statistic, morphological values and one context feature basing on a segmentation of the HAAs. This segmentation builds on the morphological differential attribute profiles (DAPs). Seven context classes are established: Urban, infrastructure, rural stable, rural unstable, natural, water and unclassified. A specific HA blob is assigned to one of these classes analyzing the CovAmCoh time series signature of the surrounding segments. In combination, also surrounding GIS information
NASA Astrophysics Data System (ADS)
Phillips, D. A.; Meertens, C. M.; Hodgkinson, K. M.; Puskas, C. M.; Boler, F. M.; Snett, L.; Mattioli, G. S.
2013-12-01
We present an overview of time series data, tools and services available from UNAVCO along with two specific and compelling examples of geodetic time series analysis. UNAVCO provides a diverse suite of geodetic data products and cyberinfrastructure services to support community research and education. The UNAVCO archive includes data from 2500+ continuous GPS stations, borehole geophysics instruments (strainmeters, seismometers, tiltmeters, pore pressure sensors), and long baseline laser strainmeters. These data span temporal scales from seconds to decades and provide global spatial coverage with regionally focused networks including the EarthScope Plate Boundary Observatory (PBO) and COCONet. This rich, open access dataset is a tremendous resource that enables the exploration, identification and analysis of time varying signals associated with crustal deformation, reference frame determinations, isostatic adjustments, atmospheric phenomena, hydrologic processes and more. UNAVCO provides a suite of time series exploration and analysis resources including static plots, dynamic plotting tools, and data products and services designed to enhance time series analysis. The PBO GPS network allow for identification of ~1 mm level deformation signals. At some GPS stations seasonal signals and longer-term trends in both the vertical and horizontal components can be dominated by effects of hydrological loading from natural and anthropogenic sources. Modeling of hydrologic deformation using GLDAS and a variety of land surface models (NOAH, MOSAIC, VIC and CLM) shows promise for independently modeling hydrologic effects and separating them from tectonic deformation as well as anthropogenic loading sources. A major challenge is to identify where loading is dominant and corrections from GLDAS can apply and where pumping is the dominant signal and corrections are not possible without some other data. In another arena, the PBO strainmeter network was designed to capture small short
Spectral Procedures Enhance the Analysis of Three Agricultural Time Series
Technology Transfer Automated Retrieval System (TEKTRAN)
Many agricultural and environmental variables are influenced by cyclic processes that occur naturally. Consequently their time series often have cyclic behavior. This study developed times series models for three different phenomenon: (1) a 60 year-long state average crop yield record, (2) a four ...
Nonlinear times series analysis of epileptic human electroencephalogram (EEG)
NASA Astrophysics Data System (ADS)
Li, Dingzhou
The problem of seizure anticipation in patients with epilepsy has attracted significant attention in the past few years. In this paper we discuss two approaches, using methods of nonlinear time series analysis applied to scalp electrode recordings, which is able to distinguish between epochs temporally distant from and just prior to, the onset of a seizure in patients with temporal lobe epilepsy. First we describe a method involving a comparison of recordings taken from electrodes adjacent to and remote from the site of the seizure focus. In particular, we define a nonlinear quantity which we call marginal predictability. This quantity is computed using data from remote and from adjacent electrodes. We find that the difference between the marginal predictabilities computed for the remote and adjacent electrodes decreases several tens of minutes prior to seizure onset, compared to its value interictally. We also show that these difl'crcnc es of marginal predictability intervals are independent of the behavior state of the patient. Next we examine the please coherence between different electrodes both in the long-range and the short-range. When time is distant from seizure onsets ("interictally"), epileptic patients have lower long-range phase coherence in the delta (1-4Hz) and beta (18-30Hz) frequency band compared to nonepileptic subjects. When seizures approach (''preictally"), we observe an increase in phase coherence in the beta band. However, interictally there is no difference in short-range phase coherence between this cohort of patients and non-epileptic subjects. Preictally short-range phase coherence also increases in the alpha (10-13Hz) and the beta band. Next we apply the quantity marginal predictability on the phase difference time series. Such marginal predictabilities are lower in the patients than in the non-epileptic subjects. However, when seizure approaches, the former moves asymptotically towards the latter.
Time series power flow analysis for distribution connected PV generation.
Broderick, Robert Joseph; Quiroz, Jimmy Edward; Ellis, Abraham; Reno, Matthew J.; Smith, Jeff; Dugan, Roger
2013-01-01
Distributed photovoltaic (PV) projects must go through an interconnection study process before connecting to the distribution grid. These studies are intended to identify the likely impacts and mitigation alternatives. In the majority of the cases, system impacts can be ruled out or mitigation can be identified without an involved study, through a screening process or a simple supplemental review study. For some proposed projects, expensive and time-consuming interconnection studies are required. The challenges to performing the studies are twofold. First, every study scenario is potentially unique, as the studies are often highly specific to the amount of PV generation capacity that varies greatly from feeder to feeder and is often unevenly distributed along the same feeder. This can cause location-specific impacts and mitigations. The second challenge is the inherent variability in PV power output which can interact with feeder operation in complex ways, by affecting the operation of voltage regulation and protection devices. The typical simulation tools and methods in use today for distribution system planning are often not adequate to accurately assess these potential impacts. This report demonstrates how quasi-static time series (QSTS) simulation and high time-resolution data can be used to assess the potential impacts in a more comprehensive manner. The QSTS simulations are applied to a set of sample feeders with high PV deployment to illustrate the usefulness of the approach. The report describes methods that can help determine how PV affects distribution system operations. The simulation results are focused on enhancing the understanding of the underlying technical issues. The examples also highlight the steps needed to perform QSTS simulation and describe the data needed to drive the simulations. The goal of this report is to make the methodology of time series power flow analysis readily accessible to utilities and others responsible for evaluating
Wavelet analysis and scaling properties of time series
NASA Astrophysics Data System (ADS)
Manimaran, P.; Panigrahi, Prasanta K.; Parikh, Jitendra C.
2005-10-01
We propose a wavelet based method for the characterization of the scaling behavior of nonstationary time series. It makes use of the built-in ability of the wavelets for capturing the trends in a data set, in variable window sizes. Discrete wavelets from the Daubechies family are used to illustrate the efficacy of this procedure. After studying binomial multifractal time series with the present and earlier approaches of detrending for comparison, we analyze the time series of averaged spin density in the 2D Ising model at the critical temperature, along with several experimental data sets possessing multifractal behavior.
Wavelet analysis and scaling properties of time series.
Manimaran, P; Panigrahi, Prasanta K; Parikh, Jitendra C
2005-10-01
We propose a wavelet based method for the characterization of the scaling behavior of nonstationary time series. It makes use of the built-in ability of the wavelets for capturing the trends in a data set, in variable window sizes. Discrete wavelets from the Daubechies family are used to illustrate the efficacy of this procedure. After studying binomial multifractal time series with the present and earlier approaches of detrending for comparison, we analyze the time series of averaged spin density in the 2D Ising model at the critical temperature, along with several experimental data sets possessing multifractal behavior. PMID:16383481
Assessing Spontaneous Combustion Instability with Nonlinear Time Series Analysis
NASA Technical Reports Server (NTRS)
Eberhart, C. J.; Casiano, M. J.
2015-01-01
Considerable interest lies in the ability to characterize the onset of spontaneous instabilities within liquid propellant rocket engine (LPRE) combustion devices. Linear techniques, such as fast Fourier transforms, various correlation parameters, and critical damping parameters, have been used at great length for over fifty years. Recently, nonlinear time series methods have been applied to deduce information pertaining to instability incipiency hidden in seemingly stochastic combustion noise. A technique commonly used in biological sciences known as the Multifractal Detrended Fluctuation Analysis has been extended to the combustion dynamics field, and is introduced here as a data analysis approach complementary to linear ones. Advancing, a modified technique is leveraged to extract artifacts of impending combustion instability that present themselves a priori growth to limit cycle amplitudes. Analysis is demonstrated on data from J-2X gas generator testing during which a distinct spontaneous instability was observed. Comparisons are made to previous work wherein the data were characterized using linear approaches. Verification of the technique is performed by examining idealized signals and comparing two separate, independently developed tools.
NASA Astrophysics Data System (ADS)
Strozzi, Fernanda; Zaldívar, José-Manuel; Zbilut, Joseph P.
2007-03-01
The application of recurrence quantification analysis (RQA) and state space divergence reconstruction for the analysis of financial time series in terms of cross-correlation and forecasting is illustrated using high-frequency time series and random heavy-tailed data sets. The results indicate that these techniques, able to deal with non-stationarity in the time series, may contribute to the understanding of the complex dynamics hidden in financial markets. The results demonstrate that financial time series are highly correlated. Finally, an on-line trading strategy is illustrated and the results shown using high-frequency foreign exchange time series.
Time-series analysis of offshore-wind-wave groupiness
Liang, H.B.
1988-01-01
This research is to applies basic time-series-analysis techniques on the complex envelope function where the study of the offshore-wind-wave groupiness is a relevant interest. In constructing the complex envelope function, a phase-unwrapping technique is integrated into the algorithm for estimating the carrier frequency and preserving the phase information for further studies. The Gaussian random wave model forms the basis of the wave-group statistics by the envelope-amplitude crossings. Good agreement between the theory and the analysis of field records is found. Other linear models, such as the individual-waves approach and the energy approach, are compared to the envelope approach by analyzing the same set of records. It is found that the character of the filter used in each approach dominates the wave-group statistics. Analyses indicate that the deep offshore wind waves are weakly nonlinear and the Gaussian random assumption remains appropriate for describing the sea state. Wave groups statistics derived from the Gaussian random wave model thus become applicable.
Interglacial climate dynamics and advanced time series analysis
NASA Astrophysics Data System (ADS)
Mudelsee, Manfred; Bermejo, Miguel; Köhler, Peter; Lohmann, Gerrit
2013-04-01
Studying the climate dynamics of past interglacials (IGs) helps to better assess the anthropogenically influenced dynamics of the current IG, the Holocene. We select the IG portions from the EPICA Dome C ice core archive, which covers the past 800 ka, to apply methods of statistical time series analysis (Mudelsee 2010). The analysed variables are deuterium/H (indicating temperature) (Jouzel et al. 2007), greenhouse gases (Siegenthaler et al. 2005, Loulergue et al. 2008, L¨ü thi et al. 2008) and a model-co-derived climate radiative forcing (Köhler et al. 2010). We select additionally high-resolution sea-surface-temperature records from the marine sedimentary archive. The first statistical method, persistence time estimation (Mudelsee 2002) lets us infer the 'climate memory' property of IGs. Second, linear regression informs about long-term climate trends during IGs. Third, ramp function regression (Mudelsee 2000) is adapted to look on abrupt climate changes during IGs. We compare the Holocene with previous IGs in terms of these mathematical approaches, interprete results in a climate context, assess uncertainties and the requirements to data from old IGs for yielding results of 'acceptable' accuracy. This work receives financial support from the Deutsche Forschungsgemeinschaft (Project ClimSens within the DFG Research Priority Program INTERDYNAMIK) and the European Commission (Marie Curie Initial Training Network LINC, No. 289447, within the 7th Framework Programme). References Jouzel J, Masson-Delmotte V, Cattani O, Dreyfus G, Falourd S, Hoffmann G, Minster B, Nouet J, Barnola JM, Chappellaz J, Fischer H, Gallet JC, Johnsen S, Leuenberger M, Loulergue L, Luethi D, Oerter H, Parrenin F, Raisbeck G, Raynaud D, Schilt A, Schwander J, Selmo E, Souchez R, Spahni R, Stauffer B, Steffensen JP, Stenni B, Stocker TF, Tison JL, Werner M, Wolff EW (2007) Orbital and millennial Antarctic climate variability over the past 800,000 years. Science 317:793. Köhler P, Bintanja R
Time Series Analysis of the Blazar OJ 287
NASA Astrophysics Data System (ADS)
Gamel, Ellen; Ryle, W. T.; Carini, M. T.
2013-06-01
Blazars are a subset of active galactic nuclei (AGN) where the light is viewed along the jet of radiation produced by the central supermassive black hole. These very luminous objects vary in brightness and are associated with the cores of distant galaxies. The blazar, OJ 287, has been monitored and its brightness tracked over time. From these light curves the relationship between the characteristic “break frequency” and black hole mass can be determined through the use of power density spectra. In order to obtain a well-sampled light curve, this blazar will be observed at a wide range of timescales. Long time scales will be obtained using archived light curves from published literature. Medium time scales were obtained through a combination of data provided by Western Kentucky University and data collected at The Bank of Kentucky Observatory. Short time scales were achieved via a single night of observation at the 72” Perkins Telescope at Lowell Observatory in Flagstaff, AZ. Using time series analysis, we present a revised mass estimate for the super massive black hole of OJ 287. This object is of particular interest because it may harbor a binary black hole at its center.
Permutation Entropy Analysis of Geomagnetic Indices Time Series
NASA Astrophysics Data System (ADS)
De Michelis, Paola; Consolini, Giuseppe
2013-04-01
The Earth's magnetospheric dynamics displays a very complex nature in response to solar wind changes as widely documented in the scientific literature. This complex dynamics manifests in various physical processes occurring in different regions of the Earth's magnetosphere as clearly revealed by previous analyses on geomagnetic indices (AE-indices, Dst, Sym-H, ....., etc.). One of the most interesting features of the geomagnetic indices as proxies of the Earth's magnetospheric dynamics is the multifractional nature of the time series of such indices. This aspect has been interpreted as the occurrence of intermittence and dynamical phase transition in the Earth's magnetosphere. Here, we investigate the Markovian nature of different geomagnetic indices (AE-indices, Sym-H, Asy-H) and their fluctuations by means of Permutation Entropy Analysis. The results clearly show the non-Markovian and different nature of the distinct sets of geomagnetic indices, pointing towards diverse underlying physical processes. A discussion in connection with the nature of the physical processes responsible of each set of indices and their multifractional character is attempted.
Chaotic time series analysis of vision evoked EEG
NASA Astrophysics Data System (ADS)
Zhang, Ningning; Wang, Hong
2009-12-01
To investigate the human brain activities for aesthetic processing, beautiful woman face picture and ugly buffoon face picture were applied. Twelve subjects were assigned the aesthetic processing task while the electroencephalogram (EEG) was recorded. Event-related brain potential (ERP) was required from the 32 scalp electrodes and the ugly buffoon picture produced larger amplitudes for the N1, P2, N2, and late slow wave components. Average ERP from the ugly buffoon picture were larger than that from the beautiful woman picture. The ERP signals shows that the ugly buffoon elite higher emotion waves than the beautiful woman face, because some expression is on the face of the buffoon. Then, chaos time series analysis was carried out to calculate the largest Lyapunov exponent using small data set method and the correlation dimension using G-P algorithm. The results show that the largest Lyapunov exponents of the ERP signals are greater than zero, which indicate that the ERP signals may be chaotic. The correlations dimensions coming from the beautiful woman picture are larger than that from the ugly buffoon picture. The comparison of the correlations dimensions shows that the beautiful face can excite the brain nerve cells. The research in the paper is a persuasive proof to the opinion that cerebrum's work is chaotic under some picture stimuli.
Chaotic time series analysis of vision evoked EEG
NASA Astrophysics Data System (ADS)
Zhang, Ningning; Wang, Hong
2010-01-01
To investigate the human brain activities for aesthetic processing, beautiful woman face picture and ugly buffoon face picture were applied. Twelve subjects were assigned the aesthetic processing task while the electroencephalogram (EEG) was recorded. Event-related brain potential (ERP) was required from the 32 scalp electrodes and the ugly buffoon picture produced larger amplitudes for the N1, P2, N2, and late slow wave components. Average ERP from the ugly buffoon picture were larger than that from the beautiful woman picture. The ERP signals shows that the ugly buffoon elite higher emotion waves than the beautiful woman face, because some expression is on the face of the buffoon. Then, chaos time series analysis was carried out to calculate the largest Lyapunov exponent using small data set method and the correlation dimension using G-P algorithm. The results show that the largest Lyapunov exponents of the ERP signals are greater than zero, which indicate that the ERP signals may be chaotic. The correlations dimensions coming from the beautiful woman picture are larger than that from the ugly buffoon picture. The comparison of the correlations dimensions shows that the beautiful face can excite the brain nerve cells. The research in the paper is a persuasive proof to the opinion that cerebrum's work is chaotic under some picture stimuli.
Time series analysis of Monte Carlo neutron transport calculations
NASA Astrophysics Data System (ADS)
Nease, Brian Robert
A time series based approach is applied to the Monte Carlo (MC) fission source distribution to calculate the non-fundamental mode eigenvalues of the system. The approach applies Principal Oscillation Patterns (POPs) to the fission source distribution, transforming the problem into a simple autoregressive order one (AR(1)) process. Proof is provided that the stationary MC process is linear to first order approximation, which is a requirement for the application of POPs. The autocorrelation coefficient of the resulting AR(1) process corresponds to the ratio of the desired mode eigenvalue to the fundamental mode eigenvalue. All modern k-eigenvalue MC codes calculate the fundamental mode eigenvalue, so the desired mode eigenvalue can be easily determined. The strength of this approach is contrasted against the Fission Matrix method (FMM) in terms of accuracy versus computer memory constraints. Multi-dimensional problems are considered since the approach has strong potential for use in reactor analysis, and the implementation of the method into production codes is discussed. Lastly, the appearance of complex eigenvalues is investigated and solutions are provided.
On the Fourier and Wavelet Analysis of Coronal Time Series
NASA Astrophysics Data System (ADS)
Auchère, F.; Froment, C.; Bocchialini, K.; Buchlin, E.; Solomon, J.
2016-07-01
Using Fourier and wavelet analysis, we critically re-assess the significance of our detection of periodic pulsations in coronal loops. We show that the proper identification of the frequency dependence and statistical properties of the different components of the power spectra provides a strong argument against the common practice of data detrending, which tends to produce spurious detections around the cut-off frequency of the filter. In addition, the white and red noise models built into the widely used wavelet code of Torrence & Compo cannot, in most cases, adequately represent the power spectra of coronal time series, thus also possibly causing false positives. Both effects suggest that several reports of periodic phenomena should be re-examined. The Torrence & Compo code nonetheless effectively computes rigorous confidence levels if provided with pertinent models of mean power spectra, and we describe the appropriate manner in which to call its core routines. We recall the meaning of the default confidence levels output from the code, and we propose new Monte-Carlo-derived levels that take into account the total number of degrees of freedom in the wavelet spectra. These improvements allow us to confirm that the power peaks that we detected have a very low probability of being caused by noise.
Detrended fluctuation analysis of laser Doppler flowmetry time series.
Esen, Ferhan; Aydin, Gülsün Sönmez; Esen, Hamza
2009-12-01
Detrended fluctuation analysis (DFA) of laser Doppler flow (LDF) time series appears to yield improved prognostic power in microvascular dysfunction, through calculation of the scaling exponent, alpha. In the present study the long lasting strenuous activity-induced change in microvascular function was evaluated by DFA in basketball players compared with sedentary control. Forearm skin blood flow was measured at rest and during local heating. Three scaling exponents, the slopes of the three regression lines, were identified corresponding to cardiac, cardio-respiratory and local factors. Local scaling exponent was always approximately one, alpha=1.01+/-0.15, in the control group and did not change with local heating. However, we found a broken line with two scaling exponents (alpha(1)=1.06+/-0.01 and alpha(2)=0.75+/-0.01) in basketball players. The broken line became a single line having one scaling exponent (alpha(T)=0.94+/-0.01) with local heating. The scaling exponents, alpha(2) and alpha(T), smaller than 1 indicate reduced long-range correlation in blood flow due to a loss of integration in local mechanisms and suggest endothelial dysfunction as the most likely candidate. Evaluation of microvascular function from a baseline LDF signal at rest is the superiority of DFA to other methods, spectral or not, that use the amplitude changes of evoked relative signal. PMID:19660479
Time series analysis of the cataclysmic variable V1101 Aquilae
NASA Astrophysics Data System (ADS)
Spahn, Alexander C.
This work reports on the application of various time series analysis techniques to a two month portion of the light curve of the cataclysmic variable V1101 Aquilae. The system is a Z Cam type dwarf nova with an orbital period of 4.089 hours and an active outburst cycle of 15.15 days due to a high mass transfer rate. The system's light curve also displays higher frequency variations, known as negative sumperhums, with a period of 3.891 hours and a period deficit of --5.1%. The amplitude of the negative superhumps varies as an inverse function of system brightness, with an amplitude of 0.70358 during outburst and 0.97718 during quiescence (relative flux units). These variations are believed to be caused by the contrast between the accretion disk and the bright spot. An O--?C diagram was constructed and reveals the system's evolution. In general, during the rise to outburst, the disk moment of inertia decreases as mass is lost from the disk, causing the precession period of the tilted disk to increase and with it the negative superhump period. The decline of outburst is associated with the opposite effects. While no standstills were observed in this data, they are present in the AAVSO data and the results agree with the conditions for Z Cam stars.
Time series analysis and the analysis of aquatic and riparian ecosystems
Milhous, R.T.
2003-01-01
Time series analysis of physical instream habitat and the riparian zone is not done as frequently as would be beneficial in understanding the fisheries aspects of the aquatic ecosystem. This paper presents two case studies have how time series analysis may be accomplished. Time series analysis is the analysis of the variation of the physical habitat or the hydro-period in the riparian zone (in many situations, the floodplain).
On fractal analysis of cardiac interbeat time series
NASA Astrophysics Data System (ADS)
Guzmán-Vargas, L.; Calleja-Quevedo, E.; Angulo-Brown, F.
2003-09-01
In recent years the complexity of a cardiac beat-to-beat time series has been taken as an auxiliary tool to identify the health status of human hearts. Several methods has been employed to characterize the time series complexity. In this work we calculate the fractal dimension of interbeat time series arising from three groups: 10 young healthy persons, 8 elderly healthy persons and 10 patients with congestive heart failures. Our numerical results reflect evident differences in the dynamic behavior corresponding to each group. We discuss these results within the context of the neuroautonomic control of heart rate dynamics. We also propose a numerical simulation which reproduce aging effects of heart rate behavior.
A multiscale approach to InSAR time series analysis
NASA Astrophysics Data System (ADS)
Simons, M.; Hetland, E. A.; Muse, P.; Lin, Y. N.; Dicaprio, C.; Rickerby, A.
2008-12-01
We describe a new technique to constrain time-dependent deformation from repeated satellite-based InSAR observations of a given region. This approach, which we call MInTS (Multiscale analysis of InSAR Time Series), relies on a spatial wavelet decomposition to permit the inclusion of distance based spatial correlations in the observations while maintaining computational tractability. This approach also permits a consistent treatment of all data independent of the presence of localized holes in any given interferogram. In essence, MInTS allows one to considers all data at the same time (as opposed to one pixel at a time), thereby taking advantage of both spatial and temporal characteristics of the deformation field. In terms of the temporal representation, we have the flexibility to explicitly parametrize known processes that are expected to contribute to a given set of observations (e.g., co-seismic steps and post-seismic transients, secular variations, seasonal oscillations, etc.). Our approach also allows for the temporal parametrization to includes a set of general functions (e.g., splines) in order to account for unexpected processes. We allow for various forms of model regularization using a cross-validation approach to select penalty parameters. The multiscale analysis allows us to consider various contributions (e.g., orbit errors) that may affect specific scales but not others. The methods described here are all embarrassingly parallel and suitable for implementation on a cluster computer. We demonstrate the use of MInTS using a large suite of ERS-1/2 and Envisat interferograms for Long Valley Caldera, and validate our results by comparing with ground-based observations.
Analytical framework for recurrence network analysis of time series
NASA Astrophysics Data System (ADS)
Donges, Jonathan F.; Heitzig, Jobst; Donner, Reik V.; Kurths, Jürgen
2012-04-01
Recurrence networks are a powerful nonlinear tool for time series analysis of complex dynamical systems. While there are already many successful applications ranging from medicine to paleoclimatology, a solid theoretical foundation of the method has still been missing so far. Here, we interpret an ɛ-recurrence network as a discrete subnetwork of a “continuous” graph with uncountably many vertices and edges corresponding to the system's attractor. This step allows us to show that various statistical measures commonly used in complex network analysis can be seen as discrete estimators of newly defined continuous measures of certain complex geometric properties of the attractor on the scale given by ɛ. In particular, we introduce local measures such as the ɛ-clustering coefficient, mesoscopic measures such as ɛ-motif density, path-based measures such as ɛ-betweennesses, and global measures such as ɛ-efficiency. This new analytical basis for the so far heuristically motivated network measures also provides an objective criterion for the choice of ɛ via a percolation threshold, and it shows that estimation can be improved by so-called node splitting invariant versions of the measures. We finally illustrate the framework for a number of archetypical chaotic attractors such as those of the Bernoulli and logistic maps, periodic and two-dimensional quasiperiodic motions, and for hyperballs and hypercubes by deriving analytical expressions for the novel measures and comparing them with data from numerical experiments. More generally, the theoretical framework put forward in this work describes random geometric graphs and other networks with spatial constraints, which appear frequently in disciplines ranging from biology to climate science.
Multiscale entropy analysis of complex physiologic time series.
Costa, Madalena; Goldberger, Ary L; Peng, C-K
2002-08-01
There has been considerable interest in quantifying the complexity of physiologic time series, such as heart rate. However, traditional algorithms indicate higher complexity for certain pathologic processes associated with random outputs than for healthy dynamics exhibiting long-range correlations. This paradox may be due to the fact that conventional algorithms fail to account for the multiple time scales inherent in healthy physiologic dynamics. We introduce a method to calculate multiscale entropy (MSE) for complex time series. We find that MSE robustly separates healthy and pathologic groups and consistently yields higher values for simulated long-range correlated noise compared to uncorrelated noise. PMID:12190613
Minimum entropy density method for the time series analysis
NASA Astrophysics Data System (ADS)
Lee, Jeong Won; Park, Joongwoo Brian; Jo, Hang-Hyun; Yang, Jae-Suk; Moon, Hie-Tae
2009-01-01
The entropy density is an intuitive and powerful concept to study the complicated nonlinear processes derived from physical systems. We develop the minimum entropy density method (MEDM) to detect the structure scale of a given time series, which is defined as the scale in which the uncertainty is minimized, hence the pattern is revealed most. The MEDM is applied to the financial time series of Standard and Poor’s 500 index from February 1983 to April 2006. Then the temporal behavior of structure scale is obtained and analyzed in relation to the information delivery time and efficient market hypothesis.
A Time-Series Analysis of Hispanic Unemployment.
ERIC Educational Resources Information Center
Defreitas, Gregory
1986-01-01
This study undertakes the first systematic time-series research on the cyclical patterns and principal determinants of Hispanic joblessness in the United States. The principal findings indicate that Hispanics tend to bear a disproportionate share of increases in unemployment during recessions. (Author/CT)
Time Series Analysis for the Drac River Basin (france)
NASA Astrophysics Data System (ADS)
Parra-Castro, K.; Donado-Garzon, L. D.; Rodriguez, E.
2013-12-01
This research is based on analyzing of discharge time-series in four stream flow gage stations located in the Drac River basin in France: (i) Guinguette Naturelle, (ii) Infernet, (iii) Parassat and the stream flow gage (iv) Villard Loubière. In addition, time-series models as the linear regression (single and multiple) and the MORDOR model were implemented to analyze the behavior the Drac River from year 1969 until year 2010. Twelve different models were implemented to assess the daily and monthly discharge time-series for the four flow gage stations. Moreover, five selection criteria were use to analyze the models: average division, variance division, the coefficient R2, Kling-Gupta Efficiency (KGE) and the Nash Number. The selection of the models was made to have the strongest models with an important level confidence. In this case, according to the best correlation between the time-series of stream flow gage stations and the best fitting models. Four of the twelve models were selected: two models for the stream flow gage station Guinguette Naturel, one for the station Infernet and one model for the station Villard Loubière. The R2 coefficients achieved were 0.87, 0.95, 0.85 and 0.87 respectively. Consequently, both confidence levels (the modeled and the empirical) were tested in the selected model, leading to the best fitting of both discharge time-series and models with the empirical confidence interval. Additionally, a procedure for validation of the models was conducted using the data for the year 2011, where extreme hydrologic and changes in hydrologic regimes events were identified. Furthermore, two different forms of estimating uncertainty through the use of confidence levels were studied: the modeled and the empirical confidence levels. This research was useful to update the used procedures and validate time-series in the four stream flow gage stations for the use of the company Électricité de France. Additionally, coefficients for both the models and
Complexity analysis of the turbulent environmental fluid flow time series
NASA Astrophysics Data System (ADS)
Mihailović, D. T.; Nikolić-Đorić, E.; Drešković, N.; Mimić, G.
2014-02-01
We have used the Kolmogorov complexities, sample and permutation entropies to quantify the randomness degree in river flow time series of two mountain rivers in Bosnia and Herzegovina, representing the turbulent environmental fluid, for the period 1926-1990. In particular, we have examined the monthly river flow time series from two rivers (the Miljacka and the Bosnia) in the mountain part of their flow and then calculated the Kolmogorov complexity (KL) based on the Lempel-Ziv Algorithm (LZA) (lower-KLL and upper-KLU), sample entropy (SE) and permutation entropy (PE) values for each time series. The results indicate that the KLL, KLU, SE and PE values in two rivers are close to each other regardless of the amplitude differences in their monthly flow rates. We have illustrated the changes in mountain river flow complexity by experiments using (i) the data set for the Bosnia River and (ii) anticipated human activities and projected climate changes. We have explored the sensitivity of considered measures in dependence on the length of time series. In addition, we have divided the period 1926-1990 into three subintervals: (a) 1926-1945, (b) 1946-1965, (c) 1966-1990, and calculated the KLL, KLU, SE, PE values for the various time series in these subintervals. It is found that during the period 1946-1965, there is a decrease in their complexities, and corresponding changes in the SE and PE, in comparison to the period 1926-1990. This complexity loss may be primarily attributed to (i) human interventions, after the Second World War, on these two rivers because of their use for water consumption and (ii) climate change in recent times.
A Multiscale Approach to InSAR Time Series Analysis
NASA Astrophysics Data System (ADS)
Simons, M.; Hetland, E. A.; Muse, P.; Lin, Y.; Dicaprio, C. J.
2009-12-01
We describe progress in the development of MInTS (Multiscale analysis of InSAR Time Series), an approach to constructed self-consistent time-dependent deformation observations from repeated satellite-based InSAR images of a given region. MInTS relies on a spatial wavelet decomposition to permit the inclusion of distance based spatial correlations in the observations while maintaining computational tractability. In essence, MInTS allows one to considers all data at the same time as opposed to one pixel at a time, thereby taking advantage of both spatial and temporal characteristics of the deformation field. This approach also permits a consistent treatment of all data independent of the presence of localized holes due to unwrapping issues in any given interferogram. Specifically, the presence of holes is accounted for through a weighting scheme that accounts for the extent of actual data versus the area of holes associated with any given wavelet. In terms of the temporal representation, we have the flexibility to explicitly parametrize known processes that are expected to contribute to a given set of observations (e.g., co-seismic steps and post-seismic transients, secular variations, seasonal oscillations, etc.). Our approach also allows for the temporal parametrization to include a set of general functions in order to account for unexpected processes. We allow for various forms of model regularization using a cross-validation approach to select penalty parameters. We also experiment with the use of sparsity inducing regularization as a way to select from a large dictionary of time functions. The multiscale analysis allows us to consider various contributions (e.g., orbit errors) that may affect specific scales but not others. The methods described here are all embarrassingly parallel and suitable for implementation on a cluster computer. We demonstrate the use of MInTS using a large suite of ERS-1/2 and Envisat interferograms for Long Valley Caldera, and validate
Time series analysis as a tool for karst water management
NASA Astrophysics Data System (ADS)
Fournier, Matthieu; Massei, Nicolas; Duran, Léa
2015-04-01
Karst hydrosystems are well known for their vulnerability to turbidity due to their complex and unique characteristics which make them very different from other aquifers. Moreover, many parameters can affect their functioning. It makes the characterization of their vulnerability difficult and needs the use of statistical analyses Time series analyses on turbidity, electrical conductivity and water discharge datasets, such as correlation and spectral analyses, have proven to be useful in improving our understanding of karst systems. However, the loss of information on time localization is a major drawback of those Fourier spectral methods; this problem has been overcome by the development of wavelet analysis (continuous or discrete) for hydrosystems offering the possibility to better characterize the complex modalities of variation inherent to non stationary processes. Nevertheless, from wavelet transform, signal is decomposed on several continuous wavelet signals which cannot be true with local-time processes frequently observed in karst aquifer. More recently, a new approach associating empirical mode decomposition and the Hilbert transform was presented for hydrosystems. It allows an orthogonal decomposition of the signal analyzed and provides a more accurate estimation of changing variability scales across time for highly transient signals. This study aims to identify the natural and anthropogenic parameters which control turbidity released at a well for drinking water supply. The well is located in the chalk karst aquifer near the Seine river at 40 km of the Seine estuary in western Paris Basin. At this location, tidal variations greatly affect the level of the water in the Seine. Continuous wavelet analysis on turbidity dataset have been used to decompose turbidity release at the well into three components i) the rain event periods, ii) the pumping periods and iii) the tidal range of Seine river. Time-domain reconstruction by inverse wavelet transform allows
Modeling Philippine Stock Exchange Composite Index Using Time Series Analysis
NASA Astrophysics Data System (ADS)
Gayo, W. S.; Urrutia, J. D.; Temple, J. M. F.; Sandoval, J. R. D.; Sanglay, J. E. A.
2015-06-01
This study was conducted to develop a time series model of the Philippine Stock Exchange Composite Index and its volatility using the finite mixture of ARIMA model with conditional variance equations such as ARCH, GARCH, EG ARCH, TARCH and PARCH models. Also, the study aimed to find out the reason behind the behaviorof PSEi, that is, which of the economic variables - Consumer Price Index, crude oil price, foreign exchange rate, gold price, interest rate, money supply, price-earnings ratio, Producers’ Price Index and terms of trade - can be used in projecting future values of PSEi and this was examined using Granger Causality Test. The findings showed that the best time series model for Philippine Stock Exchange Composite index is ARIMA(1,1,5) - ARCH(1). Also, Consumer Price Index, crude oil price and foreign exchange rate are factors concluded to Granger cause Philippine Stock Exchange Composite Index.
Multifractal analysis of time series generated by discrete Ito equations
Telesca, Luciano; Czechowski, Zbigniew; Lovallo, Michele
2015-06-15
In this study, we show that discrete Ito equations with short-tail Gaussian marginal distribution function generate multifractal time series. The multifractality is due to the nonlinear correlations, which are hidden in Markov processes and are generated by the interrelation between the drift and the multiplicative stochastic forces in the Ito equation. A link between the range of the generalized Hurst exponents and the mean of the squares of all averaged net forces is suggested.
Financial time series analysis based on information categorization method
NASA Astrophysics Data System (ADS)
Tian, Qiang; Shang, Pengjian; Feng, Guochen
2014-12-01
The paper mainly applies the information categorization method to analyze the financial time series. The method is used to examine the similarity of different sequences by calculating the distances between them. We apply this method to quantify the similarity of different stock markets. And we report the results of similarity in US and Chinese stock markets in periods 1991-1998 (before the Asian currency crisis), 1999-2006 (after the Asian currency crisis and before the global financial crisis), and 2007-2013 (during and after global financial crisis) by using this method. The results show the difference of similarity between different stock markets in different time periods and the similarity of the two stock markets become larger after these two crises. Also we acquire the results of similarity of 10 stock indices in three areas; it means the method can distinguish different areas' markets from the phylogenetic trees. The results show that we can get satisfactory information from financial markets by this method. The information categorization method can not only be used in physiologic time series, but also in financial time series.
Dynamical Analysis and Visualization of Tornadoes Time Series
2015-01-01
In this paper we analyze the behavior of tornado time-series in the U.S. from the perspective of dynamical systems. A tornado is a violently rotating column of air extending from a cumulonimbus cloud down to the ground. Such phenomena reveal features that are well described by power law functions and unveil characteristics found in systems with long range memory effects. Tornado time series are viewed as the output of a complex system and are interpreted as a manifestation of its dynamics. Tornadoes are modeled as sequences of Dirac impulses with amplitude proportional to the events size. First, a collection of time series involving 64 years is analyzed in the frequency domain by means of the Fourier transform. The amplitude spectra are approximated by power law functions and their parameters are read as an underlying signature of the system dynamics. Second, it is adopted the concept of circular time and the collective behavior of tornadoes analyzed. Clustering techniques are then adopted to identify and visualize the emerging patterns. PMID:25790281
Dynamical analysis and visualization of tornadoes time series.
Lopes, António M; Tenreiro Machado, J A
2015-01-01
In this paper we analyze the behavior of tornado time-series in the U.S. from the perspective of dynamical systems. A tornado is a violently rotating column of air extending from a cumulonimbus cloud down to the ground. Such phenomena reveal features that are well described by power law functions and unveil characteristics found in systems with long range memory effects. Tornado time series are viewed as the output of a complex system and are interpreted as a manifestation of its dynamics. Tornadoes are modeled as sequences of Dirac impulses with amplitude proportional to the events size. First, a collection of time series involving 64 years is analyzed in the frequency domain by means of the Fourier transform. The amplitude spectra are approximated by power law functions and their parameters are read as an underlying signature of the system dynamics. Second, it is adopted the concept of circular time and the collective behavior of tornadoes analyzed. Clustering techniques are then adopted to identify and visualize the emerging patterns. PMID:25790281
Time-Series Analysis of Supergranule Characterstics at Solar Minimum
NASA Technical Reports Server (NTRS)
Williams, Peter E.; Pesnell, W. Dean
2013-01-01
Sixty days of Doppler images from the Solar and Heliospheric Observatory (SOHO) / Michelson Doppler Imager (MDI) investigation during the 1996 and 2008 solar minima have been analyzed to show that certain supergranule characteristics (size, size range, and horizontal velocity) exhibit fluctuations of three to five days. Cross-correlating parameters showed a good, positive correlation between supergranulation size and size range, and a moderate, negative correlation between size range and velocity. The size and velocity do exhibit a moderate, negative correlation, but with a small time lag (less than 12 hours). Supergranule sizes during five days of co-temporal data from MDI and the Solar Dynamics Observatory (SDO) / Helioseismic Magnetic Imager (HMI) exhibit similar fluctuations with a high level of correlation between them. This verifies the solar origin of the fluctuations, which cannot be caused by instrumental artifacts according to these observations. Similar fluctuations are also observed in data simulations that model the evolution of the MDI Doppler pattern over a 60-day period. Correlations between the supergranule size and size range time-series derived from the simulated data are similar to those seen in MDI data. A simple toy-model using cumulative, uncorrelated exponential growth and decay patterns at random emergence times produces a time-series similar to the data simulations. The qualitative similarities between the simulated and the observed time-series suggest that the fluctuations arise from stochastic processes occurring within the solar convection zone. This behavior, propagating to surface manifestations of supergranulation, may assist our understanding of magnetic-field-line advection, evolution, and interaction.
Time series analysis of transient chaos: Theory and experiment
Janosi, I.M.; Tel, T.
1996-06-01
A simple method is described how nonattracting chaotic sets can be reconstructed from time series by gluing those pieces of many transiently chaotic signals together that come close to this invariant set. The method is illustrated by both a map of well known dynamics, the H{acute e}non map, and a signal obtained from an experiment, the NMR laser. The strange saddle responsible for the transient chaotic behavior is reconstructed and its characteristics like dimension, Lyapunov exponent, and correlation function are determined. {copyright} {ital 1996 American Institute of Physics.}
Visibility graph network analysis of gold price time series
NASA Astrophysics Data System (ADS)
Long, Yu
2013-08-01
Mapping time series into a visibility graph network, the characteristics of the gold price time series and return temporal series, and the mechanism underlying the gold price fluctuation have been explored from the perspective of complex network theory. The network degree distribution characters, which change from power law to exponent law when the series was shuffled from original sequence, and the average path length characters, which change from L∼lnN into lnL∼lnN as the sequence was shuffled, demonstrate that price series and return series are both long-rang dependent fractal series. The relations of Hurst exponent to the power-law exponent of degree distribution demonstrate that the logarithmic price series is a fractal Brownian series and the logarithmic return series is a fractal Gaussian series. Power-law exponents of degree distribution in a time window changing with window moving demonstrates that a logarithmic gold price series is a multifractal series. The Power-law average clustering coefficient demonstrates that the gold price visibility graph is a hierarchy network. The hierarchy character, in light of the correspondence of graph to price fluctuation, means that gold price fluctuation is a hierarchy structure, which appears to be in agreement with Elliot’s experiential Wave Theory on stock price fluctuation, and the local-rule growth theory of a hierarchy network means that the hierarchy structure of gold price fluctuation originates from persistent, short term factors, such as short term speculation.
A new complexity measure for time series analysis and classification
NASA Astrophysics Data System (ADS)
Nagaraj, Nithin; Balasubramanian, Karthi; Dey, Sutirth
2013-07-01
Complexity measures are used in a number of applications including extraction of information from data such as ecological time series, detection of non-random structure in biomedical signals, testing of random number generators, language recognition and authorship attribution etc. Different complexity measures proposed in the literature like Shannon entropy, Relative entropy, Lempel-Ziv, Kolmogrov and Algorithmic complexity are mostly ineffective in analyzing short sequences that are further corrupted with noise. To address this problem, we propose a new complexity measure ETC and define it as the "Effort To Compress" the input sequence by a lossless compression algorithm. Here, we employ the lossless compression algorithm known as Non-Sequential Recursive Pair Substitution (NSRPS) and define ETC as the number of iterations needed for NSRPS to transform the input sequence to a constant sequence. We demonstrate the utility of ETC in two applications. ETC is shown to have better correlation with Lyapunov exponent than Shannon entropy even with relatively short and noisy time series. The measure also has a greater rate of success in automatic identification and classification of short noisy sequences, compared to entropy and a popular measure based on Lempel-Ziv compression (implemented by Gzip).
Time series analysis of molecular dynamics simulation using wavelet
NASA Astrophysics Data System (ADS)
Toda, Mikito
2012-08-01
A new method is presented to extract nonstationary features of slow collective motion toward time series data of molecular dynamics simulation for proteins. The method consists of the following two steps: (1) the wavelet transformation and (2) the singular value decomposition (SVD). The wavelet transformation enables us to characterize time varying features of oscillatory motions and SVD enables us to reduce the degrees of freedom of the movement. We apply the method to molecular dynamics simulation of various proteins such as Adenylate Kinase from Escherichia coli (AKE) and Thermomyces lanuginosa lipase (TLL). Moreover, we introduce indexes to characterize collective motion of proteins. These indexes provide us with information of nonstationary deformation of protein structures. We discuss future prospects of our study involving "intrinsically disordered proteins".
Feasibility of Estimating Relative Nutrient Contributions of Agriculture using MODIS Time Series
NASA Technical Reports Server (NTRS)
Ross, Kenton W.; Gasser, Gerald; Spiering, Bruce
2008-01-01
Around the Gulf of Mexico, high-input crops in several regions make a significant contribution to nutrient loading of small to medium estuaries and to the near-shore Gulf. Some crops cultivated near the coast include sorghum in Texas, rice in Texas and Louisiana, sugarcane in Florida and Louisiana, citrus orchards in Florida, pecan orchards in Mississippi and Alabama, and heavy sod and ornamental production around Mobile and Tampa Bay. In addition to crops, management of timberlands in proximity to the coasts also plays a role in nutrient loading. In the summer of 2008, a feasibility project is planned to explore the use of NASA data to enhance the spatial and temporal resolution of near-coast nutrient source information available to the coastal community. The purpose of this project is to demonstrate the viability of nutrient source information products applicable to small to medium watersheds surrounding the Gulf of Mexico. Conceptually, these products are intended to complement estuarine nutrient monitoring.
NASA Technical Reports Server (NTRS)
Ross, Kenton W.; Gasser, Gerald; Spiering, Bruce
2010-01-01
Around the Gulf of Mexico, high-input crops in several regions make a significant contribution to nutrient loading of small to medium estuaries and to the near-shore Gulf. Some crops cultivated near the coast include sorghum in Texas, rice in Texas and Louisiana, sugarcane in Florida and Louisiana, citrus orchards in Florida, pecan orchards in Mississippi and Alabama, and heavy sod and ornamental production around Mobile and Tampa Bay. In addition to crops, management of timberlands in proximity to the coasts also plays a role in nutrient loading. In the summer of 2008, a feasibility project is planned to explore the use of NASA data to enhance the spatial and temporal resolution of near-coast nutrient source information available to the coastal community. The purpose of this project is to demonstrate the viability of nutrient source information products applicable to small to medium watersheds surrounding the Gulf of Mexico. Conceptually, these products are intended to complement estuarine nutrient monitoring.
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.
Physiological time-series analysis: what does regularity quantify?
NASA Technical Reports Server (NTRS)
Pincus, S. M.; Goldberger, A. L.
1994-01-01
Approximate entropy (ApEn) is a recently developed statistic quantifying regularity and complexity that appears to have potential application to a wide variety of physiological and clinical time-series data. The focus here is to provide a better understanding of ApEn to facilitate its proper utilization, application, and interpretation. After giving the formal mathematical description of ApEn, we provide a multistep description of the algorithm as applied to two contrasting clinical heart rate data sets. We discuss algorithm implementation and interpretation and introduce a general mathematical hypothesis of the dynamics of a wide class of diseases, indicating the utility of ApEn to test this hypothesis. We indicate the relationship of ApEn to variability measures, the Fourier spectrum, and algorithms motivated by study of chaotic dynamics. We discuss further mathematical properties of ApEn, including the choice of input parameters, statistical issues, and modeling considerations, and we conclude with a section on caveats to ensure correct ApEn utilization.
Presentations to Emergency Departments for COPD: A Time Series Analysis
Youngson, Erik; Rowe, Brian H.
2016-01-01
Background. Chronic obstructive pulmonary disease (COPD) is a common respiratory condition characterized by progressive dyspnea and acute exacerbations which may result in emergency department (ED) presentations. This study examines monthly rates of presentations to EDs in one Canadian province. Methods. Presentations for COPD made by individuals aged ≥55 years during April 1999 to March 2011 were extracted from provincial databases. Data included age, sex, and health zone of residence (North, Central, South, and urban). Crude rates were calculated. Seasonal autoregressive integrated moving average (SARIMA) time series models were developed. Results. ED presentations for COPD totalled 188,824 and the monthly rate of presentation remained relatively stable (from 197.7 to 232.6 per 100,000). Males and seniors (≥65 years) comprised 52.2% and 73.7% of presentations, respectively. The ARIMA(1,0, 0) × (1,0, 1)12 model was appropriate for the overall rate of presentations and for each sex and seniors. Zone specific models showed relatively stable or decreasing rates; the North zone had an increasing trend. Conclusions. ED presentation rates for COPD have been relatively stable in Alberta during the past decade. However, their increases in northern regions deserve further exploration. The SARIMA models quantified the temporal patterns and can help planning future health care service needs. PMID:27445514
Presentations to Emergency Departments for COPD: A Time Series Analysis.
Rosychuk, Rhonda J; Youngson, Erik; Rowe, Brian H
2016-01-01
Background. Chronic obstructive pulmonary disease (COPD) is a common respiratory condition characterized by progressive dyspnea and acute exacerbations which may result in emergency department (ED) presentations. This study examines monthly rates of presentations to EDs in one Canadian province. Methods. Presentations for COPD made by individuals aged ≥55 years during April 1999 to March 2011 were extracted from provincial databases. Data included age, sex, and health zone of residence (North, Central, South, and urban). Crude rates were calculated. Seasonal autoregressive integrated moving average (SARIMA) time series models were developed. Results. ED presentations for COPD totalled 188,824 and the monthly rate of presentation remained relatively stable (from 197.7 to 232.6 per 100,000). Males and seniors (≥65 years) comprised 52.2% and 73.7% of presentations, respectively. The ARIMA(1,0, 0) × (1,0, 1)12 model was appropriate for the overall rate of presentations and for each sex and seniors. Zone specific models showed relatively stable or decreasing rates; the North zone had an increasing trend. Conclusions. ED presentation rates for COPD have been relatively stable in Alberta during the past decade. However, their increases in northern regions deserve further exploration. The SARIMA models quantified the temporal patterns and can help planning future health care service needs. PMID:27445514
Nonlinear time series analysis of normal and pathological human walking
NASA Astrophysics Data System (ADS)
Dingwell, Jonathan B.; Cusumano, Joseph P.
2000-12-01
Characterizing locomotor dynamics is essential for understanding the neuromuscular control of locomotion. In particular, quantifying dynamic stability during walking is important for assessing people who have a greater risk of falling. However, traditional biomechanical methods of defining stability have not quantified the resistance of the neuromuscular system to perturbations, suggesting that more precise definitions are required. For the present study, average maximum finite-time Lyapunov exponents were estimated to quantify the local dynamic stability of human walking kinematics. Local scaling exponents, defined as the local slopes of the correlation sum curves, were also calculated to quantify the local scaling structure of each embedded time series. Comparisons were made between overground and motorized treadmill walking in young healthy subjects and between diabetic neuropathic (NP) patients and healthy controls (CO) during overground walking. A modification of the method of surrogate data was developed to examine the stochastic nature of the fluctuations overlying the nominally periodic patterns in these data sets. Results demonstrated that having subjects walk on a motorized treadmill artificially stabilized their natural locomotor kinematics by small but statistically significant amounts. Furthermore, a paradox previously present in the biomechanical literature that resulted from mistakenly equating variability with dynamic stability was resolved. By slowing their self-selected walking speeds, NP patients adopted more locally stable gait patterns, even though they simultaneously exhibited greater kinematic variability than CO subjects. Additionally, the loss of peripheral sensation in NP patients was associated with statistically significant differences in the local scaling structure of their walking kinematics at those length scales where it was anticipated that sensory feedback would play the greatest role. Lastly, stride-to-stride fluctuations in the
Time series analysis of diverse extreme phenomena: universal features
NASA Astrophysics Data System (ADS)
Eftaxias, K.; Balasis, G.
2012-04-01
The field of study of complex systems holds that the dynamics of complex systems are founded on universal principles that may used to describe a great variety of scientific and technological approaches of different types of natural, artificial, and social systems. We suggest that earthquake, epileptic seizures, solar flares, and magnetic storms dynamics can be analyzed within similar mathematical frameworks. A central property of aforementioned extreme events generation is the occurrence of coherent large-scale collective behavior with very rich structure, resulting from repeated nonlinear interactions among the corresponding constituents. Consequently, we apply the Tsallis nonextensive statistical mechanics as it proves an appropriate framework in order to investigate universal principles of their generation. First, we examine the data in terms of Tsallis entropy aiming to discover common "pathological" symptoms of transition to a significant shock. By monitoring the temporal evolution of the degree of organization in time series we observe similar distinctive features revealing significant reduction of complexity during their emergence. Second, a model for earthquake dynamics coming from a nonextensive Tsallis formalism, starting from first principles, has been recently introduced. This approach leads to an energy distribution function (Gutenberg-Richter type law) for the magnitude distribution of earthquakes, providing an excellent fit to seismicities generated in various large geographic areas usually identified as seismic regions. We show that this function is able to describe the energy distribution (with similar non-extensive q-parameter) of solar flares, magnetic storms, epileptic and earthquake shocks. The above mentioned evidence of a universal statistical behavior suggests the possibility of a common approach for studying space weather, earthquakes and epileptic seizures.
New insights into time series analysis. I. Correlated observations
NASA Astrophysics Data System (ADS)
Ferreira Lopes, C. E.; Cross, N. J. G.
2016-02-01
Context. The first step when investigating time varying data is the detection of any reliable changes in star brightness. This step is crucial to decreasing the processing time by reducing the number of sources processed in later, slower steps. Variability indices and their combinations have been used to identify variability patterns and to select non-stochastic variations, but the separation of true variables is hindered because of wavelength-correlated systematics of instrumental and atmospheric origin or due to possible data reduction anomalies. Aims: The main aim is to review the current inventory of correlation variability indices and measure the efficiency for selecting non-stochastic variations in photometric data. Methods: We test new and standard data-mining methods for correlated data using public time-domain data from the WFCAM Science Archive (WSA). This archive contains multi-wavelength calibration data (WFCAMCAL) for 216,722 point sources, with at least ten unflagged epochs in any of five filters (YZJHK), which were used to test the different indices against. We improve the panchromatic variability indices and introduce a new set of variability indices for preselecting variable star candidates. Using the WFCAMCAL Variable Star Catalogue (WVSC1) we delimit the efficiency of each variability index. Moreover we test new insights about these indices to improve the efficiency of detection of time-series data dominated by correlated variations. Results: We propose five new variability indices that display high efficiency for the detection of variable stars. We determine the best way to select variable stars with these indices and the current tool inventory. In addition, we propose a universal analytical expression to select likely variables using the fraction of fluctuations on these indices (ffluc). The ffluc can be used as a universal way to analyse photometric data since it displays a only weak dependency with the instrument properties. The variability
On-line analysis of reactor noise using time-series analysis
McGevna, V.G.
1981-10-01
A method to allow use of time series analysis for on-line noise analysis has been developed. On-line analysis of noise in nuclear power reactors has been limited primarily to spectral analysis and related frequency domain techniques. Time series analysis has many distinct advantages over spectral analysis in the automated processing of reactor noise. However, fitting an autoregressive-moving average (ARMA) model to time series data involves non-linear least squares estimation. Unless a high speed, general purpose computer is available, the calculations become too time consuming for on-line applications. To eliminate this problem, a special purpose algorithm was developed for fitting ARMA models. While it is based on a combination of steepest descent and Taylor series linearization, properties of the ARMA model are used so that the auto- and cross-correlation functions can be used to eliminate the need for estimating derivatives.
On the Interpretation of Running Trends as Summary Statistics for Time Series Analysis
NASA Astrophysics Data System (ADS)
Vigo, Isabel M.; Trottini, Mario; Belda, Santiago
2016-04-01
In recent years, running trends analysis (RTA) has been widely used in climate applied research as summary statistics for time series analysis. There is no doubt that RTA might be a useful descriptive tool, but despite its general use in applied research, precisely what it reveals about the underlying time series is unclear and, as a result, its interpretation is unclear too. This work contributes to such interpretation in two ways: 1) an explicit formula is obtained for the set of time series with a given series of running trends, making it possible to show that running trends, alone, perform very poorly as summary statistics for time series analysis; and 2) an equivalence is established between RTA and the estimation of a (possibly nonlinear) trend component of the underlying time series using a weighted moving average filter. Such equivalence provides a solid ground for RTA implementation and interpretation/validation.
NASA Astrophysics Data System (ADS)
Liu, Bin; Dai, Wujiao; Peng, Wei; Meng, Xiaolin
2015-11-01
GPS has been widely used in the field of geodesy and geodynamics thanks to its technology development and the improvement of positioning accuracy. A time series observed by GPS in vertical direction usually contains tectonic signals, non-tectonic signals, residual atmospheric delay, measurement noise, etc. Analyzing these information is the basis of crustal deformation research. Furthermore, analyzing the GPS time series and extracting the non-tectonic information are helpful to study the effect of various geophysical events. Principal component analysis (PCA) is an effective tool for spatiotemporal filtering and GPS time series analysis. But as it is unable to extract statistically independent components, PCA is unfavorable for achieving the implicit information in time series. Independent component analysis (ICA) is a statistical method of blind source separation (BSS) and can separate original signals from mixed observations. In this paper, ICA is used as a spatiotemporal filtering method to analyze the spatial and temporal features of vertical GPS coordinate time series in the UK and Sichuan-Yunnan region in China. Meanwhile, the contributions from atmospheric and soil moisture mass loading are evaluated. The analysis of the relevance between the independent components and mass loading with their spatial distribution shows that the signals extracted by ICA have a strong correlation with the non-tectonic deformation, indicating that ICA has a better performance in spatiotemporal analysis.
NASA Astrophysics Data System (ADS)
Yan, Jun; Dong, Danan; Chen, Wen
2016-04-01
Due to the development of GNSS technology and the improvement of its positioning accuracy, observational data obtained by GNSS is widely used in Earth space geodesy and geodynamics research. Whereas the GNSS time series data of observation stations contains a plenty of information. This includes geographical space changes, deformation of the Earth, the migration of subsurface material, instantaneous deformation of the Earth, weak deformation and other blind signals. In order to decompose some instantaneous deformation underground, weak deformation and other blind signals hided in GNSS time series, we apply Independent Component Analysis (ICA) to daily station coordinate time series of the Southern California Integrated GPS Network. As ICA is based on the statistical characteristics of the observed signal. It uses non-Gaussian and independence character to process time series to obtain the source signal of the basic geophysical events. In term of the post-processing procedure of precise time-series data by GNSS, this paper examines GNSS time series using the principal component analysis (PCA) module of QOCA and ICA algorithm to separate the source signal. Then we focus on taking into account of these two signal separation technologies, PCA and ICA, for separating original signal that related geophysical disturbance changes from the observed signals. After analyzing these separation process approaches, we demonstrate that in the case of multiple factors, PCA exists ambiguity in the separation of source signals, that is the result related is not clear, and ICA will be better than PCA, which means that dealing with GNSS time series that the combination of signal source is unknown is suitable to use ICA.
NASA Astrophysics Data System (ADS)
Peng, Wei; Dai, Wujiao; Santerre, Rock; Cai, Changsheng; Kuang, Cuilin
2016-05-01
Daily vertical coordinate time series of Global Navigation Satellite System (GNSS) stations usually contains tectonic and non-tectonic deformation signals, residual atmospheric delay signals, measurement noise, etc. In geophysical studies, it is very important to separate various geophysical signals from the GNSS time series to truthfully reflect the effect of mass loadings on crustal deformation. Based on the independence of mass loadings, we combine the Ensemble Empirical Mode Decomposition (EEMD) with the Phase Space Reconstruction-based Independent Component Analysis (PSR-ICA) method to analyze the vertical time series of GNSS reference stations. In the simulation experiment, the seasonal non-tectonic signal is simulated by the sum of the correction of atmospheric mass loading and soil moisture mass loading. The simulated seasonal non-tectonic signal can be separated into two independent signals using the PSR-ICA method, which strongly correlated with atmospheric mass loading and soil moisture mass loading, respectively. Likewise, in the analysis of the vertical time series of GNSS reference stations of Crustal Movement Observation Network of China (CMONOC), similar results have been obtained using the combined EEMD and PSR-ICA method. All these results indicate that the EEMD and PSR-ICA method can effectively separate the independent atmospheric and soil moisture mass loading signals and illustrate the significant cause of the seasonal variation of GNSS vertical time series in the mainland of China.
NASA Astrophysics Data System (ADS)
Tang, You-Fu; Liu, Shu-Lin; Jiang, Rui-Hong; Liu, Ying-Hui
2013-03-01
We study the correlation between detrended fluctuation analysis (DFA) and the Lempel-Ziv complexity (LZC) in nonlinear time series analysis in this paper. Typical dynamic systems including a logistic map and a Duffing model are investigated. Moreover, the influence of Gaussian random noise on both the DFA and LZC are analyzed. The results show a high correlation between the DFA and LZC, which can quantify the non-stationarity and the nonlinearity of the time series, respectively. With the enhancement of the random component, the exponent a and the normalized complexity index C show increasing trends. In addition, C is found to be more sensitive to the fluctuation in the nonlinear time series than α. Finally, the correlation between the DFA and LZC is applied to the extraction of vibration signals for a reciprocating compressor gas valve, and an effective fault diagnosis result is obtained.
Estimating Reliability of Disturbances in Satellite Time Series Data Based on Statistical Analysis
NASA Astrophysics Data System (ADS)
Zhou, Z.-G.; Tang, P.; Zhou, M.
2016-06-01
Normally, the status of land cover is inherently dynamic and changing continuously on temporal scale. However, disturbances or abnormal changes of land cover — caused by such as forest fire, flood, deforestation, and plant diseases — occur worldwide at unknown times and locations. Timely detection and characterization of these disturbances is of importance for land cover monitoring. Recently, many time-series-analysis methods have been developed for near real-time or online disturbance detection, using satellite image time series. However, the detection results were only labelled with "Change/ No change" by most of the present methods, while few methods focus on estimating reliability (or confidence level) of the detected disturbances in image time series. To this end, this paper propose a statistical analysis method for estimating reliability of disturbances in new available remote sensing image time series, through analysis of full temporal information laid in time series data. The method consists of three main steps. (1) Segmenting and modelling of historical time series data based on Breaks for Additive Seasonal and Trend (BFAST). (2) Forecasting and detecting disturbances in new time series data. (3) Estimating reliability of each detected disturbance using statistical analysis based on Confidence Interval (CI) and Confidence Levels (CL). The method was validated by estimating reliability of disturbance regions caused by a recent severe flooding occurred around the border of Russia and China. Results demonstrated that the method can estimate reliability of disturbances detected in satellite image with estimation error less than 5% and overall accuracy up to 90%.
Catchment classification based on a comparative analysis of time series of natural tracers
NASA Astrophysics Data System (ADS)
Lehr, Christian; Lischeid, Gunnar; Tetzlaff, Doerthe
2014-05-01
Catchments do not only smooth the precipitation signal into the discharge hydrograph, but transform also chemical signals (e.g. contaminations or nutrients) in a characteristic way. Under the assumption of an approximately homogeneous input signal of a conservative tracer in the catchment the transformation of the signal at different locations can be used to infer hydrological properties of the catchment. For this study comprehensive data on geology, soils, topography, land use, etc. as well as hydrological knowledge about transit times, mixing ratio of base flow, etc. is available for the catchment of the river Dee (1849 km2) in Scotland, UK. The Dee has its origin in the Cairngorm Mountains in Central Scotland and flows towards the eastern coast of Scotland where it ends in the Northern Sea at Aberdeen. From the source in the west to the coast in the east there is a distinct decrease in precipitation and altitude. For one year water quality in the Dee has been sampled biweekly at 59 sites along the main stem of the river and outflows of a number of tributaries. A nonlinear variant of Principal Component Analysis (Isometric Feature Mapping) has been applied on time series of different chemical parameters that were assumed to be relative conservative and applicable as natural tracers. Here, the information in the time series was not used to analyse the temporal development at the different sites, but in a snapshot kind of approach, the spatial expression of the different solutes at the 26 sampling dates. For all natural tracers the first component depicted > 89 % of the variance in the series. Subsequently, the spatial expression of the first component was related to the spatial patterns of the catchment characteristics. The presented approach allows to characterise a catchment in a spatial discrete way according to the hydrologically active properties of the catchment on the landscape scale, which is often the scale of interest for water managing purposes.
CCD Observing and Dynamical Time Series Analysis of Active Galactic Nuclei.
NASA Astrophysics Data System (ADS)
Nair, Achotham Damodaran
1995-01-01
The properties, working and operations procedure of the Charge Coupled Device (CCD) at the 30" telescope at Rosemary Hill Observatory (RHO) are discussed together with the details of data reduction. Several nonlinear techniques of time series analysis, based on the behavior of the nearest neighbors, have been used to analyze the time series of the quasar 3C 345. A technique using Artificial Neural Networks based on prediction of the time series is used to study the dynamical properties of 3C 345. Finally, a heuristic model for variability of Active Galactic Nuclei is discussed.
NASA Astrophysics Data System (ADS)
Visser, H.; Molenaar, J.
1995-05-01
The detection of trends in climatological data has become central to the discussion on climate change due to the enhanced greenhouse effect. To prove detection, a method is needed (i) to make inferences on significant rises or declines in trends, (ii) to take into account natural variability in climate series, and (iii) to compare output from GCMs with the trends in observed climate data. To meet these requirements, flexible mathematical tools are needed. A structural time series model is proposed with which a stochastic trend, a deterministic trend, and regression coefficients can be estimated simultaneously. The stochastic trend component is described using the class of ARIMA models. The regression component is assumed to be linear. However, the regression coefficients corresponding with the explanatory variables may be time dependent to validate this assumption. The mathematical technique used to estimate this trend-regression model is the Kaiman filter. The main features of the filter are discussed.Examples of trend estimation are given using annual mean temperatures at a single station in the Netherlands (1706-1990) and annual mean temperatures at Northern Hemisphere land stations (1851-1990). The inclusion of explanatory variables is shown by regressing the latter temperature series on four variables: Southern Oscillation index (SOI), volcanic dust index (VDI), sunspot numbers (SSN), and a simulated temperature signal, induced by increasing greenhouse gases (GHG). In all analyses, the influence of SSN on global temperatures is found to be negligible. The correlations between temperatures and SOI and VDI appear to be negative. For SOI, this correlation is significant, but for VDI it is not, probably because of a lack of volcanic eruptions during the sample period. The relation between temperatures and GHG is positive, which is in agreement with the hypothesis of a warming climate because of increasing levels of greenhouse gases. The prediction performance of
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
Time series analysis of hydraulic head and strain of subsurface formations in the Kanto Plain, Japan
NASA Astrophysics Data System (ADS)
Aichi, Masaatsu
2015-04-01
The hydraulic head and strain of subsurface formations have been monitored more than several decades in the Kanto Plain, Japan. Time series analysis of these data revealed that the relation between hydraulic head and strain observed in some monitoring wells could be modeled by linear poroelasticity. Based on the relations of time series data, the poroelastic coefficients were estimated. The obtained values were consistent with those from laboratory experiments reported in literatures.
NASA Astrophysics Data System (ADS)
Cavers, M. S.; Vasudevan, K.
2015-10-01
Directed graph representation of a Markov chain model to study global earthquake sequencing leads to a time series of state-to-state transition probabilities that includes the spatio-temporally linked recurrent events in the record-breaking sense. A state refers to a configuration comprised of zones with either the occurrence or non-occurrence of an earthquake in each zone in a pre-determined time interval. Since the time series is derived from non-linear and non-stationary earthquake sequencing, we use known analysis methods to glean new information. We apply decomposition procedures such as ensemble empirical mode decomposition (EEMD) to study the state-to-state fluctuations in each of the intrinsic mode functions. We subject the intrinsic mode functions, derived from the time series using the EEMD, to a detailed analysis to draw information content of the time series. Also, we investigate the influence of random noise on the data-driven state-to-state transition probabilities. We consider a second aspect of earthquake sequencing that is closely tied to its time-correlative behaviour. Here, we extend the Fano factor and Allan factor analysis to the time series of state-to-state transition frequencies of a Markov chain. Our results support not only the usefulness of the intrinsic mode functions in understanding the time series but also the presence of power-law behaviour exemplified by the Fano factor and the Allan factor.
Process fault detection and nonlinear time series analysis for anomaly detection in safeguards
Burr, T.L.; Mullen, M.F.; Wangen, L.E.
1994-02-01
In this paper we discuss two advanced techniques, process fault detection and nonlinear time series analysis, and apply them to the analysis of vector-valued and single-valued time-series data. We investigate model-based process fault detection methods for analyzing simulated, multivariate, time-series data from a three-tank system. The model-predictions are compared with simulated measurements of the same variables to form residual vectors that are tested for the presence of faults (possible diversions in safeguards terminology). We evaluate two methods, testing all individual residuals with a univariate z-score and testing all variables simultaneously with the Mahalanobis distance, for their ability to detect loss of material from two different leak scenarios from the three-tank system: a leak without and with replacement of the lost volume. Nonlinear time-series analysis tools were compared with the linear methods popularized by Box and Jenkins. We compare prediction results using three nonlinear and two linear modeling methods on each of six simulated time series: two nonlinear and four linear. The nonlinear methods performed better at predicting the nonlinear time series and did as well as the linear methods at predicting the linear values.
Dean, Dennis A.; Adler, Gail K.; Nguyen, David P.; Klerman, Elizabeth B.
2014-01-01
We present a novel approach for analyzing biological time-series data using a context-free language (CFL) representation that allows the extraction and quantification of important features from the time-series. This representation results in Hierarchically AdaPtive (HAP) analysis, a suite of multiple complementary techniques that enable rapid analysis of data and does not require the user to set parameters. HAP analysis generates hierarchically organized parameter distributions that allow multi-scale components of the time-series to be quantified and includes a data analysis pipeline that applies recursive analyses to generate hierarchically organized results that extend traditional outcome measures such as pharmacokinetics and inter-pulse interval. Pulsicons, a novel text-based time-series representation also derived from the CFL approach, are introduced as an objective qualitative comparison nomenclature. We apply HAP to the analysis of 24 hours of frequently sampled pulsatile cortisol hormone data, which has known analysis challenges, from 14 healthy women. HAP analysis generated results in seconds and produced dozens of figures for each participant. The results quantify the observed qualitative features of cortisol data as a series of pulse clusters, each consisting of one or more embedded pulses, and identify two ultradian phenotypes in this dataset. HAP analysis is designed to be robust to individual differences and to missing data and may be applied to other pulsatile hormones. Future work can extend HAP analysis to other time-series data types, including oscillatory and other periodic physiological signals. PMID:25184442
Multivariate stochastic analysis for Monthly hydrological time series at Cuyahoga River Basin
NASA Astrophysics Data System (ADS)
zhang, L.
2011-12-01
Copula has become a very powerful statistic and stochastic methodology in case of the multivariate analysis in Environmental and Water resources Engineering. In recent years, the popular one-parameter Archimedean copulas, e.g. Gumbel-Houggard copula, Cook-Johnson copula, Frank copula, the meta-elliptical copula, e.g. Gaussian Copula, Student-T copula, etc. have been applied in multivariate hydrological analyses, e.g. multivariate rainfall (rainfall intensity, duration and depth), flood (peak discharge, duration and volume), and drought analyses (drought length, mean and minimum SPI values, and drought mean areal extent). Copula has also been applied in the flood frequency analysis at the confluences of river systems by taking into account the dependence among upstream gauge stations rather than by using the hydrological routing technique. In most of the studies above, the annual time series have been considered as stationary signal which the time series have been assumed as independent identically distributed (i.i.d.) random variables. But in reality, hydrological time series, especially the daily and monthly hydrological time series, cannot be considered as i.i.d. random variables due to the periodicity existed in the data structure. Also, the stationary assumption is also under question due to the Climate Change and Land Use and Land Cover (LULC) change in the fast years. To this end, it is necessary to revaluate the classic approach for the study of hydrological time series by relaxing the stationary assumption by the use of nonstationary approach. Also as to the study of the dependence structure for the hydrological time series, the assumption of same type of univariate distribution also needs to be relaxed by adopting the copula theory. In this paper, the univariate monthly hydrological time series will be studied through the nonstationary time series analysis approach. The dependence structure of the multivariate monthly hydrological time series will be
Providing web-based tools for time series access and analysis
NASA Astrophysics Data System (ADS)
Eberle, Jonas; Hüttich, Christian; Schmullius, Christiane
2014-05-01
Time series information is widely used in environmental change analyses and is also an essential information for stakeholders and governmental agencies. However, a challenging issue is the processing of raw data and the execution of time series analysis. In most cases, data has to be found, downloaded, processed and even converted in the correct data format prior to executing time series analysis tools. Data has to be prepared to use it in different existing software packages. Several packages like TIMESAT (Jönnson & Eklundh, 2004) for phenological studies, BFAST (Verbesselt et al., 2010) for breakpoint detection, and GreenBrown (Forkel et al., 2013) for trend calculations are provided as open-source software and can be executed from the command line. This is needed if data pre-processing and time series analysis is being automated. To bring both parts, automated data access and data analysis, together, a web-based system was developed to provide access to satellite based time series data and access to above mentioned analysis tools. Users of the web portal are able to specify a point or a polygon and an available dataset (e.g., Vegetation Indices and Land Surface Temperature datasets from NASA MODIS). The data is then being processed and provided as a time series CSV file. Afterwards the user can select an analysis tool that is being executed on the server. The final data (CSV, plot images, GeoTIFFs) is visualized in the web portal and can be downloaded for further usage. As a first use case, we built up a complimentary web-based system with NASA MODIS products for Germany and parts of Siberia based on the Earth Observation Monitor (www.earth-observation-monitor.net). The aim of this work is to make time series analysis with existing tools as easy as possible that users can focus on the interpretation of the results. References: Jönnson, P. and L. Eklundh (2004). TIMESAT - a program for analysing time-series of satellite sensor data. Computers and Geosciences 30
Detecting Anomaly Regions in Satellite Image Time Series Based on Sesaonal Autocorrelation Analysis
NASA Astrophysics Data System (ADS)
Zhou, Z.-G.; Tang, P.; Zhou, M.
2016-06-01
Anomaly regions in satellite images can reflect unexpected changes of land cover caused by flood, fire, landslide, etc. Detecting anomaly regions in satellite image time series is important for studying the dynamic processes of land cover changes as well as for disaster monitoring. Although several methods have been developed to detect land cover changes using satellite image time series, they are generally designed for detecting inter-annual or abrupt land cover changes, but are not focusing on detecting spatial-temporal changes in continuous images. In order to identify spatial-temporal dynamic processes of unexpected changes of land cover, this study proposes a method for detecting anomaly regions in each image of satellite image time series based on seasonal autocorrelation analysis. The method was validated with a case study to detect spatial-temporal processes of a severe flooding using Terra/MODIS image time series. Experiments demonstrated the advantages of the method that (1) it can effectively detect anomaly regions in each of satellite image time series, showing spatial-temporal varying process of anomaly regions, (2) it is flexible to meet some requirement (e.g., z-value or significance level) of detection accuracies with overall accuracy being up to 89% and precision above than 90%, and (3) it does not need time series smoothing and can detect anomaly regions in noisy satellite images with a high reliability.
Complexity analysis of the air temperature and the precipitation time series in Serbia
NASA Astrophysics Data System (ADS)
Mimić, G.; Mihailović, D. T.; Kapor, D.
2015-11-01
In this paper, we have analyzed the time series of daily values for three meteorological elements, two continuous and a discontinuous one, i.e., the maximum and minimum air temperature and the precipitation. The analysis was done based on the observations from seven stations in Serbia from the period 1951-2010. The main aim of this paper was to quantify the complexity of the annual values for the mentioned time series and to calculate the rate of its change. For that purpose, we have used the sample entropy and the Kolmogorov complexity as the measures which can indicate the variability and irregularity of a given time series. Results obtained show that the maximum temperature has increasing trends in the given period which points out a warming, ranged in the interval 1-2 °C. The increasing temperature indicates the higher internal energy of the atmosphere, changing the weather patterns, manifested in the time series. The Kolmogorov complexity of the maximum temperature time series has statistically significant increasing trends, while the sample entropy has increasing but statistically insignificant trend. The trends of complexity measures for the minimum temperature depend on the location. Both complexity measures for the precipitation time series have decreasing trends.
Filter-based multiscale entropy analysis of complex physiological time series.
Xu, Yuesheng; Zhao, Liang
2013-08-01
Multiscale entropy (MSE) has been widely and successfully used in analyzing the complexity of physiological time series. We reinterpret the averaging process in MSE as filtering a time series by a filter of a piecewise constant type. From this viewpoint, we introduce filter-based multiscale entropy (FME), which filters a time series to generate multiple frequency components, and then we compute the blockwise entropy of the resulting components. By choosing filters adapted to the feature of a given time series, FME is able to better capture its multiscale information and to provide more flexibility for studying its complexity. Motivated by the heart rate turbulence theory, which suggests that the human heartbeat interval time series can be described in piecewise linear patterns, we propose piecewise linear filter multiscale entropy (PLFME) for the complexity analysis of the time series. Numerical results from PLFME are more robust to data of various lengths than those from MSE. The numerical performance of the adaptive piecewise constant filter multiscale entropy without prior information is comparable to that of PLFME, whose design takes prior information into account. PMID:24032873
The application of complex network time series analysis in turbulent heated jets
Charakopoulos, A. K.; Karakasidis, T. E. Liakopoulos, A.; Papanicolaou, P. N.
2014-06-15
In the present study, we applied the methodology of the complex network-based time series analysis to experimental temperature time series from a vertical turbulent heated jet. More specifically, we approach the hydrodynamic problem of discriminating time series corresponding to various regions relative to the jet axis, i.e., time series corresponding to regions that are close to the jet axis from time series originating at regions with a different dynamical regime based on the constructed network properties. Applying the transformation phase space method (k nearest neighbors) and also the visibility algorithm, we transformed time series into networks and evaluated the topological properties of the networks such as degree distribution, average path length, diameter, modularity, and clustering coefficient. The results show that the complex network approach allows distinguishing, identifying, and exploring in detail various dynamical regions of the jet flow, and associate it to the corresponding physical behavior. In addition, in order to reject the hypothesis that the studied networks originate from a stochastic process, we generated random network and we compared their statistical properties with that originating from the experimental data. As far as the efficiency of the two methods for network construction is concerned, we conclude that both methodologies lead to network properties that present almost the same qualitative behavior and allow us to reveal the underlying system dynamics.
The application of complex network time series analysis in turbulent heated jets
NASA Astrophysics Data System (ADS)
Charakopoulos, A. K.; Karakasidis, T. E.; Papanicolaou, P. N.; Liakopoulos, A.
2014-06-01
In the present study, we applied the methodology of the complex network-based time series analysis to experimental temperature time series from a vertical turbulent heated jet. More specifically, we approach the hydrodynamic problem of discriminating time series corresponding to various regions relative to the jet axis, i.e., time series corresponding to regions that are close to the jet axis from time series originating at regions with a different dynamical regime based on the constructed network properties. Applying the transformation phase space method (k nearest neighbors) and also the visibility algorithm, we transformed time series into networks and evaluated the topological properties of the networks such as degree distribution, average path length, diameter, modularity, and clustering coefficient. The results show that the complex network approach allows distinguishing, identifying, and exploring in detail various dynamical regions of the jet flow, and associate it to the corresponding physical behavior. In addition, in order to reject the hypothesis that the studied networks originate from a stochastic process, we generated random network and we compared their statistical properties with that originating from the experimental data. As far as the efficiency of the two methods for network construction is concerned, we conclude that both methodologies lead to network properties that present almost the same qualitative behavior and allow us to reveal the underlying system dynamics.
NASA Astrophysics Data System (ADS)
He, Xiaoxing; Hua, Xianghong; Yu, Kegen; Xuan, Wei; Lu, Tieding; Zhang, W.; Chen, X.
2015-03-01
This paper focuses on performance analysis and accuracy enhancement of long-term position time series of a regional network of GPS stations with two near sub-blocks, one block of 8 stations in Cascadia region and another block of 14 stations in Southern California. We have analyzed the seasonal variations of the 22 IGS site positions between 2004 and 2011. The Green's function is used to calculate the station-site displacements induced by the environmental loading due to atmospheric pressure, soil moisture, snow depth and nontidal ocean. The analysis has revealed that these loading factors can result in position shift of centimeter level, the displacement time series exhibit a periodic pattern, which can explain about 12.70-21.78% of the seasonal amplitude on vertical GPS time series, and the loading effect is significantly different among the two nearby geographical regions. After the loading effect is corrected, the principal component analysis (PCA)-based block spatial filtering is proposed to filter out the remaining common mode error (CME) of the GPS time series. The results show that the PCA-based block spatial filtering can extract the CME more accurately and effectively than the conventional overall filtering method, reducing more of the uncertainty. With the loading correction and block spatial filtering, about 68.34-73.20% of the vertical GPS seasonal power can be separated and removed, improving the reliability of the GPS time series and hence enabling better deformation analysis and higher precision geodetic applications.
Mobile Visualization and Analysis Tools for Spatial Time-Series Data
NASA Astrophysics Data System (ADS)
Eberle, J.; Hüttich, C.; Schmullius, C.
2013-12-01
The Siberian Earth System Science Cluster (SIB-ESS-C) provides access and analysis services for spatial time-series data build on products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and climate data from meteorological stations. Until now a webportal for data access, visualization and analysis with standard-compliant web services was developed for SIB-ESS-C. As a further enhancement a mobile app was developed to provide an easy access to these time-series data for field campaigns. The app sends the current position from the GPS receiver and a specific dataset (like land surface temperature or vegetation indices) - selected by the user - to our SIB-ESS-C web service and gets the requested time-series data for the identified pixel back in real-time. The data is then being plotted directly in the app. Furthermore the user has possibilities to analyze the time-series data for breaking points and other phenological values. These processings are executed on demand of the user on our SIB-ESS-C web server and results are transfered to the app. Any processing can also be done at the SIB-ESS-C webportal. The aim of this work is to make spatial time-series data and analysis functions available for end users without the need of data processing. In this presentation the author gives an overview on this new mobile app, the functionalities, the technical infrastructure as well as technological issues (how the app was developed, our made experiences).
On statistical inference in time series analysis of the evolution of road safety.
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. PMID:23260716
Wavelet analysis for non-stationary, non-linear time series
NASA Astrophysics Data System (ADS)
Schulte, J. A.
2015-12-01
Methods for detecting and quantifying nonlinearities in nonstationary time series are introduced and developed. In particular, higher-order wavelet analysis was applied to an ideal time series and the Quasi-biennial Oscillation (QBO) time series. Multiple-testing problems inherent in wavelet analysis were addressed by controlling the false discovery rate. A new local autobicoherence spectrum facilitated the detection of local nonlinearities and the quantification of cycle geometry. The local autobicoherence spectrum of the QBO time series showed that the QBO time series contained a mode with a period of 28 months that was phase-coupled to a harmonic with a period of 14 months. An additional nonlinearly interacting triad was found among modes with periods of 10, 16, 26 months. Local biphase spectra determined that the nonlinear interactions were not quadratic and that the effect of the nonlinearities was to produce non-smoothly varying oscillations. The oscillations were found to be skewed so that negative QBO regimes were preferred, and also asymmetric in the sense that phase transitions between the easterly and westerly phases occurred more rapidly than those from westerly to easterly regimes.
NASA Astrophysics Data System (ADS)
Donner, R. V.; Zou, Y.; Donges, J. F.; Marwan, N.; Kurths, J.
2009-12-01
We present a new approach for analysing structural properties of time series from complex systems. Starting from the concept of recurrences in phase space, the recurrence matrix of a time series is interpreted as the adjacency matrix of an associated complex network which links different points in time if the evolution of the considered states is very similar. A critical comparison of these recurrence networks with similar existing techniques is presented, revealing strong conceptual benefits of the new approach which can be considered as a unifying framework for transforming time series into complex networks that also includes other methods as special cases. Based on different model systems, we demonstrate that there are fundamental interrelationships between the topological properties of recurrence networks and the statistical properties of the phase space density of the underlying dynamical system. Hence, the network description yields new quantitative characteristics of the dynamical complexity of a time series, which substantially complement existing measures of recurrence quantification analysis. Finally, we illustrate the potential of our approach for detecting hidden dynamical transitions from geoscientific time series by applying it to different paleoclimate records. In particular, we are able to resolve previously unknown climatic regime shifts in East Africa during the last about 4 million years, which might have had a considerable influence on the evolution of hominids in the area.
NASA Astrophysics Data System (ADS)
Pal, Mayukha; Madhusudana Rao, P.; Manimaran, P.
2014-12-01
We apply the recently developed multifractal detrended cross-correlation analysis method to investigate the cross-correlation behavior and fractal nature between two non-stationary time series. We analyze the daily return price of gold, West Texas Intermediate and Brent crude oil, foreign exchange rate data, over a period of 18 years. The cross correlation has been measured from the Hurst scaling exponents and the singularity spectrum quantitatively. From the results, the existence of multifractal cross-correlation between all of these time series is found. We also found that the cross correlation between gold and oil prices possess uncorrelated behavior and the remaining bivariate time series possess persistent behavior. It was observed for five bivariate series that the cross-correlation exponents are less than the calculated average generalized Hurst exponents (GHE) for q<0 and greater than GHE when q>0 and for one bivariate series the cross-correlation exponent is greater than GHE for all q values.
Scalable Hyper-parameter Estimation for Gaussian Process Based Time Series Analysis
Chandola, Varun; Vatsavai, Raju
2010-01-01
Gaussian process (GP) is increasingly becoming popular as a kernel machine learning tool for non-parametric data analysis. Recently, GP has been applied to model non-linear dependencies in time series data. GP based analysis can be used to solve problems of time series prediction, forecasting, missing data imputation, change point detection, anomaly detection, etc. But the use of GP to handle massive scientific time series data sets has been limited, owing to its expensive computational complexity. The primary bottleneck is the handling of the covariance matrix whose size is quadratic in the length of the time series. In this paper we propose a scalable method that exploit the special structure of the covariance matrix for hyper-parameter estimation in GP based learning. The proposed method allows estimation of hyper parameters associated with GP in quadratic time, which is an order of magnitude improvement over standard methods with cubic complexity. Moreover, the proposed method does not require explicit computation of the covariance matrix and hence has memory requirement linear to the length of the time series as opposed to the quadratic memory requirement of standard methods. To further improve the computational complexity of the proposed method, we provide a parallel version to concurrently estimate the log likelihood for a set of time series which is the key step in the hyper-parameter estimation. Performance results on a multi-core system show that our proposed method provides significant speedups as high as 1000, even when running in serial mode, while maintaining a small memory footprint. The parallel version exploits the natural parallelization potential of the serial algorithm and is shown to perform significantly better than the serial faster algorithm, with speedups as high as 10.
NASA Astrophysics Data System (ADS)
Qian, Xi-Yuan; Liu, Ya-Min; Jiang, Zhi-Qiang; Podobnik, Boris; Zhou, Wei-Xing; Stanley, H. Eugene
2015-06-01
When common factors strongly influence two power-law cross-correlated time series recorded in complex natural or social systems, using detrended cross-correlation analysis (DCCA) without considering these common factors will bias the results. We use detrended partial cross-correlation analysis (DPXA) to uncover the intrinsic power-law cross correlations between two simultaneously recorded time series in the presence of nonstationarity after removing the effects of other time series acting as common forces. The DPXA method is a generalization of the detrended cross-correlation analysis that takes into account partial correlation analysis. We demonstrate the method by using bivariate fractional Brownian motions contaminated with a fractional Brownian motion. We find that the DPXA is able to recover the analytical cross Hurst indices, and thus the multiscale DPXA coefficients are a viable alternative to the conventional cross-correlation coefficient. We demonstrate the advantage of the DPXA coefficients over the DCCA coefficients by analyzing contaminated bivariate fractional Brownian motions. We calculate the DPXA coefficients and use them to extract the intrinsic cross correlation between crude oil and gold futures by taking into consideration the impact of the U.S. dollar index. We develop the multifractal DPXA (MF-DPXA) method in order to generalize the DPXA method and investigate multifractal time series. We analyze multifractal binomial measures masked with strong white noises and find that the MF-DPXA method quantifies the hidden multifractal nature while the multifractal DCCA method fails.
A Comparison of Alternative Approaches to the Analysis of Interrupted Time-Series.
ERIC Educational Resources Information Center
Harrop, John W.; Velicer, Wayne F.
1985-01-01
Computer generated data representative of 16 Auto Regressive Integrated Moving Averages (ARIMA) models were used to compare the results of interrupted time-series analysis using: (1) the known model identification, (2) an assumed (l,0,0) model, and (3) an assumed (3,0,0) model as an approximation to the General Transformation approach. (Author/BW)
A Comparison of Missing-Data Procedures for Arima Time-Series Analysis
ERIC Educational Resources Information Center
Velicer, Wayne F.; Colby, Suzanne M.
2005-01-01
Missing data are a common practical problem for longitudinal designs. Time-series analysis is a longitudinal method that involves a large number of observations on a single unit. Four different missing-data methods (deletion, mean substitution, mean of adjacent observations, and maximum likelihood estimation) were evaluated. Computer-generated…
Time Series in Education: The Analysis of Daily Attendance in Two High Schools
ERIC Educational Resources Information Center
Koopmans, Matthijs
2011-01-01
This presentation discusses the use of a time series approach to the analysis of daily attendance in two urban high schools over the course of one school year (2009-10). After establishing that the series for both schools were stationary, they were examined for moving average processes, autoregression, seasonal dependencies (weekly cycles),…
NASA Astrophysics Data System (ADS)
Donges, Jonathan F.; Heitzig, Jobst; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik V.; Marwan, Norbert; Dijkstra, Henk A.; Kurths, Jürgen
2015-11-01
We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics, or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis, recurrence networks, visibility graphs, and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology.
Qian, Xi-Yuan; Liu, Ya-Min; Jiang, Zhi-Qiang; Podobnik, Boris; Zhou, Wei-Xing; Stanley, H Eugene
2015-06-01
When common factors strongly influence two power-law cross-correlated time series recorded in complex natural or social systems, using detrended cross-correlation analysis (DCCA) without considering these common factors will bias the results. We use detrended partial cross-correlation analysis (DPXA) to uncover the intrinsic power-law cross correlations between two simultaneously recorded time series in the presence of nonstationarity after removing the effects of other time series acting as common forces. The DPXA method is a generalization of the detrended cross-correlation analysis that takes into account partial correlation analysis. We demonstrate the method by using bivariate fractional Brownian motions contaminated with a fractional Brownian motion. We find that the DPXA is able to recover the analytical cross Hurst indices, and thus the multiscale DPXA coefficients are a viable alternative to the conventional cross-correlation coefficient. We demonstrate the advantage of the DPXA coefficients over the DCCA coefficients by analyzing contaminated bivariate fractional Brownian motions. We calculate the DPXA coefficients and use them to extract the intrinsic cross correlation between crude oil and gold futures by taking into consideration the impact of the U.S. dollar index. We develop the multifractal DPXA (MF-DPXA) method in order to generalize the DPXA method and investigate multifractal time series. We analyze multifractal binomial measures masked with strong white noises and find that the MF-DPXA method quantifies the hidden multifractal nature while the multifractal DCCA method fails. PMID:26172763
Donges, Jonathan F; Heitzig, Jobst; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik V; Marwan, Norbert; Dijkstra, Henk A; Kurths, Jürgen
2015-11-01
We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics, or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis, recurrence networks, visibility graphs, and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology. PMID:26627561
NASA Astrophysics Data System (ADS)
Donges, Jonathan; Heitzig, Jobst; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik; Marwan, Norbert; Dijkstra, Henk; Kurths, Jürgen
2016-04-01
We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics, or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis, recurrence networks, visibility graphs, and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology. pyunicorn is available online at https://github.com/pik-copan/pyunicorn. Reference: J.F. Donges, J. Heitzig, B. Beronov, M. Wiedermann, J. Runge, Q.-Y. Feng, L. Tupikina, V. Stolbova, R.V. Donner, N. Marwan, H.A. Dijkstra, and J. Kurths, Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package, Chaos 25, 113101 (2015), DOI: 10.1063/1.4934554, Preprint: arxiv.org:1507.01571 [physics.data-an].
NASA Astrophysics Data System (ADS)
Pavon-Dominguez, Pablo; Ariza-Villaverde, Ana B.; Jimenez-Hornero, Francisco J.; Gutierrez de Rave, Eduardo
2010-05-01
The time series corresponding to variables related with the climate have been frequently studied by using the descriptive statistics. However, as several works have suggested, other approaches such as the multifractal analysis can be taken into account to complete the information about some climatic and environmental phenomena obtained from the standard methods. As a consequence, the main aim of this work was to check whether some meteorological variables relevant in urban environments (i.e. air temperature, rainfall, relative humidity, solar radiation and surface wind velocity and direction) exhibited a multifractal nature. The analysis was extended to several time scales determining the multifractal parameters and exploring the existing relationships between them and those reported by the descriptive statistics. The daily time series studied in this work were recorded in Córdoba (37.85°N 4.85°W), southern Spain, from 2001 to 2006. The altitude of this location is 117 m and the climate of this location can be defined as a mixture of Mediterranean characteristics and Continental effects. The multifractal spectra showed convex shapes for all the considered variables, confirming the presence of a multifractal type of scaling that was kept for time resolutions ranging from one day to six years. In the case of rainfall, the observed range of time scales that exhibited a multifractal nature was more restrictive due to the presence of many zeros in the daily data that characterized the precipitation regime in some places of southern Spain. The multifractal spectra corresponding to surface wind velocity and rainfall showed longer left tails implying greater heterogeneity in the time series high values. However, the multifractal spectra obtained for the rest of meteorological variables exhibited the opposite behavior meaning that the low data in the time series had more influence in the distribution variability. The presence of rare low values was significant for
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
REDFIT-X: Cross-spectral analysis of unevenly spaced paleoclimate time series
NASA Astrophysics Data System (ADS)
Björg Ólafsdóttir, Kristín; Schulz, Michael; Mudelsee, Manfred
2016-06-01
Cross-spectral analysis is commonly used in climate research to identify joint variability between two variables and to assess the phase (lead/lag) between them. Here we present a Fortran 90 program (REDFIT-X) that is specially developed to perform cross-spectral analysis of unevenly spaced paleoclimate time series. The data properties of climate time series that are necessary to take into account are for example data spacing (unequal time scales and/or uneven spacing between time points) and the persistence in the data. Lomb-Scargle Fourier transform is used for the cross-spectral analyses between two time series with unequal and/or uneven time scale and the persistence in the data is taken into account when estimating the uncertainty associated with cross-spectral estimates. We use a Monte Carlo approach to estimate the uncertainty associated with coherency and phase. False-alarm level is estimated from empirical distribution of coherency estimates and confidence intervals for the phase angle are formed from the empirical distribution of the phase estimates. The method is validated by comparing the Monte Carlo uncertainty estimates with the traditionally used measures. Examples are given where the method is applied to paleoceanographic time series.
Application of the Allan Variance to Time Series Analysis in Astrometry and Geodesy: A Review.
Malkin, Zinovy
2016-04-01
The Allan variance (AVAR) was introduced 50 years ago as a statistical tool for assessing the frequency standards deviations. For the past decades, AVAR has increasingly been used in geodesy and astrometry to assess the noise characteristics in geodetic and astrometric time series. A specific feature of astrometric and geodetic measurements, as compared with clock measurements, is that they are generally associated with uncertainties; thus, an appropriate weighting should be applied during data analysis. In addition, some physically connected scalar time series naturally form series of multidimensional vectors. For example, three station coordinates time series X, Y, and Z can be combined to analyze 3-D station position variations. The classical AVAR is not intended for processing unevenly weighted and/or multidimensional data. Therefore, AVAR modifications, namely weighted AVAR (WAVAR), multidimensional AVAR (MAVAR), and weighted multidimensional AVAR (WMAVAR), were introduced to overcome these deficiencies. In this paper, a brief review is given of the experience of using AVAR and its modifications in processing astrogeodetic time series. PMID:26540681
Lutaif, N.A.; Palazzo, R.; Gontijo, J.A.R.
2014-01-01
Maintenance of thermal homeostasis in rats fed a high-fat diet (HFD) is associated with changes in their thermal balance. The thermodynamic relationship between heat dissipation and energy storage is altered by the ingestion of high-energy diet content. Observation of thermal registers of core temperature behavior, in humans and rodents, permits identification of some characteristics of time series, such as autoreference and stationarity that fit adequately to a stochastic analysis. To identify this change, we used, for the first time, a stochastic autoregressive model, the concepts of which match those associated with physiological systems involved and applied in male HFD rats compared with their appropriate standard food intake age-matched male controls (n=7 per group). By analyzing a recorded temperature time series, we were able to identify when thermal homeostasis would be affected by a new diet. The autoregressive time series model (AR model) was used to predict the occurrence of thermal homeostasis, and this model proved to be very effective in distinguishing such a physiological disorder. Thus, we infer from the results of our study that maximum entropy distribution as a means for stochastic characterization of temperature time series registers may be established as an important and early tool to aid in the diagnosis and prevention of metabolic diseases due to their ability to detect small variations in thermal profile. PMID:24519093
Lutaif, N A; Palazzo, R; Gontijo, J A R
2014-01-01
Maintenance of thermal homeostasis in rats fed a high-fat diet (HFD) is associated with changes in their thermal balance. The thermodynamic relationship between heat dissipation and energy storage is altered by the ingestion of high-energy diet content. Observation of thermal registers of core temperature behavior, in humans and rodents, permits identification of some characteristics of time series, such as autoreference and stationarity that fit adequately to a stochastic analysis. To identify this change, we used, for the first time, a stochastic autoregressive model, the concepts of which match those associated with physiological systems involved and applied in male HFD rats compared with their appropriate standard food intake age-matched male controls (n=7 per group). By analyzing a recorded temperature time series, we were able to identify when thermal homeostasis would be affected by a new diet. The autoregressive time series model (AR model) was used to predict the occurrence of thermal homeostasis, and this model proved to be very effective in distinguishing such a physiological disorder. Thus, we infer from the results of our study that maximum entropy distribution as a means for stochastic characterization of temperature time series registers may be established as an important and early tool to aid in the diagnosis and prevention of metabolic diseases due to their ability to detect small variations in thermal profile. PMID:24519093
Costa, Madalena D.; Goldberger, Ary L.
2016-01-01
We introduce a generalization of multiscale entropy (MSE) analysis. The method is termed MSEn, where the subscript denotes the moment used to coarse-grain a time series. MSEμ, described previously, uses the mean value (first moment). Here, we focus on MSEσ2, which uses the second moment, i.e., the variance. MSEσ2 quantifies the dynamics of the volatility (variance) of a signal over multiple time scales. We use the method to analyze the structure of heartbeat time series. We find that the dynamics of the volatility of heartbeat time series obtained from healthy young subjects is highly complex. Furthermore, we find that the multiscale complexity of the volatility, not only the multiscale complexity of the mean heart rate, degrades with aging and pathology. The “bursty” behavior of the dynamics may be related to intermittency in energy and information flows, as part of multiscale cycles of activation and recovery. Generalized MSE may also be useful in quantifying the dynamical properties of other physiologic and of non-physiologic time series. PMID:27099455
Effective low-order models for atmospheric dynamics and time series analysis
NASA Astrophysics Data System (ADS)
Gluhovsky, Alexander; Grady, Kevin
2016-02-01
The paper focuses on two interrelated problems: developing physically sound low-order models (LOMs) for atmospheric dynamics and employing them as novel time-series models to overcome deficiencies in current atmospheric time series analysis. The first problem is warranted since arbitrary truncations in the Galerkin method (commonly used to derive LOMs) may result in LOMs that violate fundamental conservation properties of the original equations, causing unphysical behaviors such as unbounded solutions. In contrast, the LOMs we offer (G-models) are energy conserving, and some retain the Hamiltonian structure of the original equations. This work examines LOMs from recent publications to show that all of them that are physically sound can be converted to G-models, while those that cannot lack energy conservation. Further, motivated by recent progress in statistical properties of dynamical systems, we explore G-models for a new role of atmospheric time series models as their data generating mechanisms are well in line with atmospheric dynamics. Currently used time series models, however, do not specifically utilize the physics of the governing equations and involve strong statistical assumptions rarely met in real data.
Effective low-order models for atmospheric dynamics and time series analysis.
Gluhovsky, Alexander; Grady, Kevin
2016-02-01
The paper focuses on two interrelated problems: developing physically sound low-order models (LOMs) for atmospheric dynamics and employing them as novel time-series models to overcome deficiencies in current atmospheric time series analysis. The first problem is warranted since arbitrary truncations in the Galerkin method (commonly used to derive LOMs) may result in LOMs that violate fundamental conservation properties of the original equations, causing unphysical behaviors such as unbounded solutions. In contrast, the LOMs we offer (G-models) are energy conserving, and some retain the Hamiltonian structure of the original equations. This work examines LOMs from recent publications to show that all of them that are physically sound can be converted to G-models, while those that cannot lack energy conservation. Further, motivated by recent progress in statistical properties of dynamical systems, we explore G-models for a new role of atmospheric time series models as their data generating mechanisms are well in line with atmospheric dynamics. Currently used time series models, however, do not specifically utilize the physics of the governing equations and involve strong statistical assumptions rarely met in real data. PMID:26931600
Time series analysis of infrared satellite data for detecting thermal anomalies: a hybrid approach
NASA Astrophysics Data System (ADS)
Koeppen, W. C.; Pilger, E.; Wright, R.
2011-07-01
We developed and tested an automated algorithm that analyzes thermal infrared satellite time series data to detect and quantify the excess energy radiated from thermal anomalies such as active volcanoes. Our algorithm enhances the previously developed MODVOLC approach, a simple point operation, by adding a more complex time series component based on the methods of the Robust Satellite Techniques (RST) algorithm. Using test sites at Anatahan and Kīlauea volcanoes, the hybrid time series approach detected ~15% more thermal anomalies than MODVOLC with very few, if any, known false detections. We also tested gas flares in the Cantarell oil field in the Gulf of Mexico as an end-member scenario representing very persistent thermal anomalies. At Cantarell, the hybrid algorithm showed only a slight improvement, but it did identify flares that were undetected by MODVOLC. We estimate that at least 80 MODIS images for each calendar month are required to create good reference images necessary for the time series analysis of the hybrid algorithm. The improved performance of the new algorithm over MODVOLC will result in the detection of low temperature thermal anomalies that will be useful in improving our ability to document Earth's volcanic eruptions, as well as detecting low temperature thermal precursors to larger eruptions.
Time series analysis of satellite derived surface temperature for Lake Garda
NASA Astrophysics Data System (ADS)
Pareeth, Sajid; Metz, Markus; Rocchini, Duccio; Salmaso, Nico; Neteler, Markus
2014-05-01
Remotely sensed satellite imageryis the most suitable tool for researchers around the globe in complementing in-situ observations. Nonetheless, it would be crucial to check for quality, validate and standardize methodologies to estimate the target variables from sensor data. Satellite imagery with thermal infrared bands provides opportunity to remotely measure the temperature in a very high spatio-temporal scale. Monitoring surface temperature of big lakes to understand the thermal fluctuations over time is considered crucial in the current status of global climate change scenario. The main disadvantage of remotely sensed data is the gaps due to presence of clouds and aerosols. In this study we use statistically reconstructed daily land surface temperature products from MODIS (MOD11A1 and MYD11A1) at a better spatial resolution of 250 m. The ability of remotely sensed datasets to capture the thermal variations over time is validated against historical monthly ground observation data collected for Lake Garda. The correlation between time series of satellite data LST (x,y,t) and the field measurements f (x,y,t) are found to be in acceptable range with a correlation coefficient of 0.94. We compared multiple time series analysis methods applied on the temperature maps recorded in the last ten years (2002 - 2012) and monthly field measurements in two sampling points in Lake Garda. The time series methods STL - Seasonal Time series decomposition based on Loess method, DTW - Dynamic Time Waping method, and BFAST - Breaks for Additive Season and Trend, are implemented and compared in their ability to derive changes in trends and seasonalities. These methods are mostly implemented on time series of vegetation indices from satellite data, but seldom used on thermal data because of the temporal incoherence of the data. The preliminary results show that time series methods applied on satellite data are able to reconstruct the seasons on an annual scale while giving us a
Mining biomedical time series by combining structural analysis and temporal abstractions.
Bellazzi, R.; Magni, P.; Larizza, C.; De Nicolao, G.; Riva, A.; Stefanelli, M.
1998-01-01
This paper describes the combination of Structural Time Series analysis and Temporal Abstractions for the interpretation of data coming from home monitoring of diabetic patients. Blood Glucose data are analyzed by a novel Bayesian technique for time series analysis. The results obtained are post-processed using Temporal Abstractions in order to extract knowledge that can be exploited "at the point of use" from physicians. The proposed data analysis procedure can be viewed as a Knowledge Discovery in Data Base process that is applied to time-varying data. The work here described is part of a Web-based telemedicine system for the management of Insulin Dependent Diabetes Mellitus patients, called T-IDDM. PMID:9929202
Modified cross sample entropy and surrogate data analysis method for financial time series
NASA Astrophysics Data System (ADS)
Yin, Yi; Shang, Pengjian
2015-09-01
For researching multiscale behaviors from the angle of entropy, we propose a modified cross sample entropy (MCSE) and combine surrogate data analysis with it in order to compute entropy differences between original dynamics and surrogate series (MCSDiff). MCSDiff is applied to simulated signals to show accuracy and then employed to US and Chinese stock markets. We illustrate the presence of multiscale behavior in the MCSDiff results and reveal that there are synchrony containing in the original financial time series and they have some intrinsic relations, which are destroyed by surrogate data analysis. Furthermore, the multifractal behaviors of cross-correlations between these financial time series are investigated by multifractal detrended cross-correlation analysis (MF-DCCA) method, since multifractal analysis is a multiscale analysis. We explore the multifractal properties of cross-correlation between these US and Chinese markets and show the distinctiveness of NQCI and HSI among the markets in their own region. It can be concluded that the weaker cross-correlation between US markets gives the evidence for the better inner mechanism in the US stock markets than that of Chinese stock markets. To study the multiscale features and properties of financial time series can provide valuable information for understanding the inner mechanism of financial markets.
Statistical Analysis of Sensor Network Time Series at Multiple Time Scales
NASA Astrophysics Data System (ADS)
Granat, R. A.; Donnellan, A.
2013-12-01
Modern sensor networks often collect data at multiple time scales in order to observe physical phenomena that occur at different scales. Whether collected by heterogeneous or homogenous sensor networks, measurements at different time scales are usually subject to different dynamics, noise characteristics, and error sources. We explore the impact of these effects on the results of statistical time series analysis methods applied to multi-scale time series data. As a case study, we analyze results from GPS time series position data collected in Japan and the Western United States, which produce raw observations at 1Hz and orbit corrected observations at time resolutions of 5 minutes, 30 minutes, and 24 hours. We utilize the GPS analysis package (GAP) software to perform three types of statistical analysis on these observations: hidden Markov modeling, probabilistic principle components analysis, and covariance distance analysis. We compare the results of these methods at the different time scales and discuss the impact on science understanding of earthquake fault systems generally and recent large seismic events specifically, including the Tohoku-Oki earthquake in Japan and El Mayor-Cucupah earthquake in Mexico.
Time series analysis of knowledge of results effects during motor skill acquisition.
Blackwell, J R; Simmons, R W; Spray, J A
1991-03-01
Time series analysis was used to investigate the hypothesis that during acquisition of a motor skill, knowledge of results (KR) information is used to generate a stable internal referent about which response errors are randomly distributed. Sixteen subjects completed 50 acquisition trials of each of three movements whose spatial-temporal characteristics differed. Acquisition trials were either blocked, with each movement being presented in series, or randomized, with the presentation of movements occurring in random order. Analysis of movement time data indicated the contextual interference effect reported in previous studies was replicated in the present experiment. Time series analysis of the acquisition trial data revealed the majority of individual subject response patterns during blocked trials were best described by a model with a temporarily stationary, internal reference of the criterion and systematic, trial-to-trial variation of response errors. During random trial conditions, response patterns were usually best described by a "White-noise" model. This model predicts a permanently stationary, internal reference associated with randomly distributed response errors that are unaffected by KR information. These results are not consistent with previous work using time series analysis to describe motor behavior (Spray & Newell, 1986). PMID:2028084
Imai, Chisato; Hashizume, Masahiro
2015-01-01
Background: Time series analysis is suitable for investigations of relatively direct and short-term effects of exposures on outcomes. In environmental epidemiology studies, this method has been one of the standard approaches to assess impacts of environmental factors on acute non-infectious diseases (e.g. cardiovascular deaths), with conventionally generalized linear or additive models (GLM and GAM). However, the same analysis practices are often observed with infectious diseases despite of the substantial differences from non-infectious diseases that may result in analytical challenges. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, systematic review was conducted to elucidate important issues in assessing the associations between environmental factors and infectious diseases using time series analysis with GLM and GAM. Published studies on the associations between weather factors and malaria, cholera, dengue, and influenza were targeted. Findings: Our review raised issues regarding the estimation of susceptible population and exposure lag times, the adequacy of seasonal adjustments, the presence of strong autocorrelations, and the lack of a smaller observation time unit of outcomes (i.e. daily data). These concerns may be attributable to features specific to infectious diseases, such as transmission among individuals and complicated causal mechanisms. Conclusion: The consequence of not taking adequate measures to address these issues is distortion of the appropriate risk quantifications of exposures factors. Future studies should pay careful attention to details and examine alternative models or methods that improve studies using time series regression analysis for environmental determinants of infectious diseases. PMID:25859149
Local Rainfall Forecast System based on Time Series Analysis and Neural Networks
NASA Astrophysics Data System (ADS)
Buendia, Fulgencio S.; Tarquis, A. M.; Buendia, G.; Andina, D.
2010-05-01
Rainfall is one of the most important events in daily life of human beings. During several decades, scientists have been trying to characterize the weather, current forecasts are based on high complex dynamic models. In this paper is presented a local rainfall forecast system based on Time Series analysis and Neural Networks. This model tries to complement the currently state of the art ensembles, from a locally historical perspective, where the model definition is not so dependent from the exact values of the initial conditions. After several year taking data, expert meteorologists proposed this approximation to characterize the local weather behavior, that is being automated by this system in different stages. However the whole system is introduced, it is focused on the different rainfall events situation classification as well as the time series analysis and forecast
Adventures in Modern Time Series Analysis: From the Sun to the Crab Nebula and Beyond
NASA Technical Reports Server (NTRS)
Scargle, Jeffrey
2014-01-01
With the generation of long, precise, and finely sampled time series the Age of Digital Astronomy is uncovering and elucidating energetic dynamical processes throughout the Universe. Fulfilling these opportunities requires data effective analysis techniques rapidly and automatically implementing advanced concepts. The Time Series Explorer, under development in collaboration with Tom Loredo, provides tools ranging from simple but optimal histograms to time and frequency domain analysis for arbitrary data modes with any time sampling. Much of this development owes its existence to Joe Bredekamp and the encouragement he provided over several decades. Sample results for solar chromospheric activity, gamma-ray activity in the Crab Nebula, active galactic nuclei and gamma-ray bursts will be displayed.
Identification of statistical patterns in complex systems via symbolic time series analysis.
Gupta, Shalabh; Khatkhate, Amol; Ray, Asok; Keller, Eric
2006-10-01
Identification of statistical patterns from observed time series of spatially distributed sensor data is critical for performance monitoring and decision making in human-engineered complex systems, such as electric power generation, petrochemical, and networked transportation. This paper presents an information-theoretic approach to identification of statistical patterns in such systems, where the main objective is to enhance structural integrity and operation reliability. The core concept of pattern identification is built upon the principles of Symbolic Dynamics, Automata Theory, and Information Theory. To this end, a symbolic time series analysis method has been formulated and experimentally validated on a special-purpose test apparatus that is designed for data acquisition and real-time analysis of fatigue damage in polycrystalline alloys. PMID:17063932
Parametric time-series analysis of daily air pollutants of city of Shumen, Bulgaria
NASA Astrophysics Data System (ADS)
Ivanov, A.; Voynikova, D.; Gocheva-Ilieva, S.; Boyadzhiev, D.
2012-10-01
The urban air pollution is one of the main factors determining the ambient air quality, which affects on the human health and the environment. In this paper parametric time series models are obtained for studying the distribution over time of primary pollutants as sulphur and nitrogen oxides, particulate matter and a secondary pollutant ground level ozon in the town of Shumen, Bulgaria. The methods of factor analysis and ARIMA are used to carry out the time series analysis based on hourly average data in 2011 and first quarter of 2012. The constructed models are applied for a short-term air pollution forecasting. The results are estimated on the basis of national and European regulation indices. The sources of pollutants in the region and their harmful effects on human health are also discussed.
Dutta, Debaditya; Mahmoud, Ahmed M.; Leers, Steven A.; Kim, Kang
2013-01-01
Large lipid pools in vulnerable plaques, in principle, can be detected using US based thermal strain imaging (US-TSI). One practical challenge for in vivo cardiovascular application of US-TSI is that the thermal strain is masked by the mechanical strain caused by cardiac pulsation. ECG gating is a widely adopted method for cardiac motion compensation, but it is often susceptible to electrical and physiological noise. In this paper, we present an alternative time series analysis approach to separate thermal strain from the mechanical strain without using ECG. The performance and feasibility of the time-series analysis technique was tested via numerical simulation as well as in vitro water tank experiments using a vessel mimicking phantom and an excised human atherosclerotic artery where the cardiac pulsation is simulated by a pulsatile pump. PMID:24808628
Dequéant, Mary-Lee; Fagegaltier, Delphine; Hu, Yanhui; Spirohn, Kerstin; Simcox, Amanda; Hannon, Gregory J.; Perrimon, Norbert
2015-01-01
The use of time series profiling to identify groups of functionally related genes (synexpression groups) is a powerful approach for the discovery of gene function. Here we apply this strategy during RasV12 immortalization of Drosophila embryonic cells, a phenomenon not well characterized. Using high-resolution transcriptional time-series datasets, we generated a gene network based on temporal expression profile similarities. This analysis revealed that common immortalized cells are related to adult muscle precursors (AMPs), a stem cell-like population contributing to adult muscles and sharing properties with vertebrate satellite cells. Remarkably, the immortalized cells retained the capacity for myogenic differentiation when treated with the steroid hormone ecdysone. Further, we validated in vivo the transcription factor CG9650, the ortholog of mammalian Bcl11a/b, as a regulator of AMP proliferation predicted by our analysis. Our study demonstrates the power of time series synexpression analysis to characterize Drosophila embryonic progenitor lines and identify stem/progenitor cell regulators. PMID:26438832
Detection of chaos: New approach to atmospheric pollen time-series analysis
NASA Astrophysics Data System (ADS)
Bianchi, M. M.; Arizmendi, C. M.; Sanchez, J. R.
1992-09-01
Pollen and spores are biological particles that are ubiquitous to the atmosphere and are pathologically significant, causing plant diseases and inhalant allergies. One of the main objectives of aerobiological surveys is forecasting. Prediction models are required in order to apply aerobiological knowledge to medical or agricultural practice; a necessary condition of these models is not to be chaotic. The existence of chaos is detected through the analysis of a time series. The time series comprises hourly counts of atmospheric pollen grains obtained using a Burkard spore trap from 1987 to 1989 at Mar del Plata. Abraham's method to obtain the correlation dimension was applied. A low and fractal dimension shows chaotic dynamics. The predictability of models for atomspheric pollen forecasting is discussed.
Dowling, Thomas E; Turner, Thomas F; Carson, Evan W; Saltzgiver, Melody J; Adams, Deborah; Kesner, Brian; Marsh, Paul C
2014-01-01
Time-series analysis is used widely in ecology to study complex phenomena and may have considerable potential to clarify relationships of genetic and demographic processes in natural and exploited populations. We explored the utility of this approach to evaluate population responses to management in razorback sucker, a long-lived and fecund, but declining freshwater fish species. A core population in Lake Mohave (Arizona-Nevada, USA) has experienced no natural recruitment for decades and is maintained by harvesting naturally produced larvae from the lake, rearing them in protective custody, and repatriating them at sizes less vulnerable to predation. Analyses of mtDNA and 15 microsatellites characterized for sequential larval cohorts collected over a 15-year time series revealed no changes in geographic structuring but indicated significant increase in mtDNA diversity for the entire population over time. Likewise, ratios of annual effective breeders to annual census size (Nb/Na) increased significantly despite sevenfold reduction of Na. These results indicated that conservation actions diminished near-term extinction risk due to genetic factors and should now focus on increasing numbers of fish in Lake Mohave to ameliorate longer-term risks. More generally, time-series analysis permitted robust testing of trends in genetic diversity, despite low precision of some metrics. PMID:24665337
Geospatial Analysis of Near-Surface Soil Moisture Time Series Data Over Indian Region
NASA Astrophysics Data System (ADS)
Berwal, P.; Murthy, C. S.; Raju, P. V.; Sesha Sai, M. V. R.
2016-06-01
The present study has developed the time series database surface soil moisture over India, for June, July and August months for the period of 20 years from 1991 to 2010, using data products generated under Climate Change Initiative Programme of European Space Agency. These three months represent the crop sowing period in the prime cropping season in the country and the soil moisture data during this period is highly useful to detect the drought conditions and assess the drought impact. The time series soil moisture data which is in 0.25 degree spatial resolution was analyzed to generate different indicators. Rainfall data of same spatial resolution for the same period, generated by India Meteorological Department was also procured and analyzed. Geospatial analysis of soil moisture and rainfall derived indicators was carried out to study (1) inter annual variability of soil moisture and rainfall, (2) soil moisture deviations from normal during prominent drought years, (3) soil moisture and rainfall correlations and (4) drought exposure based on soil moisture and rainfall variability. The study has successfully demonstrated the potential of these soil moisture time series data sets for generating regional drought surveillance information products, drought hazard mapping, drought exposure analysis and detection of drought sensitive areas in the crop planting period.
Dowling, Thomas E; Turner, Thomas F; Carson, Evan W; Saltzgiver, Melody J; Adams, Deborah; Kesner, Brian; Marsh, Paul C
2014-03-01
Time-series analysis is used widely in ecology to study complex phenomena and may have considerable potential to clarify relationships of genetic and demographic processes in natural and exploited populations. We explored the utility of this approach to evaluate population responses to management in razorback sucker, a long-lived and fecund, but declining freshwater fish species. A core population in Lake Mohave (Arizona-Nevada, USA) has experienced no natural recruitment for decades and is maintained by harvesting naturally produced larvae from the lake, rearing them in protective custody, and repatriating them at sizes less vulnerable to predation. Analyses of mtDNA and 15 microsatellites characterized for sequential larval cohorts collected over a 15-year time series revealed no changes in geographic structuring but indicated significant increase in mtDNA diversity for the entire population over time. Likewise, ratios of annual effective breeders to annual census size (N b /N a) increased significantly despite sevenfold reduction of N a. These results indicated that conservation actions diminished near-term extinction risk due to genetic factors and should now focus on increasing numbers of fish in Lake Mohave to ameliorate longer-term risks. More generally, time-series analysis permitted robust testing of trends in genetic diversity, despite low precision of some metrics. PMID:24665337
Inverting geodetic time series with a principal component analysis-based inversion method
NASA Astrophysics Data System (ADS)
Kositsky, A. P.; Avouac, J.-P.
2010-03-01
The Global Positioning System (GPS) system now makes it possible to monitor deformation of the Earth's surface along plate boundaries with unprecedented accuracy. In theory, the spatiotemporal evolution of slip on the plate boundary at depth, associated with either seismic or aseismic slip, can be inferred from these measurements through some inversion procedure based on the theory of dislocations in an elastic half-space. We describe and test a principal component analysis-based inversion method (PCAIM), an inversion strategy that relies on principal component analysis of the surface displacement time series. We prove that the fault slip history can be recovered from the inversion of each principal component. Because PCAIM does not require externally imposed temporal filtering, it can deal with any kind of time variation of fault slip. We test the approach by applying the technique to synthetic geodetic time series to show that a complicated slip history combining coseismic, postseismic, and nonstationary interseismic slip can be retrieved from this approach. PCAIM produces slip models comparable to those obtained from standard inversion techniques with less computational complexity. We also compare an afterslip model derived from the PCAIM inversion of postseismic displacements following the 2005 8.6 Nias earthquake with another solution obtained from the extended network inversion filter (ENIF). We introduce several extensions of the algorithm to allow statistically rigorous integration of multiple data sources (e.g., both GPS and interferometric synthetic aperture radar time series) over multiple timescales. PCAIM can be generalized to any linear inversion algorithm.
NASA Astrophysics Data System (ADS)
Eduardo Virgilio Silva, Luiz; Otavio Murta, Luiz
2012-12-01
Complexity in time series is an intriguing feature of living dynamical systems, with potential use for identification of system state. Although various methods have been proposed for measuring physiologic complexity, uncorrelated time series are often assigned high values of complexity, errouneously classifying them as a complex physiological signals. Here, we propose and discuss a method for complex system analysis based on generalized statistical formalism and surrogate time series. Sample entropy (SampEn) was rewritten inspired in Tsallis generalized entropy, as function of q parameter (qSampEn). qSDiff curves were calculated, which consist of differences between original and surrogate series qSampEn. We evaluated qSDiff for 125 real heart rate variability (HRV) dynamics, divided into groups of 70 healthy, 44 congestive heart failure (CHF), and 11 atrial fibrillation (AF) subjects, and for simulated series of stochastic and chaotic process. The evaluations showed that, for nonperiodic signals, qSDiff curves have a maximum point (qSDiffmax) for q ≠1. Values of q where the maximum point occurs and where qSDiff is zero were also evaluated. Only qSDiffmax values were capable of distinguish HRV groups (p-values 5.10×10-3, 1.11×10-7, and 5.50×10-7 for healthy vs. CHF, healthy vs. AF, and CHF vs. AF, respectively), consistently with the concept of physiologic complexity, and suggests a potential use for chaotic system analysis.
Nonlinear time series analysis of the fluctuations of the geomagnetic horizontal field
NASA Astrophysics Data System (ADS)
George, B.; Renuka, G.; Satheesh Kumar, K.; Kumar, C. P. Anil; Venugopal, C.
2002-02-01
A detailed nonlinear time series analysis of the hourly data of the geomagnetic horizontal intensity H measured at Kodaikanal (10.2° N; 77.5° E; mag: dip 3.5° N) has been carried out to investigate the dynamical behaviour of the fluctuations of H. The recurrence plots, spatiotemporal entropy and the result of the surrogate data test show the deterministic nature of the fluctuations, rejecting the hypothesis that H belong to the family of linear stochastic signals. The low dimensional character of the dynamics is evident from the estimated value of the correlation dimension and the fraction of false neighbours calculated for various embedding dimensions. The exponential decay of the power spectrum and the positive Lyapunov exponent indicate chaotic behaviour of the underlying dynamics of H. This is also supported by the results of the comparison of the chaotic characteristics of the time series of H with the pseudo-chaotic characteristics of coloured noise time series. We have also shown that the error involved in the short-term prediction of successive values of H, using a simple but robust, zero-order nonlinear prediction method, increases exponentially. It has also been suggested that there exists the possibility of characterizing the geomagnetic fluctuations in terms of the invariants in chaos theory, such as Lyapunov exponents and correlation dimension. The results of the analysis could also have implications in the development of a suitable model for the daily fluctuations of geomagnetic horizontal intensity.
NASA Technical Reports Server (NTRS)
Aires, Filipe; Rossow, William B.; Chedin, Alain; Hansen, James E. (Technical Monitor)
2000-01-01
The use of the Principal Component Analysis technique for the analysis of geophysical time series has been questioned in particular for its tendency to extract components that mix several physical phenomena even when the signal is just their linear sum. We demonstrate with a data simulation experiment that the Independent Component Analysis, a recently developed technique, is able to solve this problem. This new technique requires the statistical independence of components, a stronger constraint, that uses higher-order statistics, instead of the classical decorrelation a weaker constraint, that uses only second-order statistics. Furthermore, ICA does not require additional a priori information such as the localization constraint used in Rotational Techniques.
Reference manual for generation and analysis of Habitat Time Series: version II
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
Global coseismic deformations, GNSS time series analysis, and earthquake scaling laws
NASA Astrophysics Data System (ADS)
Métivier, Laurent; Collilieux, Xavier; Lercier, Daphné; Altamimi, Zuheir; Beauducel, François
2014-12-01
We investigate how two decades of coseismic deformations affect time series of GPS station coordinates (Global Navigation Satellite System) and what constraints geodetic observations give on earthquake scaling laws. We developed a simple but rapid model for coseismic deformations, assuming different earthquake scaling relations, that we systematically applied on earthquakes with magnitude larger than 4. We found that coseismic displacements accumulated during the last two decades can be larger than 10 m locally and that the cumulative displacement is not only due to large earthquakes but also to the accumulation of many small motions induced by smaller earthquakes. Then, investigating a global network of GPS stations, we demonstrate that a systematic global modeling of coseismic deformations helps greatly to detect discontinuities in GPS coordinate time series, which are still today one of the major sources of error in terrestrial reference frame construction (e.g., the International Terrestrial Reference Frame). We show that numerous discontinuities induced by earthquakes are too small to be visually detected because of seasonal variations and GPS noise that disturb their identification. However, not taking these discontinuities into account has a large impact on the station velocity estimation, considering today's precision requirements. Finally, six groups of earthquake scaling laws were tested. Comparisons with our GPS time series analysis on dedicated earthquakes give insights on the consistency of these scaling laws with geodetic observations and Okada coseismic approach.
Karakaya, N; Evrendilek, F
2010-06-01
Big Melen stream is one of the major water resources providing 0.268 [corrected] km(3) year(-1) of drinking and municipal water for Istanbul. Monthly time series data between 1991 and 2004 for 25 chemical, biological, and physical water properties of Big Melen stream were separated into linear trend, seasonality, and error components using additive decomposition models. Water quality index (WQI) derived from 17 water quality variables were used to compare Aksu upstream and Big Melen downstream water quality. Twenty-six additive decomposition models of water quality time series data including WQI had R (2) values ranging from 88% for log(water temperature) (P < or = 0.001) to 3% for log(total dissolved solids) (P < or = 0.026). Linear trend models revealed that total hardness, calcium concentration, and log(nitrite concentration) had the highest rate of increase over time. Tukey's multiple comparison pointed to significant decreases in 17 water quality variables including WQI of Big Melen downstream relative to those of Aksu upstream (P < or = 0.001). Monitoring changes in water quality on the basis of watersheds through WQI and decomposition analysis of time series data paves the way for an adaptive management process of water resources that can be tailored in response to effectiveness and dynamics of management practices. PMID:19444637
Hazledine, Saul; Sun, Jongho; Wysham, Derin; Downie, J. Allan; Oldroyd, Giles E. D.; Morris, Richard J.
2009-01-01
Legume plants form beneficial symbiotic interactions with nitrogen fixing bacteria (called rhizobia), with the rhizobia being accommodated in unique structures on the roots of the host plant. The legume/rhizobial symbiosis is responsible for a significant proportion of the global biologically available nitrogen. The initiation of this symbiosis is governed by a characteristic calcium oscillation within the plant root hair cells and this signal is activated by the rhizobia. Recent analyses on calcium time series data have suggested that stochastic effects have a large role to play in defining the nature of the oscillations. The use of multiple nonlinear time series techniques, however, suggests an alternative interpretation, namely deterministic chaos. We provide an extensive, nonlinear time series analysis on the nature of this calcium oscillation response. We build up evidence through a series of techniques that test for determinism, quantify linear and nonlinear components, and measure the local divergence of the system. Chaos is common in nature and it seems plausible that properties of chaotic dynamics might be exploited by biological systems to control processes within the cell. Systems possessing chaotic control mechanisms are more robust in the sense that the enhanced flexibility allows more rapid response to environmental changes with less energetic costs. The desired behaviour could be most efficiently targeted in this manner, supporting some intriguing speculations about nonlinear mechanisms in biological signaling. PMID:19675679
InSAR time series analysis of crustal deformation in southern California from 1992-2010
NASA Astrophysics Data System (ADS)
Liu, Z.; Lundgren, P.
2010-12-01
Since early the 1990’s, Interferometric Satellite Aperture Radar (InSAR) data has had some success imaging surface deformation of plate boundary deformation zones. The ~18 years of extensive data collection over southern California now make it possible to generate a long time interval InSAR-based line-of-sight (LOS) velocity map to examine the resolution of both steady-state and transient deformation processes. We perform InSAR time series analysis on an extensive catalog of ERS-1/2 and Envisat data from 1992 up to the present in southern California by applying a variant of the Small Baseline Subset (SBAS) time series analysis approach. Despite the limitation imposed by atmospheric phase delay, the large number of data acquisitions and long duration of data sampling allow us to effectively suppress the atmospheric noise through spatiotemporal smoothing in the time series analysis. We integrate an updated version of a California GPS velocity solution with InSAR to constrain the long wavelength deformation signals while estimating and removing the effect of orbital error. A large number of interferograms (> 800) over 5 tracks in southern California have been processed and analyzed. We examine the time dependency of resulting deformation patterns. Preliminary results from the ~18 year time series already reveal some interesting features. For example, the InSAR LOS displacements show significant transient variations in greater spatial resolution following the 1999 Mw7.1 Hector Mine earthquake. The 7-year post-seismic rate map demonstrates a broad transient deformation pattern and much localized deformation near the fault surface trace, reflecting a combined effect from afterslip, poroelastic, and viscoelastic relaxation at different spatiotemporal scales. We observe a variation of deformation rate across the Blackwater-Little lake fault system in the Eastern California Shear Zone, suggesting a possible transient variation over this part of the plate boundary. The In
On the Impact of a Quadratic Acceleration Term in the Analysis of Position Time Series
NASA Astrophysics Data System (ADS)
Bogusz, Janusz; Klos, Anna; Bos, Machiel Simon; Hunegnaw, Addisu; Teferle, Felix Norman
2016-04-01
The analysis of Global Navigation Satellite System (GNSS) position time series generally assumes that each of the coordinate component series is described by the sum of a linear rate (velocity) and various periodic terms. The residuals, the deviations between the fitted model and the observations, are then a measure of the epoch-to-epoch scatter and have been used for the analysis of the stochastic character (noise) of the time series. Often the parameters of interest in GNSS position time series are the velocities and their associated uncertainties, which have to be determined with the highest reliability. It is clear that not all GNSS position time series follow this simple linear behaviour. Therefore, we have added an acceleration term in the form of a quadratic polynomial function to the model in order to better describe the non-linear motion in the position time series. This non-linear motion could be a response to purely geophysical processes, for example, elastic rebound of the Earth's crust due to ice mass loss in Greenland, artefacts due to deficiencies in bias mitigation models, for example, of the GNSS satellite and receiver antenna phase centres, or any combination thereof. In this study we have simulated 20 time series with different stochastic characteristics such as white, flicker or random walk noise of length of 23 years. The noise amplitude was assumed at 1 mm/y-/4. Then, we added the deterministic part consisting of a linear trend of 20 mm/y (that represents the averaged horizontal velocity) and accelerations ranging from minus 0.6 to plus 0.6 mm/y2. For all these data we estimated the noise parameters with Maximum Likelihood Estimation (MLE) using the Hector software package without taken into account the non-linear term. In this way we set the benchmark to then investigate how the noise properties and velocity uncertainty may be affected by any un-modelled, non-linear term. The velocities and their uncertainties versus the accelerations for
Teaching time-series analysis. I. Finite Fourier analysis of ocean waves
NASA Astrophysics Data System (ADS)
Whitford, Dennis J.; Vieira, Mario E. C.; Waters, Jennifer K.
2001-04-01
The introduction of students to methods of time-series analysis is a pedagogical challenge, since the availability of easily manipulated computer software presents an attractive alternative to an understanding of the computations, as well as their assumptions and limitations. A two-part pedagogical tutorial exercise is offered as a hands-on laboratory to complement classroom discussions or as a reference for students involved in independent research projects. The exercises are focused on the analysis of ocean waves, specifically wind-generated surface gravity waves. The exercises are cross-disciplinary in nature and can be extended to any other field dealing with random signal analysis. The first exercise introduces the manual arithmetic steps of a finite Fourier analysis of a wave record, develops a spectrum, and compares these results to the results obtained using a fast Fourier transform (FFT). The second part of the exercise, described in the subsequent article, takes a longer wave record and addresses the theoretical and observed wave probability distributions of wave heights and sea surface elevations. These results are then compared to a FFT, thus linking the two pedagogical laboratory exercise parts for a more complete understanding of both exercises.
Studies in astronomical time series analysis. I - Modeling random processes in the time domain
NASA Technical Reports Server (NTRS)
Scargle, J. D.
1981-01-01
Several random process models in the time domain are defined and discussed. Attention is given to the moving average model, the autoregressive model, and relationships between and combinations of these models. Consideration is then given to methods for investigating pulse structure, procedures of model construction, computational methods, and numerical experiments. A FORTRAN algorithm of time series analysis has been developed which is relatively stable numerically. Results of test cases are given to study the effect of adding noise and of different distributions for the pulse amplitudes. A preliminary analysis of the light curve of the quasar 3C 272 is considered as an example.
Extracting tidal frequencies using multivariate harmonic analysis of sea level height time series
NASA Astrophysics Data System (ADS)
Amiri-Simkooei, A. R.; Zaminpardaz, S.; Sharifi, M. A.
2014-10-01
This contribution is seen as a first attempt to extract the tidal frequencies using a multivariate spectral analysis method applied to multiple time series of tide-gauge records. The existing methods are either physics-based in which the ephemeris of Moon, Sun and other planets are used, or are observation-based in which univariate analysis methods—Fourier and wavelet for instance—are applied to tidal observations. The existence of many long tide-gauge records around the world allows one to use tidal observations and extract the main tidal constituents for which efficient multivariate methods are to be developed. This contribution applies the multivariate least-squares harmonic estimation (LS-HE) to the tidal time series of the UK tide-gauge stations. The first 413 harmonics of the tidal constituents and their nonlinear components are provided using the multivariate LS-HE. A few observations of the research are highlighted: (1) the multivariate analysis takes information of multiple time series into account in an optimal least- squares sense, and thus the tidal frequencies have higher detection power compared to the univariate analysis. (2) Dominant tidal frequencies range from the long-term signals to the sixth-diurnal species interval. Higher frequencies have negligible effects. (3) The most important tidal constituents (the first 50 frequencies) ordered from their amplitudes range from 212 cm (M2) to 1 cm (OQ2) for the data set considered. There are signals in this list that are not available in the 145 main tidal frequencies of the literature. (4) Tide predictions using different lists of tidal frequencies on five different data sets around the world are compared. The prediction results using the first significant 50 constituents provided promising results on these locations of the world.
Sinking Chao Phraya delta plain, Thailand, derived from SAR interferometry time series analysis
NASA Astrophysics Data System (ADS)
Tanaka, A.; Mio, A.; Saito, Y.
2013-12-01
The Bangkok Metropolitan region and its surrounding provinces are located in a low-lying delta plain of the Chao Phraya River. Extensive groundwater use from the late 1950s has caused the decline of groundwater levels in the aquifers and Holocene clay compaction beneath the Bangkok Region, resulting in significant subsidence of the ground. This ground deformation has been monitored using leveling surveys since 1978, and differential InSAR (Interferometric Synthetic Aperture Radar) analysis. It shows that the Bangkok Metropolitan region is subsiding at a rate of about 20 mm/year during the recent years due to law-limited groundwater pumping, although the highest subsidence rate as high as 120 mm/year was recorded in 1981. The subsidence rate in the Bangkok area has significantly decreased since the late 1980s; however, the affected area has spread out to the surrounding areas. The maximum subsidence rate up to 30 mm/year occurred in the outlying southeast and southwest coastal zones in 2002. In this study, we apply a SAR interferometry time series analysis to monitor ground deformations in the lower Chao Phraya delta plain (Lower Central Plain), Thailand, using ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array type L-band SAR) data acquired between July 2007 and September 2010. We derive a single reference time series interferogram from the stacking of unwrapped phases under the assumptions that those phases are smoothly and continuously connected, and apply a smoothness-constrained inversion algorithm that optimizes the displacement from the phase unwrapping of multitemporal differential SAR interferograms. The SAR interferometry time series analysis succeeds to monitor the incremental line-of-sight (LOS)-change between SAR scene acquisitions. LOS displacements are converted to vertical displacements, based on the assumption that the ground displacement in this area occurs only in the vertical directions. This reveals an overall pattern of subsidence
Multifractal analysis of geophysical time series in the urban lake of Créteil (France).
NASA Astrophysics Data System (ADS)
Mezemate, Yacine; Tchiguirinskaia, Ioulia; Bonhomme, Celine; Schertzer, Daniel; Lemaire, Bruno Jacques; Vinçon leite, Brigitte; Lovejoy, Shaun
2013-04-01
Urban water bodies take part in the environmental quality of the cities. They regulate heat, contribute to the beauty of landscape and give some space for leisure activities (aquatic sports, swimming). As they are often artificial they are only a few meters deep. It confers them some specific properties. Indeed, they are particularly sensitive to global environmental changes, including climate change, eutrophication and contamination by micro-pollutants due to the urbanization of the watershed. Monitoring their quality has become a major challenge for urban areas. The need for a tool for predicting short-term proliferation of potentially toxic phytoplankton therefore arises. In lakes, the behavior of biological and physical (temperature) fields is mainly driven by the turbulence regime in the water. Turbulence is highly non linear, nonstationary and intermittent. This is why statistical tools are needed to characterize the evolution of the fields. The knowledge of the probability distribution of all the statistical moments of a given field is necessary to fully characterize it. This possibility is offered by the multifractal analysis based on the assumption of scale invariance. To investigate the effect of space-time variability of temperature, chlorophyll and dissolved oxygen on the cyanobacteria proliferation in the urban lake of Creteil (France), a spectral analysis is first performed on each time series (or on subsamples) to have an overall estimate of their scaling behaviors. Then a multifractal analysis (Trace Moment, Double Trace Moment) estimates the statistical moments of different orders. This analysis is adapted to the specific properties of the studied time series, i. e. the presence of large scale gradients. The nonlinear behavior of the scaling functions K(q) confirms that the investigated aquatic time series are indeed multifractal and highly intermittent .The knowledge of the universal multifractal parameters is the key to calculate the different
Analysis of trends and breakpoints in observed discharge time series in Lower Saxony, Germany
NASA Astrophysics Data System (ADS)
Fangmann, Anne; Belli, Aslan; Haberlandt, Uwe
2013-04-01
Historical streamflow in the federal state of Lower Saxony, Germany was analyzed for potential trends and breakpoints. The investigation was based on time series of daily mean discharge values in the periods 1951 to 2005, for which 34 gauging stations showed a sufficient record length, and 1966 to 2005, for which 110 gauges were available. Indices characterizing both high and low flow conditions, as well as the mean discharge within a year and the individual seasons, were extracted from the daily time series and subjected to statistical analyses, including the estimation of trend direction, slope and local and global significance, as well as a breakpoint analysis. Simultaneously, several precipitation and temperature indices were tested for trends in the exact same manner, in order to investigate alterations in the atmospheric driving forces as potential causes for changes in the hydrological regime. 263 precipitation and 18 temperature stations provided the daily data from 1951 to 2005. For the discharge the largest significant changes could be noted in summer, where low, high and medium flows decreased throughout. Spatially, these downward trends proved strongest in the eastern half of Lower Saxony. A breakpoint analysis revealed that a large portion of gauging stations feature breaks in the summer indicator time series in 1988, after which a trend reversal, i.e. an increase in discharge, was observed. In spring and fall, a spatial differentiation between an increase in the northwest and a decrease in the southeast were found for the low flow. In winter, an increasing tendency in all discharge portions could be noted, but merely the trends in the flood indices proved field significant. Generally, the trends in discharge were found consistent with those in temperature and especially precipitation. For the mean temperature, consistently strong, positive, significant trends were detected, while the analysis of the precipitation indices revealed increases in winter
Finite element techniques in computational time series analysis of turbulent flows
NASA Astrophysics Data System (ADS)
Horenko, I.
2009-04-01
In recent years there has been considerable increase of interest in the mathematical modeling and analysis of complex systems that undergo transitions between several phases or regimes. Such systems can be found, e.g., in weather forecast (transitions between weather conditions), climate research (ice and warm ages), computational drug design (conformational transitions) and in econometrics (e.g., transitions between different phases of the market). In all cases, the accumulation of sufficiently detailed time series has led to the formation of huge databases, containing enormous but still undiscovered treasures of information. However, the extraction of essential dynamics and identification of the phases is usually hindered by the multidimensional nature of the signal, i.e., the information is "hidden" in the time series. The standard filtering approaches (like f.~e. wavelets-based spectral methods) have in general unfeasible numerical complexity in high-dimensions, other standard methods (like f.~e. Kalman-filter, MVAR, ARCH/GARCH etc.) impose some strong assumptions about the type of the underlying dynamics. Approach based on optimization of the specially constructed regularized functional (describing the quality of data description in terms of the certain amount of specified models) will be introduced. Based on this approach, several new adaptive mathematical methods for simultaneous EOF/SSA-like data-based dimension reduction and identification of hidden phases in high-dimensional time series will be presented. The methods exploit the topological structure of the analysed data an do not impose severe assumptions on the underlying dynamics. Special emphasis will be done on the mathematical assumptions and numerical cost of the constructed methods. The application of the presented methods will be first demonstrated on a toy example and the results will be compared with the ones obtained by standard approaches. The importance of accounting for the mathematical
NASA Astrophysics Data System (ADS)
Chen, Wei-Shing
2011-04-01
The aim of the article is to answer the question if the Taiwan unemployment rate dynamics is generated by a non-linear deterministic dynamic process. This paper applies a recurrence plot and recurrence quantification approach based on the analysis of non-stationary hidden transition patterns of the unemployment rate of Taiwan. The case study uses the time series data of the Taiwan’s unemployment rate during the period from 1978/01 to 2010/06. The results show that recurrence techniques are able to identify various phases in the evolution of unemployment transition in Taiwan.
Time series analysis of Adaptive Optics wave-front sensor telemetry data
Poyneer, L A; Palmer, D
2004-03-22
Time series analysis techniques are applied to wave-front sensor telemetry data from the Lick Adaptive Optics System. For 28 fully-illuminated subapertures, telemetry data of 4096 consecutive slope estimates for each subaperture are available. The primary problem is performance comparison of alternative wave-front sensing algorithms. Using direct comparison of data in open loop and closed-loop trials, we analyze algorithm performance in terms of gain, noise and residual power. We also explore the benefits of multi-input Wiener filtering and analyze the open-loop and closed-loop spatial correlations of the sensor measurements.
NASA Astrophysics Data System (ADS)
Silvestri, M.; Buongiorno, M. F.; Pieri, D. C.
2014-12-01
To monitoring of active volcanoes the systematic acquisition of medium/high resolution thermal data and the subsequent analysis of time series may improve the capability to detect small surface temperature variation related to changes in volcanic activity level and contribute to the early warning systems. Examples on the processing of long time series based EO data of Mt Etna activity and Phlegraean Fields observation by using remote sensing techniques and at different spatial resolution data (ASTER - 90mt, AVHRR -1km, MODIS-1km, MSG SEVIRI-3km) are showed. The use of TIR sensors with high spatial resolution offers the possibility to obtain detailed information on the areas where there are significant changes, detecting variation in fumaroles fields and summit craters before eruptions. Thanks to ASTER thermal infrared (TIR, 5 bands) regions of the electromagnetic spectrum we have obtained the surface temperature map on the volcano area. For this study we have considered the ASTER's night observations that show well defined episodes of increasing thermal emission of crater thanks to a more uniform background temperature. Two different procedures are shown, both using the TIR high spatial resolution data: for Phlegraean Fields (active but quiescent volcano) the analysis of time series of surface temperature which may improve the capability to detect small surface temperature variation related to changes in volcanic activity level; for Mt. Etna (active volcano) a semi-automatic procedure which extract the summit area radiance values with the goal of detecting variation related to eruptive events. The advantage of direct download of EO data by means INGV antennas even though low spatial resolution offers the possibility of a systematic data processing having a daily updating of information for prompt response and hazard mitigation. At the same time the comparison of surface temperature retrievals at different scale is an important issue for future satellite sensors.
Blind summarization: content-adaptive video summarization using time-series analysis
NASA Astrophysics Data System (ADS)
Divakaran, Ajay; Radhakrishnan, Regunathan; Peker, Kadir A.
2006-01-01
Severe complexity constraints on consumer electronic devices motivate us to investigate general-purpose video summarization techniques that are able to apply a common hardware setup to multiple content genres. On the other hand, we know that high quality summaries can only be produced with domain-specific processing. In this paper, we present a time-series analysis based video summarization technique that provides a general core to which we are able to add small content-specific extensions for each genre. The proposed time-series analysis technique consists of unsupervised clustering of samples taken through sliding windows from the time series of features obtained from the content. We classify content into two broad categories, scripted content such as news and drama, and unscripted content such as sports and surveillance. The summarization problem then reduces to finding either finding semantic boundaries of the scripted content or detecting highlights in the unscripted content. The proposed technique is essentially an event detection technique and is thus best suited to unscripted content, however, we also find applications to scripted content. We thoroughly examine the trade-off between content-neutral and content-specific processing for effective summarization for a number of genres, and find that our core technique enables us to minimize the complexity of the content-specific processing and to postpone it to the final stage. We achieve the best results with unscripted content such as sports and surveillance video in terms of quality of summaries and minimizing content-specific processing. For other genres such as drama, we find that more content-specific processing is required. We also find that judicious choice of key audio-visual object detectors enables us to minimize the complexity of the content-specific processing while maintaining its applicability to a broad range of genres. We will present a demonstration of our proposed technique at the conference.
Use of a Principal Components Analysis for the Generation of Daily Time Series.
NASA Astrophysics Data System (ADS)
Dreveton, Christine; Guillou, Yann
2004-07-01
A new approach for generating daily time series is considered in response to the weather-derivatives market. This approach consists of performing a principal components analysis to create independent variables, the values of which are then generated separately with a random process. Weather derivatives are financial or insurance products that give companies the opportunity to cover themselves against adverse climate conditions. The aim of a generator is to provide a wider range of feasible situations to be used in an assessment of risk. Generation of a temperature time series is required by insurers or bankers for pricing weather options. The provision of conditional probabilities and a good representation of the interannual variance are the main challenges of a generator when used for weather derivatives. The generator was developed according to this new approach using a principal components analysis and was applied to the daily average temperature time series of the Paris-Montsouris station in France. The observed dataset was homogenized and the trend was removed to represent correctly the present climate. The results obtained with the generator show that it represents correctly the interannual variance of the observed climate; this is the main result of the work, because one of the main discrepancies of other generators is their inability to represent accurately the observed interannual climate variance—this discrepancy is not acceptable for an application to weather derivatives. The generator was also tested to calculate conditional probabilities: for example, the knowledge of the aggregated value of heating degree-days in the middle of the heating season allows one to estimate the probability if reaching a threshold at the end of the heating season. This represents the main application of a climate generator for use with weather derivatives.
Online Time Series Analysis of Land Products over Asia Monsoon Region via Giovanni
NASA Technical Reports Server (NTRS)
Shen, Suhung; Leptoukh, Gregory G.; Gerasimov, Irina
2011-01-01
Time series analysis is critical to the study of land cover/land use changes and climate. Time series studies at local-to-regional scales require higher spatial resolution, such as 1km or less, data. MODIS land products of 250m to 1km resolution enable such studies. However, such MODIS land data files are distributed in 10ox10o tiles, due to large data volumes. Conducting a time series study requires downloading all tiles that include the study area for the time period of interest, and mosaicking the tiles spatially. This can be an extremely time-consuming process. In support of the Monsoon Asia Integrated Regional Study (MAIRS) program, NASA GES DISC (Goddard Earth Sciences Data and Information Services Center) has processed MODIS land products at 1 km resolution over the Asia monsoon region (0o-60oN, 60o-150oE) with a common data structure and format. The processed data have been integrated into the Giovanni system (Goddard Interactive Online Visualization ANd aNalysis Infrastructure) that enables users to explore, analyze, and download data over an area and time period of interest easily. Currently, the following regional MODIS land products are available in Giovanni: 8-day 1km land surface temperature and active fire, monthly 1km vegetation index, and yearly 0.05o, 500m land cover types. More data will be added in the near future. By combining atmospheric and oceanic data products in the Giovanni system, it is possible to do further analyses of environmental and climate changes associated with the land, ocean, and atmosphere. This presentation demonstrates exploring land products in the Giovanni system with sample case scenarios.
Time Series Analysis of Onchocerciasis Data from Mexico: A Trend towards Elimination
Pérez-Rodríguez, Miguel A.; Adeleke, Monsuru A.; Orozco-Algarra, María E.; Arrendondo-Jiménez, Juan I.; Guo, Xianwu
2013-01-01
Background In Latin America, there are 13 geographically isolated endemic foci distributed among Mexico, Guatemala, Colombia, Venezuela, Brazil and Ecuador. The communities of the three endemic foci found within Mexico have been receiving ivermectin treatment since 1989. In this study, we predicted the trend of occurrence of cases in Mexico by applying time series analysis to monthly onchocerciasis data reported by the Mexican Secretariat of Health between 1988 and 2011 using the software R. Results A total of 15,584 cases were reported in Mexico from 1988 to 2011. The data of onchocerciasis cases are mainly from the main endemic foci of Chiapas and Oaxaca. The last case in Oaxaca was reported in 1998, but new cases were reported in the Chiapas foci up to 2011. Time series analysis performed for the foci in Mexico showed a decreasing trend of the disease over time. The best-fitted models with the smallest Akaike Information Criterion (AIC) were Auto-Regressive Integrated Moving Average (ARIMA) models, which were used to predict the tendency of onchocerciasis cases for two years ahead. According to the ARIMA models predictions, the cases in very low number (below 1) are expected for the disease between 2012 and 2013 in Chiapas, the last endemic region in Mexico. Conclusion The endemic regions of Mexico evolved from high onchocerciasis-endemic states to the interruption of transmission due to the strategies followed by the MSH, based on treatment with ivermectin. The extremely low level of expected cases as predicted by ARIMA models for the next two years suggest that the onchocerciasis is being eliminated in Mexico. To our knowledge, it is the first study utilizing time series for predicting case dynamics of onchocerciasis, which could be used as a benchmark during monitoring and post-treatment surveillance. PMID:23459370
Characterization of Ground Deformation above AN Urban Tunnel by Means of Insar Time Series Analysis
NASA Astrophysics Data System (ADS)
Ferretti, A.; Iannacone, J.; Falorni, G.; Berti, M.; Corsini, A.
2013-12-01
Ground deformation produced by tunnel excavation in urban areas can cause damage to buildings and infrastructure. In these contexts, monitoring systems are required to determine the surface area affected by displacement and the rates of movement. Advanced multi-image satellite-based InSAR approaches are uniquely suited for this purpose as they provide an overview of the entire affected area and can measure movement rates with millimeter precision. Persistent scatterer approaches such as SqueeSAR™ use reflections off buildings, lampposts, roads, etc to produce a high-density point cloud in which each point has a time series of deformation spanning the period covered by the imagery. We investigated an area of about 10 km2 in North Vancouver, (Canada) where the shaft excavation of the Seymour-Capilano water filtration plant was started in 2004. As part of the project, twin tunnels in bedrock were excavated to transfer water from the Capilano Reservoir to the treatment plant. A radar dataset comprising 58 images (spanning March 2001 - June 2008) acquired by the Radarsat-1 satellite and covering the period of excavation was processed with the SqueeSAR™ algorithm (Ferretti et al., 2011) to assess the ground deformation caused by the tunnel excavation. To better characterize the deformation in the time and space domains and correlate ground movement with excavation, an in-depth time series analysis was carried out. Berti et al. (2013) developed an automatic procedure for the analysis of InSAR time series based on a sequence of statistical tests. The tool classifies time series into six distinctive types (uncorrelated; linear; quadratic; bilinear; discontinuous without constant velocity; discontinuous with change in velocity) which can be linked to different physical phenomena. It also provides a series of descriptive parameters which can be used to characterize the temporal changes of ground motion. We processed the movement time series with PSTime to determine the
Wet tropospheric delays forecast based on Vienna Mapping Function time series analysis
NASA Astrophysics Data System (ADS)
Rzepecka, Zofia; Kalita, Jakub
2016-04-01
It is well known that the dry part of the zenith tropospheric delay (ZTD) is much easier to model than the wet part (ZTW). The aim of the research is applying stochastic modeling and prediction of ZTW using time series analysis tools. Application of time series analysis enables closer understanding of ZTW behavior as well as short-term prediction of future ZTW values. The ZTW data used for the studies were obtained from the GGOS service hold by Vienna technical University. The resolution of the data is six hours. ZTW for the years 2010 -2013 were adopted for the study. The International GNSS Service (IGS) permanent stations LAMA and GOPE, located in mid-latitudes, were admitted for the investigations. Initially the seasonal part was separated and modeled using periodic signals and frequency analysis. The prominent annual and semi-annual signals were removed using sines and consines functions. The autocorrelation of the resulting signal is significant for several days (20-30 samples). The residuals of this fitting were further analyzed and modeled with ARIMA processes. For both the stations optimal ARMA processes based on several criterions were obtained. On this basis predicted ZTW values were computed for one day ahead, leaving the white process residuals. Accuracy of the prediction can be estimated at about 3 cm.
Time-series analysis for determining vertical air permeability in unsaturated zones
Lu, N.
1999-01-01
The air pressure in the unsaturated subsurface changes dynamically as the barometric pressure varies with time. Depending on the material properties and boundary conditions, the intensity of the correlation between the atmospheric and subsurface pressures may be evidenced in two persistent patterns: (1) the amplitude attenuation; and (2) the phase lag for the principal modes, such as the diurnal, semidiurnal, and 8-h tides. The amplitude attenuation and the phase lag generally depend on properties that can be classified into two categories: (1) The barometric pressure parameters, such as the apparent pressure amplitudes and frequencies controlled by the atmospheric tides and others; and (2) the material properties of porous media, such as the air viscosity, air-filled porosity, and permeability. Based on the principle of superposition and a Fourier time-series analysis, an analytical solution for predicting the subsurface air pressure variation caused by the atmospheric pressure fluctuation is presented. The air permeability (or pneumatic diffusivity) can be quantitatively determined by using the calculated amplitude attenuations (or phase lags) and the appropriate analytical relations among the parameters of the atmosphere and the porous medium. An analysis using the field data shows that the Fourier time-series analysis may provide a potentially reliable and simple method for predicting the subsurface barometric pressure variation and for determining the air permeability of unsaturated zones.
Spectral analysis of hydrological time series of a river basin in southern Spain
NASA Astrophysics Data System (ADS)
Luque-Espinar, Juan Antonio; Pulido-Velazquez, David; Pardo-Igúzquiza, Eulogio; Fernández-Chacón, Francisca; Jiménez-Sánchez, Jorge; Chica-Olmo, Mario
2016-04-01
Spectral analysis has been applied with the aim to determine the presence and statistical significance of climate cycles in data series from different rainfall, piezometric and gauging stations located in upper Genil River Basin. This river starts in Sierra Nevada Range at 3,480 m a.s.l. and is one of the most important rivers of this region. The study area has more than 2.500 km2, with large topographic differences. For this previous study, we have used more than 30 rain data series, 4 piezometric data series and 3 data series from gauging stations. Considering a monthly temporal unit, the studied period range from 1951 to 2015 but most of the data series have some lacks. Spectral analysis is a methodology widely used to discover cyclic components in time series. The time series is assumed to be a linear combination of sinusoidal functions of known periods but of unknown amplitude and phase. The amplitude is related with the variance of the time series, explained by the oscillation at each frequency (Blackman and Tukey, 1958, Bras and Rodríguez-Iturbe, 1985, Chatfield, 1991, Jenkins and Watts, 1968, among others). The signal component represents the structured part of the time series, made up of a small number of embedded periodicities. Then, we take into account the known result for the one-sided confidence band of the power spectrum estimator. For this study, we established confidence levels of <90%, 90%, 95%, and 99%. Different climate signals have been identified: ENSO, QBO, NAO, Sun Spot cycles, as well as others related to sun activity, but the most powerful signals correspond to the annual cycle, followed by the 6 month and NAO cycles. Nevertheless, significant differences between rain data series and piezometric/flow data series have been pointed out. In piezometric data series and flow data series, ENSO and NAO signals could be stronger than others with high frequencies. The climatic peaks in lower frequencies in rain data are smaller and the confidence
InSAR and GPS time series analysis: Crustal deformation in the Yucca Mountain, Nevada region
NASA Astrophysics Data System (ADS)
Li, Z.; Hammond, W. C.; Blewitt, G.; Kreemer, C. W.; Plag, H.
2010-12-01
Several previous studies have successfully demonstrated that long time series (e.g. >5 years) of GPS measurements can be employed to detect tectonic signals with a vertical rate greater than 0.3 mm/yr (e.g. Hill and Blewitt, 2006; Bennett et al. 2009). However, GPS stations are often sparse, with spacing from a few kilometres to a few hundred kilometres. Interferometric SAR (InSAR) can complement GPS by providing high horizontal spatial resolution (e.g. meters to tens-of metres) over large regions (e.g. 100 km × 100 km). A major source of error for repeat-pass InSAR is the phase delay in radio signal propagation through the atmosphere. The portion of this attributable to tropospheric water vapour causes errors as large as 10-20 cm in deformation retrievals. InSAR Time Series analysis with Atmospheric Estimation Models (InSAR TS + AEM), developed at the University of Glasgow, is a robust time series analysis approach, which mainly uses interferograms with small geometric baselines to minimise the effects of decorrelation and inaccuracies in topographic data. In addition, InSAR TS + AEM can be used to separate deformation signals from atmospheric water vapour effects in order to map surface deformation as it evolves in time. The principal purposes of this study are to assess: (1) how consistent InSAR-derived deformation time series are with GPS; and (2) how precise InSAR-derived atmospheric path delays can be. The Yucca Mountain, Nevada region is chosen as the study site because of its excellent GPS network and extensive radar archives (>10 years of dense and high-quality GPS stations, and >17 years of ERS and ENVISAT radar acquisitions), and because of its arid environment. The latter results in coherence that is generally high, even for long periods that span the existing C-band radar archives of ERS and ENVISAT. Preliminary results show that our InSAR LOS deformation map agrees with GPS measurements to within 0.35 mm/yr RMS misfit at the stations which is the
Analysis of temporal correlations in GPS time series: comparison between different methods
NASA Astrophysics Data System (ADS)
Barzaghi, R.; Borghi, A.; Cannizzaro, L.
2009-04-01
Previous works (Agnew, 1992; Langbein et al., 1997; Zhang et al., 1997; Mao et al., 1999; Williams, 2003; Williams et al., 2004; Amiri-Simkoeii et al., 2007) have proved that the daily GPS time series are characterized by coloured noise. The Power Law Noise Process (PLNP) method has been generally adopted to describe the noise of continuous GPS observations. We suggest a different methodology to define the stochastic model of time series of position estimates for permanent GPS stations: when the residual data, after the linear and periodic trend reduction is performed, behave as a stationary and ergodic stochastic process, we suggest to define the noise characteristics of the GPS signal studying the Empirical Covariance Function (ECF). In principle, whether the stationary condition is satisfied, the two methodologies, PNLP with the estimate of the fractional spectral index and ECF, should give the same results, because they face the correlation analysis problem by a dual point of view: frequency and time domain respectively. However, due to the long computational time especially for long time series, the PLNP model is often approximated by fixing the spectral index to the value of the flicker noise. In this case we think that the results obtained by means of the ECF method are more rigorous than those obtained by fixing the spectral index, because it reflects, via covariance estimation, the proper stochastic structure of the data. Moreover, the ECF method have no computational burden respect to PLNP method with fractional spectral index estimation. The PLNP and ECF methodologies have been compared on a set of 70 Italian GPS stations, with variable observation windows, from a minimum of three years up to over 10 years.
Wasserstein distances in the analysis of time series and dynamical systems
NASA Astrophysics Data System (ADS)
Muskulus, Michael; Verduyn-Lunel, Sjoerd
2011-01-01
A new approach based on Wasserstein distances, which are numerical costs of an optimal transportation problem, allows us to analyze nonlinear phenomena in a robust manner. The long-term behavior is reconstructed from time series, resulting in a probability distribution over phase space. Each pair of probability distributions is then assigned a numerical distance that quantifies the differences in their dynamical properties. From the totality of all these distances a low-dimensional representation in a Euclidean space is derived, in which the time series can be classified and statistically analyzed. This representation shows the functional relationships between the dynamical systems under study. It allows us to assess synchronization properties and also offers a new way of numerical bifurcation analysis. The statistical techniques for this distance-based analysis of dynamical systems are presented, filling a gap in the literature, and their application is discussed in a few examples of datasets arising in physiology and neuroscience, and in the well-known Hénon system.
Population-level administration of AlcoholEdu for college: an ARIMA time-series analysis.
Wyatt, Todd M; Dejong, William; Dixon, Elizabeth
2013-08-01
Autoregressive integrated moving averages (ARIMA) is a powerful analytic tool for conducting interrupted time-series analysis, yet it is rarely used in studies of public health campaigns or programs. This study demonstrated the use of ARIMA to assess AlcoholEdu for College, an online alcohol education course for first-year students, and other health and safety programs introduced at a moderate-size public university in the South. From 1992 to 2009, the university administered annual Core Alcohol and Drug Surveys to samples of undergraduates (Ns = 498 to 1032). AlcoholEdu and other health and safety programs that began during the study period were assessed through a series of quasi-experimental ARIMA analyses. Implementation of AlcoholEdu in 2004 was significantly associated with substantial decreases in alcohol consumption and alcohol- or drug-related negative consequences. These improvements were sustained over time as succeeding first-year classes took the course. Previous studies have shown that AlcoholEdu has an initial positive effect on students' alcohol use and associated negative consequences. This investigation suggests that these positive changes may be sustainable over time through yearly implementation of the course with first-year students. ARIMA time-series analysis holds great promise for investigating the effect of program and policy interventions to address alcohol- and drug-related problems on campus. PMID:23742712
NASA Astrophysics Data System (ADS)
Harikrishnan, K. P.; Misra, R.; Ambika, G.
2009-09-01
We show that the combined use of correlation dimension (D2) and correlation entropy (K2) as discriminating measures can extract a more accurate information regarding the different types of noise present in a time series data. For this, we make use of an algorithmic approach for computing D2 and K2 proposed by us recently [Harikrishnan KP, Misra R, Ambika G, Kembhavi AK. Physica D 2006;215:137; Harikrishnan KP, Ambika G, Misra R. Mod Phys Lett B 2007;21:129; Harikrishnan KP, Misra R, Ambika G. Pramana - J Phys, in press], which is a modification of the standard Grassberger-Proccacia scheme. While the presence of white noise can be easily identified by computing D2 of data and surrogates, K2 is a better discriminating measure to detect colored noise in the data. Analysis of time series from a real world system involving both white and colored noise is presented as evidence. To our knowledge, this is the first time that such a combined analysis is undertaken on a real world data.
Cross-recurrence quantification analysis of categorical and continuous time series: an R package
Coco, Moreno I.; Dale, Rick
2014-01-01
This paper describes the R package crqa to perform cross-recurrence quantification analysis of two time series of either a categorical or continuous nature. Streams of behavioral information, from eye movements to linguistic elements, unfold over time. When two people interact, such as in conversation, they often adapt to each other, leading these behavioral levels to exhibit recurrent states. In dialog, for example, interlocutors adapt to each other by exchanging interactive cues: smiles, nods, gestures, choice of words, and so on. In order for us to capture closely the goings-on of dynamic interaction, and uncover the extent of coupling between two individuals, we need to quantify how much recurrence is taking place at these levels. Methods available in crqa would allow researchers in cognitive science to pose such questions as how much are two people recurrent at some level of analysis, what is the characteristic lag time for one person to maximally match another, or whether one person is leading another. First, we set the theoretical ground to understand the difference between “correlation” and “co-visitation” when comparing two time series, using an aggregative or cross-recurrence approach. Then, we describe more formally the principles of cross-recurrence, and show with the current package how to carry out analyses applying them. We end the paper by comparing computational efficiency, and results’ consistency, of crqa R package, with the benchmark MATLAB toolbox crptoolbox (Marwan, 2013). We show perfect comparability between the two libraries on both levels. PMID:25018736
A Wavelet Time Series Analysis of Aperiodic Variable Stars in the Kepler Field
NASA Astrophysics Data System (ADS)
Arnold, Timothy; Mighell, K.; Howell, S.
2009-12-01
The variable sky offers insights into the physical mechanisms of astronomical objects and can be used as a useful tool for many other purposes like the determination of distance with standard candles. Periodic variables were the first to be classified, understood, and used. Many variable but aperiodic light curves are discarded or insufficiently analyzed because of the apparent uselessness of the information contained in these data. Many contemporary projects (e.g. the Large Synoptic Survey Telescope, PanSTARRS, the Kepler mission) aim to map the transient sky, and recently methods of time series analysis have become increasingly advanced. It would be advantageous to discover identifying information in the large number of variable but ostensibly aperiodic light curves. We use a wavelet analysis, based on a weighted projection of time series data on to basis functions, to analyze aperiodic variable stars in the Burrell-Optical-Kepler Survey (BOKS). Using the Weighted Wavelet Z-Transform detailed in Foster 1996, we find that variable but aperiodic stars in our sample offer few characteristic properties that would be useful for further classification. Arnold's research was supported by the NOAO/KPNO Research Experiences for Undergraduates (REU) Program which is funded by the National Science Foundation Research Experiences for Undergraduates Program and the Department of Defense ASSURE program through Scientific Program Order No. 3 (AST-0243875) of the Cooperative Agreement No. AST-0132798 between the Association of Universities for Research in Astronomy (AURA) and the NSF.
Uniform framework for the recurrence-network analysis of chaotic time series
NASA Astrophysics Data System (ADS)
Jacob, Rinku; Harikrishnan, K. P.; Misra, R.; Ambika, G.
2016-01-01
We propose a general method for the construction and analysis of unweighted ɛ -recurrence networks from chaotic time series. The selection of the critical threshold ɛc in our scheme is done empirically and we show that its value is closely linked to the embedding dimension M . In fact, we are able to identify a small critical range Δ ɛ numerically that is approximately the same for the random and several standard chaotic time series for a fixed M . This provides us a uniform framework for the nonsubjective comparison of the statistical measures of the recurrence networks constructed from various chaotic attractors. We explicitly show that the degree distribution of the recurrence network constructed by our scheme is characteristic to the structure of the attractor and display statistical scale invariance with respect to increase in the number of nodes N . We also present two practical applications of the scheme, detection of transition between two dynamical regimes in a time-delayed system and identification of the dimensionality of the underlying system from real-world data with a limited number of points through recurrence network measures. The merits, limitations, and the potential applications of the proposed method are also highlighted.
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. PMID:24987973
Chang, Howard H.; Fuentes, Montserrat; Frey, H. Christopher
2013-01-01
This paper describes a modeling framework for estimating the acute effects of personal exposure to ambient air pollution in a time series design. First, a spatial hierarchical model is used to relate Census tract-level daily ambient concentrations and simulated exposures for a subset of the study period. The complete exposure time series is then imputed for risk estimation. Modeling exposure via a statistical model reduces the computational burden associated with simulating personal exposures considerably. This allows us to consider personal exposures at a finer spatial resolution to improve exposure assessment and for a longer study period. The proposed approach is applied to an analysis of fine particulate matter of <2.5 μm in aerodynamic diameter (PM2.5) and daily mortality in the New York City metropolitan area during the period 2001–2005. Personal PM2.5 exposures were simulated from the Stochastic Human Exposure and Dose Simulation. Accounting for exposure uncertainty, the authors estimated a 2.32% (95% posterior interval: 0.68, 3.94) increase in mortality per a 10 μg/m3 increase in personal exposure to PM2.5 from outdoor sources on the previous day. The corresponding estimates per a 10 μg/m3 increase in PM2.5 ambient concentration was 1.13% (95% confidence interval: 0.27, 2.00). The risks of mortality associated with PM2.5 were also higher during the summer months. PMID:22669499
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.
NASA Astrophysics Data System (ADS)
Lin, Min; Zhao, Gang; Wang, Gang
2015-12-01
In this study, recurrence plot (RP) and recurrence quantification analysis (RQA) techniques are applied to a magnitude time series composed of seismic events occurred in California region. Using bootstrapping techniques, we give the statistical test of the RQA for detecting dynamical transitions. From our results, we find the different patterns of RPs for magnitude time series before and after the M6.1 Joshua Tree Earthquake. RQA measurements of determinism (DET) and laminarity (LAM) quantifying the order with confidence levels also show peculiar behaviors. It is found that DET and LAM values of the recurrence-based complexity measure significantly increase to a large value at the main shock, and then gradually recovers to a small values after it. The main shock and its aftershock sequences trigger a temporary growth in order and complexity of the deterministic structure in the RP of seismic activity. It implies that the onset of the strong earthquake event is reflected in a sharp and great simultaneous change in RQA measures.
Gravity-driven deformation of Tenerife measured by InSAR time series analysis
NASA Astrophysics Data System (ADS)
Fernández, J.; Tizzani, P.; Manzo, M.; Borgia, A.; González, P. J.; Martí, J.; Pepe, A.; Camacho, A. G.; Casu, F.; Berardino, P.; Prieto, J. F.; Lanari, R.
2009-02-01
We study the state of deformation of Tenerife (Canary Islands) using Differential Synthetic Aperture Radar Interferometry (DInSAR). We apply the Small BAseline Subset (SBAS) DInSAR algorithm to radar images acquired from 1992 to 2005 by the ERS sensors to determine the deformation rate distribution and the time series for the coherent pixels identified in the island. Our analysis reveals that the summit area of the volcanic edifice is characterized by a rather continuous subsidence extending well beyond Las Cañadas caldera rim and corresponding to the dense core of the island. These results, coupled with GPS ones, structural and geological information and deformation modeling, suggest an interpretation based on the gravitational sinking of the dense core of the island into a weak lithosphere and that the volcanic edifice is in a state of compression. We also detect more localized deformation patterns correlated with water table changes and variations in the deformation time series associated with the seismic crisis in 2004.
Extensive mapping of coastal change in Alaska by Landsat time-series analysis, 1972-2013
NASA Astrophysics Data System (ADS)
Reynolds, J.; Macander, M. J.; Swingley, C. S.; Spencer, S. R.
2014-12-01
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. The authors are performing a time-series trend analysis for over 22,000 kilometers of coastline along the Bering Sea and Gulf of Alaska. Ice- and cloud-free Landsat imagery from Landsat 1-8, covering 1972-2013, were analyzed using a combination of regression, changepoint detection, and classification tree approaches to detect, classify, and map changes in near-infrared reflectance. Areas with significant changes in coastal features, as well as timing of dominant changes and, in some cases, rates of change were identified . The approach captured many coastal changes over the 42-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.
2011-01-01
Background A popular model for gene regulatory networks is the Boolean network model. In this paper, we propose an algorithm to perform an analysis of gene regulatory interactions using the Boolean network model and time-series data. Actually, the Boolean network is restricted in the sense that only a subset of all possible Boolean functions are considered. We explore some mathematical properties of the restricted Boolean networks in order to avoid the full search approach. The problem is modeled as a Constraint Satisfaction Problem (CSP) and CSP techniques are used to solve it. Results We applied the proposed algorithm in two data sets. First, we used an artificial dataset obtained from a model for the budding yeast cell cycle. The second data set is derived from experiments performed using HeLa cells. The results show that some interactions can be fully or, at least, partially determined under the Boolean model considered. Conclusions The algorithm proposed can be used as a first step for detection of gene/protein interactions. It is able to infer gene relationships from time-series data of gene expression, and this inference process can be aided by a priori knowledge available. PMID:21554763
Harmonic analysis of environmental time series with missing data or irregular sample spacing.
Dilmaghani, Shabnam; Henry, Isaac C; Soonthornnonda, Puripus; Christensen, Erik R; Henry, Ronald C
2007-10-15
The Lomb periodogram and discrete Fourier transform are described and applied to harmonic analysis of two typical data sets, one air quality time series and one water quality time series. The air quality data is a 13 year series of 24 hour average particulate elemental carbon data from the IMPROVE station in Washington, D.C. The water quality data are from the stormwater monitoring network in Milwaukee, WI and cover almost 2 years of precipitation events. These data have irregular sampling periods and missing data that preclude the straightforward application of the fast Fourier transform (FFT). In both cases, an anthropogenic periodicity is identified; a 7-day weekday/ weekend effect in the Washington elemental carbon series and a 1 month cycle in several constituents of stormwater. Practical aspects of application of the Lomb periodogram are discussed, particularly quantifying the effects of random noise. The proper application of the FFT to data that are irregularly spaced with missing values is demonstrated on the air quality data. Recommendations are given when to use the Lomb periodogram and when to use the FFT. PMID:17993144
Water Resources Management Plan for Ganga River using SWAT Modelling and Time series Analysis
NASA Astrophysics Data System (ADS)
Satish, L. N. V.
2015-12-01
Water resources management of the Ganga River is one of the primary objectives of National Ganga River Basin Environmental Management Plan. The present study aims to carry out water balance study and development of appropriate methodologies to compute environmental flow in the middle Ganga river basin between Patna-Farraka, India. The methodology adopted here are set-up a hydrological model to estimate monthly discharge at the tributaries under natural condition, hydrological alternation analysis of both observed and simulated discharge series, flow health analysis to obtain status of the stream health in the last 4 decades and estimating the e-flow using flow health indicators. ArcSWAT, was used to simulate 8 tributaries namely Kosi, Gandak and others. This modelling is quite encouraging and helps to provide the monthly water balance analysis for all tributaries for this study. The water balance analysis indicates significant change in surface and ground water interaction pattern within the study time period Indicators of hydrological alternation has been used for both observed and simulated data series to quantify hydrological alternation occurred in the tributaries and the main river in the last 4 decades,. For temporal variation of stream health, flow health tool has been used for observed and simulated discharge data. A detailed stream health analysis has been performed by considering 3 approaches based on i) observed flow time series, ii) observed and simulated flow time series and iii) simulated flow time series at small upland basin, major tributary and main Ganga river basin levels. At upland basin level, these approaches show that stream health and its temporal variations are good with non-significant temporal variation. At major tributary level, the stream health and its temporal variations are found to be deteriorating from 1970s. At the main Ganga reach level river health and its temporal variations does not show any declining trend. Finally, E- flows
Satellite time series analysis to study the ephemeral nature of archaeological marks
NASA Astrophysics Data System (ADS)
Stewart, Chris
2014-05-01
Archaeological structures buried beneath the ground often leave traces at the surface. These traces can be in the form of differences in soil moisture and composition, or vegetation growth caused for example by increased soil water retention over a buried ditch, or by insufficient soil depth over a buried wall for vegetation to place deep roots. Buried structures also often leave subtle topographic traces at the surface. Analyses is carried out on the ephemeral characteristics of buried archaeological crop and soil marks over a number of sites around the city of Rome using satellite data from both optical and SAR (Synthetic Aperture Radar) sensors, including Kompsat-2, ALOS PRISM and COSMO SkyMed. The sensitivity of topographic satellite data, obtained by optical photogrammetry and interferometric SAR, is also analysed over the same sites, as well as other sites in Egypt. The analysis includes a study of the interferometric coherence of successive pairs of a time series of SAR data over sites containing buried structuresto better understand the nature of the vegetated or bare soil surface. To understand the ephemeral nature of archaeological crop and soil marks, the spectral reflectance characteristics of areas where such marks sometimes appear are extracted from a time series of optical multispectral and panchromatic imagery, and their backscatter characteristics extracted from a time series of SAR backscatter amplitude data. The results of this analysis is then compared with the results of the coherence analysis to see if any link can be established between the appearance of archaeological structures and the nature of ground cover. Results show that archaeological marks in the study areas are more present in SAR backscatter data over vegetated surfaces, rather than bare soil surfaces, but sometimes appear also in bare soil conditions. In the study areas, crop marks appear more distinctly in optical data after long periods without rainfall. The topographic
Data Reorganization for Optimal Time Series Data Access, Analysis, and Visualization
NASA Astrophysics Data System (ADS)
Rui, H.; Teng, W. L.; Strub, R.; Vollmer, B.
2012-12-01
The way data are archived is often not optimal for their access by many user communities (e.g., hydrological), particularly if the data volumes and/or number of data files are large. The number of data records of a non-static data set generally increases with time. Therefore, most data sets are commonly archived by time steps, one step per file, often containing multiple variables. However, many research and application efforts need time series data for a given geographical location or area, i.e., a data organization that is orthogonal to the way the data are archived. The retrieval of a time series of the entire temporal coverage of a data set for a single variable at a single data point, in an optimal way, is an important and longstanding challenge, especially for large science data sets (i.e., with volumes greater than 100 GB). Two examples of such large data sets are the North American Land Data Assimilation System (NLDAS) and Global Land Data Assimilation System (GLDAS), archived at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC; Hydrology Data Holdings Portal, http://disc.sci.gsfc.nasa.gov/hydrology/data-holdings). To date, the NLDAS data set, hourly 0.125x0.125° from Jan. 1, 1979 to present, has a total volume greater than 3 TB (compressed). The GLDAS data set, 3-hourly and monthly 0.25x0.25° and 1.0x1.0° Jan. 1948 to present, has a total volume greater than 1 TB (compressed). Both data sets are accessible, in the archived time step format, via several convenient methods, including Mirador search and download (http://mirador.gsfc.nasa.gov/), GrADS Data Server (GDS; http://hydro1.sci.gsfc.nasa.gov/dods/), direct FTP (ftp://hydro1.sci.gsfc.nasa.gov/data/s4pa/), and Giovanni Online Visualization and Analysis (http://disc.sci.gsfc.nasa.gov/giovanni). However, users who need long time series currently have no efficient way to retrieve them. Continuing a longstanding tradition of facilitating data access, analysis, and
Amigo, H; Díaz, L; Pino, P; Vera, G
1994-06-01
The objective of this study was to determine the evolution of the nutritional status of the population under five years of age during the period 1975-1990. Several conditioning factors were also assessed. The information was evaluated through time series analysis by using the AREG procedure. This procedure allows for the estimation of a regression model correcting by the autocorrelation of errors. Results indicates a significant trend to decreased undernutrition rates (p < 0.0001). A seasonal effect on undernutrition was observed, being higher the prevalences in summer. Analysis of selected conditioning factors, as well as the familiar buying capacity remained stable during the period. An exception to the lack of association among undernutrition and the conditioning factors evaluated, was seen during the period 1975-1982 when clear inverse relationship was evidenced. In conclusion, the decrease of infant undernutrition in Chile during the period 1975-1990 was not related to the changes observed in certain socioeconomic indices. PMID:7733798
NASA Technical Reports Server (NTRS)
Hailperin, Max
1993-01-01
This thesis provides design and analysis of techniques for global load balancing on ensemble architectures running soft-real-time object-oriented applications with statistically periodic loads. It focuses on estimating the instantaneous average load over all the processing elements. The major contribution is the use of explicit stochastic process models for both the loading and the averaging itself. These models are exploited via statistical time-series analysis and Bayesian inference to provide improved average load estimates, and thus to facilitate global load balancing. This thesis explains the distributed algorithms used and provides some optimality results. It also describes the algorithms' implementation and gives performance results from simulation. These results show that our techniques allow more accurate estimation of the global system load ing, resulting in fewer object migration than local methods. Our method is shown to provide superior performance, relative not only to static load-balancing schemes but also to many adaptive methods.
Investigation on Law and Economics Based on Complex Network and Time Series Analysis
Yang, Jian; Qu, Zhao; Chang, Hui
2015-01-01
The research focuses on the cooperative relationship and the strategy tendency among three mutually interactive parties in financing: small enterprises, commercial banks and micro-credit companies. Complex network theory and time series analysis were applied to figure out the quantitative evidence. Moreover, this paper built up a fundamental model describing the particular interaction among them through evolutionary game. Combining the results of data analysis and current situation, it is justifiable to put forward reasonable legislative recommendations for regulations on lending activities among small enterprises, commercial banks and micro-credit companies. The approach in this research provides a framework for constructing mathematical models and applying econometrics and evolutionary game in the issue of corporation financing. PMID:26076460
NASA Technical Reports Server (NTRS)
Agarwal, G. C.; Osafo-Charles, F.; Oneill, W. D.; Gottlieb, G. L.
1982-01-01
Time series analysis is applied to model human operator dynamics in pursuit and compensatory tracking modes. The normalized residual criterion is used as a one-step analytical tool to encompass the processes of identification, estimation, and diagnostic checking. A parameter constraining technique is introduced to develop more reliable models of human operator dynamics. The human operator is adequately modeled by a second order dynamic system both in pursuit and compensatory tracking modes. In comparing the data sampling rates, 100 msec between samples is adequate and is shown to provide better results than 200 msec sampling. The residual power spectrum and eigenvalue analysis show that the human operator is not a generator of periodic characteristics.
Use of a prototype pulse oximeter for time series analysis of heart rate variability
NASA Astrophysics Data System (ADS)
González, Erika; López, Jehú; Hautefeuille, Mathieu; Velázquez, Víctor; Del Moral, Jésica
2015-05-01
This work presents the development of a low cost pulse oximeter prototype consisting of pulsed red and infrared commercial LEDs and a broad spectral photodetector used to register time series of heart rate and oxygen saturation of blood. This platform, besides providing these values, like any other pulse oximeter, processes the signals to compute a power spectrum analysis of the patient heart rate variability in real time and, additionally, the device allows access to all raw and analyzed data if databases construction is required or another kind of further analysis is desired. Since the prototype is capable of acquiring data for long periods of time, it is suitable for collecting data in real life activities, enabling the development of future wearable applications.
3D time series analysis of cell shape using Laplacian approaches
2013-01-01
Background Fundamental cellular processes such as cell movement, division or food uptake critically depend on cells being able to change shape. Fast acquisition of three-dimensional image time series has now become possible, but we lack efficient tools for analysing shape deformations in order to understand the real three-dimensional nature of shape changes. Results We present a framework for 3D+time cell shape analysis. The main contribution is three-fold: First, we develop a fast, automatic random walker method for cell segmentation. Second, a novel topology fixing method is proposed to fix segmented binary volumes without spherical topology. Third, we show that algorithms used for each individual step of the analysis pipeline (cell segmentation, topology fixing, spherical parameterization, and shape representation) are closely related to the Laplacian operator. The framework is applied to the shape analysis of neutrophil cells. Conclusions The method we propose for cell segmentation is faster than the traditional random walker method or the level set method, and performs better on 3D time-series of neutrophil cells, which are comparatively noisy as stacks have to be acquired fast enough to account for cell motion. Our method for topology fixing outperforms the tools provided by SPHARM-MAT and SPHARM-PDM in terms of their successful fixing rates. The different tasks in the presented pipeline for 3D+time shape analysis of cells can be solved using Laplacian approaches, opening the possibility of eventually combining individual steps in order to speed up computations. PMID:24090312
Quantification of evolution from order to randomness in practical time series analysis.
Pincus, S M
1994-01-01
The principal focus of this chapter is the description of a recently developed, readily usable regularity statistic, ApEn, that quantifies the continuum from perfectly orderly to completely random in time series data. Several properties of ApEn facilitate its utility for practical time series analysis: (1) ApEn is nearly unaffected by noise of magnitude below a de facto specified filter level; (2) ApEn is robust to outliers; (3) ApEn can be applied to time series of 100 or more points, with good confidence (established by standard deviation calculations); (4) ApEn is finite for stochastic, noisy deterministic, and composite (mixed) processes, the last of which are likely models for complicated biological systems; (5) increasing ApEn corresponds to intuitively increasing process complexity in the settings of (4). This applicability to medium-sized data sets and general stochastic processes is in marked contrast to capabilities of "chaos" algorithms such as the correlation dimension, which are properly applied to low-dimensional iterated deterministic dynamical systems. The potential uses of ApEn to provide new insights in biological settings are thus myriad, from a perspective complementary to that given by classic statistical methods. The ApEn statistic is typically calculated by a computer program, with a FORTRAN listing for a "basic" code referenced above. It is imperative to view ApEn as a family of statistics, each of which is a relative measure of process regularity. For proper implementation, the two input parameters m (window length) and r (tolerance width, de facto filter) must remain fixed in all calculations, as must N, the data length, to ensure meaningful comparisons. Guidelines for m and r selection are indicated above. We have found normalized regularity to be especially useful; "r" is chosen as a fixed percentage (often 15 or 20%) of the SD of the subject rather than of a group SD. This version of ApEn has the property that it is decorrelated from
Pitfalls in Fractal Time Series Analysis: fMRI BOLD as an Exemplary Case
Eke, Andras; Herman, Peter; Sanganahalli, Basavaraju G.; Hyder, Fahmeed; Mukli, Peter; Nagy, Zoltan
2012-01-01
This article will be positioned on our previous work demonstrating the importance of adhering to a carefully selected set of criteria when choosing the suitable method from those available ensuring its adequate performance when applied to real temporal signals, such as fMRI BOLD, to evaluate one important facet of their behavior, fractality. Earlier, we have reviewed on a range of monofractal tools and evaluated their performance. Given the advance in the fractal field, in this article we will discuss the most widely used implementations of multifractal analyses, too. Our recommended flowchart for the fractal characterization of spontaneous, low frequency fluctuations in fMRI BOLD will be used as the framework for this article to make certain that it will provide a hands-on experience for the reader in handling the perplexed issues of fractal analysis. The reason why this particular signal modality and its fractal analysis has been chosen was due to its high impact on today’s neuroscience given it had powerfully emerged as a new way of interpreting the complex functioning of the brain (see “intrinsic activity”). The reader will first be presented with the basic concepts of mono and multifractal time series analyses, followed by some of the most relevant implementations, characterization by numerical approaches. The notion of the dichotomy of fractional Gaussian noise and fractional Brownian motion signal classes and their impact on fractal time series analyses will be thoroughly discussed as the central theme of our application strategy. Sources of pitfalls and way how to avoid them will be identified followed by a demonstration on fractal studies of fMRI BOLD taken from the literature and that of our own in an attempt to consolidate the best practice in fractal analysis of empirical fMRI BOLD signals mapped throughout the brain as an exemplary case of potentially wide interest. PMID:23227008
Seasonal and annual precipitation time series trend analysis in North Carolina, United States
NASA Astrophysics Data System (ADS)
Sayemuzzaman, Mohammad; Jha, Manoj K.
2014-02-01
The present study performs the spatial and temporal trend analysis of the annual and seasonal time-series of a set of uniformly distributed 249 stations precipitation data across the state of North Carolina, United States over the period of 1950-2009. The Mann-Kendall (MK) test, the Theil-Sen approach (TSA) and the Sequential Mann-Kendall (SQMK) test were applied to quantify the significance of trend, magnitude of trend, and the trend shift, respectively. Regional (mountain, piedmont and coastal) precipitation trends were also analyzed using the above-mentioned tests. Prior to the application of statistical tests, the pre-whitening technique was used to eliminate the effect of autocorrelation of precipitation data series. The application of the above-mentioned procedures has shown very notable statewide increasing trend for winter and decreasing trend for fall precipitation. Statewide mixed (increasing/decreasing) trend has been detected in annual, spring, and summer precipitation time series. Significant trends (confidence level ≥ 95%) were detected only in 8, 7, 4 and 10 nos. of stations (out of 249 stations) in winter, spring, summer, and fall, respectively. Magnitude of the highest increasing (decreasing) precipitation trend was found about 4 mm/season (- 4.50 mm/season) in fall (summer) season. Annual precipitation trend magnitude varied between - 5.50 mm/year and 9 mm/year. Regional trend analysis found increasing precipitation in mountain and coastal regions in general except during the winter. Piedmont region was found to have increasing trends in summer and fall, but decreasing trend in winter, spring and on an annual basis. The SQMK test on "trend shift analysis" identified a significant shift during 1960 - 70 in most parts of the state. Finally, the comparison between winter (summer) precipitations with the North Atlantic Oscillation (Southern Oscillation) indices concluded that the variability and trend of precipitation can be explained by the
NASA Astrophysics Data System (ADS)
Loredo, Thomas
The key, central objectives of the proposed Time Series Explorer project are to develop an organized collection of software tools for analysis of time series data in current and future NASA astrophysics data archives, and to make the tools available in two ways: as a library (the Time Series Toolbox) that individual science users can use to write their own data analysis pipelines, and as an application (the Time Series Automaton) providing an accessible, data-ready interface to many Toolbox algorithms, facilitating rapid exploration and automatic processing of time series databases. A number of time series analysis methods will be implemented, including techniques that range from standard ones to state-of-the-art developments by the proposers and others. Most of the algorithms will be able to handle time series data subject to real-world problems such as data gaps, sampling that is otherwise irregular, asynchronous sampling (in multi-wavelength settings), and data with non-Gaussian measurement errors. The proposed research responds to the ADAP element supporting the development of tools for mining the vast reservoir of information residing in NASA databases. The tools that will be provided to the community of astronomers studying variability of astronomical objects (from nearby stars and extrasolar planets, through galactic and extragalactic sources) will revolutionize the quality of timing analyses that can be carried out, and greatly enhance the scientific throughput of all NASA astrophysics missions past, present, and future. The Automaton will let scientists explore time series - individual records or large data bases -- with the most informative and useful analysis methods available, without having to develop the tools themselves or understand the computational details. Both elements, the Toolbox and the Automaton, will enable deep but efficient exploratory time series data analysis, which is why we have named the project the Time Series Explorer. Science
Local Rainfall Forecast System based on Time Series Analysis and Neural Networks
NASA Astrophysics Data System (ADS)
Buendia-Buendía, F. S.; López Carrión, F.; Tarquis, A. M.; Buendía Moya, G.; Andina, D.
2010-05-01
Rainfall is one of the most important events in daily life of human beings. During several decades, scientists have been trying to characterize the weather, current forecasts are based on high complex dynamic models. In this paper is presented a local rainfall forecast system based on Time Series analysis and Neural Networks. This model tries to complement the currently state of the art ensembles, from a locally historical perspective, where the model definition is not so dependent from the exact values of the initial conditions. After several years taking data, expert meteorologists proposed this approximation to characterize the local weather behaviour, that is automated by this system. The current system predicts rainfall events over Valladolid within a time period of a month with a twelve hours accuracy. The different blocks of the system is explained as well as the work introduces how to apply the forecast system to prevent economical impact hazards produced by rainfalls.
Experimental nonlinear dynamical studies in cesium magneto-optical trap using time-series analysis
NASA Astrophysics Data System (ADS)
Anwar, M.; Islam, R.; Faisal, M.; Sikandar, M.; Ahmed, M.
2015-03-01
A magneto-optical trap of neutral atoms is essentially a dissipative quantum system. The fast thermal atoms continuously dissipate their energy to the environment via spontaneous emissions during the cooling. The atoms are, therefore, strongly coupled with the vacuum reservoir and the laser field. The vacuum fluctuations as well as the field fluctuations are imparted to the atoms as random photon recoils. Consequently, the external and internal dynamics of atoms becomes stochastic. In this paper, we have investigated the stochastic dynamics of the atoms in a magneto-optical trap during the loading process. The time series analysis of the fluorescence signal shows that the dynamics of the atoms evolves, like all dissipative systems, from deterministic to the chaotic regime. The subsequent disappearance and revival of chaos was attributed to chaos synchronization between spatially different atoms in the magneto-optical trap.
NASA Astrophysics Data System (ADS)
Schaffner, D. A.; Brown, M. R.; Rock, A. B.
2016-05-01
The frequency spectrum of magnetic fluctuations as measured on the Swarthmore Spheromak Experiment is broadband and exhibits a nearly Kolmogorov 5/3 scaling. It features a steepening region which is indicative of dissipation of magnetic fluctuation energy similar to that observed in fluid and magnetohydrodynamic turbulence systems. Two non-spectrum based time-series analysis techniques are implemented on this data set in order to seek other possible signatures of turbulent dissipation beyond just the steepening of fluctuation spectra. Presented here are results for the flatness, permutation entropy, and statistical complexity, each of which exhibits a particular character at spectral steepening scales which can then be compared to the behavior of the frequency spectrum.
Studies in astronomical time series analysis: Modeling random processes in the time domain
NASA Technical Reports Server (NTRS)
Scargle, J. D.
1979-01-01
Random process models phased in the time domain are used to analyze astrophysical time series data produced by random processes. A moving average (MA) model represents the data as a sequence of pulses occurring randomly in time, with random amplitudes. An autoregressive (AR) model represents the correlations in the process in terms of a linear function of past values. The best AR model is determined from sampled data and transformed to an MA for interpretation. The randomness of the pulse amplitudes is maximized by a FORTRAN algorithm which is relatively stable numerically. Results of test cases are given to study the effects of adding noise and of different distributions for the pulse amplitudes. A preliminary analysis of the optical light curve of the quasar 3C 273 is given.
Experimental nonlinear dynamical studies in cesium magneto-optical trap using time-series analysis
Anwar, M. Islam, R.; Faisal, M.; Sikandar, M.; Ahmed, M.
2015-03-30
A magneto-optical trap of neutral atoms is essentially a dissipative quantum system. The fast thermal atoms continuously dissipate their energy to the environment via spontaneous emissions during the cooling. The atoms are, therefore, strongly coupled with the vacuum reservoir and the laser field. The vacuum fluctuations as well as the field fluctuations are imparted to the atoms as random photon recoils. Consequently, the external and internal dynamics of atoms becomes stochastic. In this paper, we have investigated the stochastic dynamics of the atoms in a magneto-optical trap during the loading process. The time series analysis of the fluorescence signal shows that the dynamics of the atoms evolves, like all dissipative systems, from deterministic to the chaotic regime. The subsequent disappearance and revival of chaos was attributed to chaos synchronization between spatially different atoms in the magneto-optical trap.
Tidal frequencies in the spectral analysis of time series muon flux measurements
NASA Astrophysics Data System (ADS)
Feldman, Catherine; Takai, Helio
2016-03-01
Tidal frequencies are observed in the spectral analysis of time series muon flux measurements performed by the MARIACHI experiment over a period of seven years. The prominent peaks from the frequency spectrum correspond to tidal frequencies S1,S2,S3,K1,P1 and Ψ1 . We will present these results and compare them to the regular density oscillations from balloon sounding data. We interpret the observed data as being the effect of regular atmospheric density oscillations induced by the thermal heating of layers in Earth's atmosphere. As the density of the atmosphere varies, the altitude where particles are produced varies accordingly. As a consequence, the muon decay path elongates or contracts, modulating the number of muons detected at ground level. The role of other tidal effects, including geomagnetic tides, will also be discussed.
Nonlinear Analysis on Cross-Correlation of Financial Time Series by Continuum Percolation System
NASA Astrophysics Data System (ADS)
Niu, Hongli; Wang, Jun
We establish a financial price process by continuum percolation system, in which we attribute price fluctuations to the investors’ attitudes towards the financial market, and consider the clusters in continuum percolation as the investors share the same investment opinion. We investigate the cross-correlations in two return time series, and analyze the multifractal behaviors in this relationship. Further, we study the corresponding behaviors for the real stock indexes of SSE and HSI as well as the liquid stocks pair of SPD and PAB by comparison. To quantify the multifractality in cross-correlation relationship, we employ multifractal detrended cross-correlation analysis method to perform an empirical research for the simulation data and the real markets data.
The TAOS Project: Statistical Analysis of Multi-Telescope Time Series Data
NASA Astrophysics Data System (ADS)
Lehner, M. J.; Coehlo, N. K.; Zhang, Z.-W.; Bianco, F. B.; Wang, J.-H.; Rice, J. A.; Protopapas, P.; Alcock, C.; Axelrod, T.; Byun, Y.-I.; Chen, W. P.; Cook, K. H.; de Pater, I.; Kim, D.-W.; King, S.-K.; Lee, T.; Marshall, S. L.; Schwamb, M. E.; Wang, S.-Y.; Wen, C.-Y.
2010-08-01
The Taiwanese-American Occultation Survey (TAOS) monitors fields of up to ~1000 stars at 5 Hz simultaneously with four small telescopes to detect occultation events from small (~1 km) Kuiper Belt Objects (KBOs). The survey presents a number of challenges, in particular the fact that the occultation events we are searching for are extremely rare and are typically manifested as slight flux drops for only one or two consecutive time series measurements. We have developed a statistical analysis technique to search the multi-telescope data set for simultaneous flux drops which provides a robust false-positive rejection and calculation of event significance. In this article, we describe in detail this statistical technique and its application to the TAOS data set.
NASA Astrophysics Data System (ADS)
de Lautour, Oliver R.; Omenzetter, Piotr
2010-07-01
Developed for studying long sequences of regularly sampled data, time series analysis methods are being increasingly investigated for the use of Structural Health Monitoring (SHM). In this research, Autoregressive (AR) models were used to fit the acceleration time histories obtained from two experimental structures: a 3-storey bookshelf structure and the ASCE Phase II Experimental SHM Benchmark Structure, in undamaged and limited number of damaged states. The coefficients of the AR models were considered to be damage-sensitive features and used as input into an Artificial Neural Network (ANN). The ANN was trained to classify damage cases or estimate remaining structural stiffness. The results showed that the combination of AR models and ANNs are efficient tools for damage classification and estimation, and perform well using small number of damage-sensitive features and limited sensors.
Event-sequence time series analysis in ground-based gamma-ray astronomy
Barres de Almeida, U.; Chadwick, P.; Daniel, M.; Nolan, S.; McComb, L.
2008-12-24
The recent, extreme episodes of variability detected from Blazars by the leading atmospheric Cerenkov experiments motivate the development and application of specialized statistical techniques that enable the study of this rich data set to its furthest extent. The identification of the shortest variability timescales supported by the data and the actual variability structure observed in the light curves of these sources are some of the fundamental aspects being studied, that answers can bring new developments on the understanding of the physics of these objects and on the mechanisms of production of VHE gamma-rays in the Universe. Some of our efforts in studying the time variability of VHE sources involve the application of dynamic programming algorithms to the problem of detecting change-points in a Poisson sequence. In this particular paper we concentrate on the more primary issue of the applicability of counting statistics to the analysis of time-series on VHE gamma-ray astronomy.
Asymmetric multifractal detrending moving average analysis in time series of PM2.5 concentration
NASA Astrophysics Data System (ADS)
Zhang, Chen; Ni, Zhiwei; Ni, Liping; Li, Jingming; Zhou, Longfei
2016-09-01
In this paper, we propose the asymmetric multifractal detrending moving average analysis (A-MFDMA) method to explore the asymmetric correlation in non-stationary time series. The proposed method is applied to explore the asymmetric correlation of PM2.5 daily average concentration with uptrends or downtrends in China. In addition, shuffling and phase randomization procedures are applied to detect the sources of multifractality. The results show that existences of asymmetric correlations, and the asymmetric correlations are multifractal. Further, the multifractal scaling behavior in the Chinese PM2.5 is caused not only by long-range correlation but also by fat-tailed distribution, but the major source of multifractality is fat-tailed distribution.
The impact of policy on hospital productivity: a time series analysis of Dutch hospitals.
Blank, Jos L T; Eggink, Evelien
2014-06-01
The health care industry, in particular the hospital industry, is under an increasing degree of pressure, by an ageing population, advancing expensive medical technology a shrinking labor. The pressure on hospitals is further increased by the planned budget cuts in public spending by many current administrations as a result of the economic and financial crises. However, productivity increases may alleviate these problems. Therefore we study whether productivity in the hospital sector is growing, and whether this productivity growth can be influenced by government policy. Using an econometric time series analysis of the hospital sector in the Netherlands, productivity is estimated for the period 1972-2010. Then, productivity is linked to the different regulation regimes during that period, ranging from output funding in the 1970s to the current liberalized hospital market. The results indicate that the average productivity of the hospital sector in different periods differs and that these differences are related to the structure of regulation in those periods. PMID:24258183
Assimilating Cloud Initiation based on Time Series Analysis into flash flood prediction model
NASA Astrophysics Data System (ADS)
Shiff, Shilo; Lensky, Itamar
2015-04-01
We used Temporal Fourier Analysis on time series (2010-2013) of Meteosat Second Generation (MSG) European geostationary weather satellite to generate cloud free climatological values of channel 1 (0.6um) reflectance and channel 9 (10.8um) brightness temperatures (BT) on pixel basis. Discrepancy between measured reflectance and/or BT and their climatological values are used to detect "cloud contaminated" pixels. This algorithm is very sensitive to sub-pixel clouds that are visible only in the High Resolution Visible channel, but not in the spectral channels. This method is valuable for early detection of convection. We used this cloud initiation method within high-resolution numerical weather forecasts to improve its accuracy in terms of early warning on the location and timing of potential flash floods.
[Local fractal analysis of noise-like time series by all permutations method for 1-115 min periods].
Panchelyuga, V A; Panchelyuga, M S
2015-01-01
Results of local fractal analysis of 329-per-day time series of 239Pu alpha-decay rate fluctuations by means of all permutations method (APM) are presented. The APM-analysis reveals in the time series some steady frequency set. The coincidence of the frequency set with the Earth natural oscillations was demonstrated. A short review of works by different authors who analyzed the time series of fluctuations in processes of different nature is given. We have shown that the periods observed in those works correspond to the periods revealed in our study. It points to a common mechanism of the phenomenon observed. PMID:26016038
Time series analysis of influenza incidence in Chinese provinces from 2004 to 2011.
Song, Xin; Xiao, Jun; Deng, Jiang; Kang, Qiong; Zhang, Yanyu; Xu, Jinbo
2016-06-01
Influenza as a severe infectious disease has caused catastrophes throughout human history, and every pandemic of influenza has produced a great social burden. We compiled monthly data of influenza incidence from all provinces and autonomous regions in mainland China from January 2004 to December 2011, comprehensively evaluated and classified these data, and then randomly selected 4 provinces with higher incidence (Hebei, Gansu, Guizhou, and Hunan), 2 provinces with median incidence (Tianjin and Henan), 1 province with lower incidence (Shandong), using time series analysis to construct an ARIMA model, which is based on the monthly incidence from 2004 to 2011 as the training set. We exerted the X-12-ARIMA procedure for modeling due to the seasonality these data implied. Autocorrelation function (ACF), partial autocorrelation function (PACF), and automatic model selection were to determine the order of the model parameters. The optimal model was decided by a nonseasonal and seasonal moving average test. Finally, we applied this model to predict the monthly incidence of influenza in 2012 as the test set, and the simulated incidence was compared with the observed incidence to evaluate the model's validity by the criterion of both percentage variability in regression analyses (R) and root mean square error (RMSE). It is conceivable that SARIMA (0,1,1)(0,1,1)12 could simultaneously forecast the influenza incidence of the Hebei Province, Guizhou Province, Henan Province, and Shandong Province; SARIMA (1,0,0)(0,1,1)12 could forecast the influenza incidence in Gansu Province; SARIMA (3,1,1)(0,1,1)12 could forecast the influenza incidence in Tianjin City; and SARIMA (0,1,1)(0,0,1)12 could forecast the influenza incidence in Hunan Province. Time series analysis is a good tool for prediction of disease incidence. PMID:27367989
Time series analysis of influenza incidence in Chinese provinces from 2004 to 2011
Song, Xin; Xiao, Jun; Deng, Jiang; Kang, Qiong; Zhang, Yanyu; Xu, Jinbo
2016-01-01
Abstract Influenza as a severe infectious disease has caused catastrophes throughout human history, and every pandemic of influenza has produced a great social burden. We compiled monthly data of influenza incidence from all provinces and autonomous regions in mainland China from January 2004 to December 2011, comprehensively evaluated and classified these data, and then randomly selected 4 provinces with higher incidence (Hebei, Gansu, Guizhou, and Hunan), 2 provinces with median incidence (Tianjin and Henan), 1 province with lower incidence (Shandong), using time series analysis to construct an ARIMA model, which is based on the monthly incidence from 2004 to 2011 as the training set. We exerted the X-12-ARIMA procedure for modeling due to the seasonality these data implied. Autocorrelation function (ACF), partial autocorrelation function (PACF), and automatic model selection were to determine the order of the model parameters. The optimal model was decided by a nonseasonal and seasonal moving average test. Finally, we applied this model to predict the monthly incidence of influenza in 2012 as the test set, and the simulated incidence was compared with the observed incidence to evaluate the model's validity by the criterion of both percentage variability in regression analyses (R2) and root mean square error (RMSE). It is conceivable that SARIMA (0,1,1)(0,1,1)12 could simultaneously forecast the influenza incidence of the Hebei Province, Guizhou Province, Henan Province, and Shandong Province; SARIMA (1,0,0)(0,1,1)12 could forecast the influenza incidence in Gansu Province; SARIMA (3,1,1)(0,1,1)12 could forecast the influenza incidence in Tianjin City; and SARIMA (0,1,1)(0,0,1)12 could forecast the influenza incidence in Hunan Province. Time series analysis is a good tool for prediction of disease incidence. PMID:27367989
Krafty, Robert T; Hall, Martica
2013-03-01
Although many studies collect biomedical time series signals from multiple subjects, there is a dearth of models and methods for assessing the association between frequency domain properties of time series and other study outcomes. This article introduces the random Cramér representation as a joint model for collections of time series and static outcomes where power spectra are random functions that are correlated with the outcomes. A canonical correlation analysis between cepstral coefficients and static outcomes is developed to provide a flexible yet interpretable measure of association. Estimates of the canonical correlations and weight functions are obtained from a canonical correlation analysis between the static outcomes and maximum Whittle likelihood estimates of truncated cepstral coefficients. The proposed methodology is used to analyze the association between the spectrum of heart rate variability and measures of sleep duration and fragmentation in a study of older adults who serve as the primary caregiver for their ill spouse. PMID:24851143
The Bird's Ear View: Audification for the Spectral Analysis of Heliospheric Time Series Data
NASA Astrophysics Data System (ADS)
Alexander, Robert L.
The sciences are inundated with a tremendous volume of data, and the analysis of rapidly expanding data archives presents a persistent challenge. Previous research in the field of data sonification suggests that auditory display may serve a valuable function in the analysis of complex data sets. This dissertation uses the heliospheric sciences as a case study to empirically evaluate the use of audification (a specific form of sonification) for the spectral analysis of large time series. Three primary research questions guide this investigation, the first of which addresses the comparative capabilities of auditory and visual analysis methods in applied analysis tasks. A number of controlled within-subject studies revealed a strong correlation between auditory and visual observations, and demonstrated that auditory analysis provided a heightened sensitivity and accuracy in the detection of spectral features. The second research question addresses the capability of audification methods to reveal features that may be overlooked through visual analysis of spectrograms. A number of open-ended analysis tasks quantitatively demonstrated that participants using audification regularly discovered a greater percentage of embedded phenomena such as low-frequency wave storms. In addition, four case studies document collaborative research initiatives in which audification contributed to the acquisition of new domain-specific knowledge. The final question explores the potential benefits of audification when introduced into the workflow of a research scientist. A case study is presented in which a heliophysicist incorporated audification into their working practice, and the "Think-Aloud" protocol is applied to gain a sense for how audification augmented the researcher's analytical abilities. Auditory observations are demonstrated to make significant contributions to ongoing research, including the detection of previously unidentified equipment-induced artifacts. This dissertation
NASA Astrophysics Data System (ADS)
Gualandi, Adriano; Serpelloni, Enrico; Elina Belardinelli, Maria; Bonafede, Maurizio; Pezzo, Giuseppe; Tolomei, Cristiano
2015-04-01
A critical point in the analysis of ground displacement time series, as those measured by modern space geodetic techniques (primarly continuous GPS/GNSS and InSAR) is the development of data driven methods that allow to discern and characterize the different sources that generate the observed displacements. A widely used multivariate statistical technique is the Principal Component Analysis (PCA), which allows to reduce the dimensionality of the data space maintaining most of the variance of the dataset explained. It reproduces the original data using a limited number of Principal Components, but it also shows some deficiencies, since PCA does not perform well in finding the solution to the so-called Blind Source Separation (BSS) problem. The recovering and separation of the different sources that generate the observed ground deformation is a fundamental task in order to provide a physical meaning to the possible different sources. PCA fails in the BSS problem since it looks for a new Euclidean space where the projected data are uncorrelated. Usually, the uncorrelation condition is not strong enough and it has been proven that the BSS problem can be tackled imposing on the components to be independent. The Independent Component Analysis (ICA) is, in fact, another popular technique adopted to approach this problem, and it can be used in all those fields where PCA is also applied. An ICA approach enables us to explain the displacement time series imposing a fewer number of constraints on the model, and to reveal anomalies in the data such as transient deformation signals. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. To work around this problem, we use a variational bayesian ICA (vbICA) method, which models the probability density function (pdf) of each source signal using a mix of Gaussian distributions. This technique allows for more flexibility in the description of the pdf of the sources
Time series analysis of the developed financial markets' integration using visibility graphs
NASA Astrophysics Data System (ADS)
Zhuang, Enyu; Small, Michael; Feng, Gang
2014-09-01
A time series representing the developed financial markets' segmentation from 1973 to 2012 is studied. The time series reveals an obvious market integration trend. To further uncover the features of this time series, we divide it into seven windows and generate seven visibility graphs. The measuring capabilities of the visibility graphs provide means to quantitatively analyze the original time series. It is found that the important historical incidents that influenced market integration coincide with variations in the measured graphical node degree. Through the measure of neighborhood span, the frequencies of the historical incidents are disclosed. Moreover, it is also found that large "cycles" and significant noise in the time series are linked to large and small communities in the generated visibility graphs. For large cycles, how historical incidents significantly affected market integration is distinguished by density and compactness of the corresponding communities.
Use of interrupted time series analysis in evaluating health care quality improvements.
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
Visibility Modeling and Forecasting for Abu Dhabi using Time Series Analysis Method
NASA Astrophysics Data System (ADS)
Eibedingil, I. G.; Abula, B.; Afshari, A.; Temimi, M.
2015-12-01
Land-Atmosphere interactions-their strength, directionality and evolution-are one of the main sources of uncertainty in contemporary climate modeling. A particularly crucial role in sustaining and modulating land-atmosphere interaction is the one of aerosols and dusts. Aerosols are tiny particles suspended in the air ranging from a few nanometers to a few hundred micrometers in diameter. Furthermore, the amount of dust and fog in the atmosphere is an important measure of visibility, which is another dimension of land-atmosphere interactions. Visibility affects all form of traffic, aviation, land and sailing. Being able to predict the change of visibility in the air in advance enables relevant authorities to take necessary actions before the disaster falls. Time Series Analysis (TAS) method is an emerging technique for modeling and forecasting the behavior of land-atmosphere interactions, including visibility. This research assess the dynamics and evolution of visibility around Abu Dhabi International Airport (+24.4320 latitude, +54.6510 longitude, and 27m elevation) using mean daily visibility and mean daily wind speed. TAS has been first used to model and forecast the visibility, and then the Transfer Function Model has been applied, considering the wind speed as an exogenous variable. By considering the Akaike Information Criterion (AIC) and Mean Absolute Percentage Error (MAPE) as a statistical criteria, two forecasting models namely univarite time series model and transfer function model, were developed to forecast the visibility around Abu Dhabi International Airport for three weeks. Transfer function model improved the MAPE of the forecast significantly.
Time series in analysis of yerba-mate biennial growth modified by environment
NASA Astrophysics Data System (ADS)
Rakocevic, Miroslava; Martim, Simoni Fernanda
2011-03-01
To assess differences in the lag-effect pattern in the relationship between yerba-mate biennial growth and environmental factors, a time-series analysis was performed. A generalized Poisson regression model was used to control time trends, temperature, growing degree days (GDD), rainfalls and night length (NL). It was hypothesized that the active growth and growth pauses in yerba-mate are controlled endogenously and modified by environment, and that genders would respond differently to environmental modifications. The patterns in the lag effect from the distributed-lag models were similar to those of time-series models with meteorological data means with lag = 0. GDD and NL were principal factors affecting biennial yerba-mate shoot elongation and the number of green leaves of females grown in monoculture, besides their significant effects on metamer emission and leaf area in males grown in monoculture. NL also had a significant influence on shoot elongation and leaf area of both genders grown in forest understorey (FUS), indicating that yerba-mate growth is synchronized by an internal clock sensitive to temperature adjustments. The morphological plasticity and the adaptation efforts of yerba-mate were more pronounced in monoculture than in FUS. Sexual dimorphism was expressed—males were more sensitive to environmental changes than females, especially in monoculture. Growth modifications were much more intense when plants were grown in a cultivation system that is less like yerba-mate natural habitat (monoculture) than in one resembling its natural habitat (FUS). Our data support the ecological specialization theory.
NASA Astrophysics Data System (ADS)
Tigabu, T. B.; Hörmann, G.; Fohrer, N.
2015-12-01
Nowadays, time series environmental flow analysis is becoming one of the most important tasks in ecohydrology in order to design process based system solutions. Thus, the purpose of this research paper was to understand temporal and spatial variability of stream flows, rainfall, and inflows and outflows to and from the Lake Tana basin. Autocorrelation and cross correlation tests were applied for the long years' daily stream flows and rainfall using R languages. These methods were used to see how the stream flow or rainfall data were serially correlated and rainfall, stream flow and lake level time series data were cross correlated with each other. Autocorrelation tests of daily rainfall were carried out for many rainfall stations and the outputs indicate that there are no spikes showing significant seasonal signals. The annual rainfall map was produced for the whole catchment based on long years' records at different stations inside the catchment using inverse distance weighted interpolation (IDW) method in the GIS environment. Based on this map there is high spatial variability of annual rainfall in the catchment. The average maximum, minimum and mean annual rainfall values are 1506.4, 798.7, and 1238.1 mm respectively. According to the cross correlation tests done for stream flow & rainfall, better correlations were observed after 15 to 30 days lag time due to late response of the catchment for runoff generation. The study also prevailed that the Lake Tana water level and Blue Nile discharge at Bahir Dar station have positive cross correlation with maximum value at time lag of zero. There is a dramatic drop in the lake level and stream flow volume at the same location since 2000 due to human induced local climate forcing. In general, this research indicates that there is high temporal and spatial variability in rainfall, Lake water level and stream flows.
NASA Astrophysics Data System (ADS)
Luo, Shihua; Guo, Fan; Lai, Dejian; Yan, Fang; Tang, Feilai
2015-09-01
Hurst exponent is an important measure of nonlinearity of dynamical time series. In this paper, using rescaled-range (R/S) analysis, multi-fractal detrended fluctuation analysis (MF-DFA) methods, the multiscale Hurst exponent (MHE) and the multiscale generalized Hurst exponent (MGHE) of coarse-grained silicon content ([Si]) time series in blast furnace (BF) hot metal were calculated. First, we collected these [Si] time series from No. 1 BF of Nanchang Iron and Steel Co. and No. 10 BF of Xinyu Iron and Steel Co. in Jiangxi Province, China. Then, we analyzed and compared the estimated Hurst exponents and the generalized Hurst exponent of these observed time series with some simulated time series. Our results show that the observed time series from these BFs have negative correlation with the Hurst exponent less than 0.5, the generalized Hurst exponent H(q) is a nonlinear function of q, and such negative correlation and local various structure persist in their moving averages of the observed time series up to lag 5 or 10.
NASA Astrophysics Data System (ADS)
Mihailović, Dragutin T.; Mimić, Gordan; Nikolić-Djorić, Emilija; Arsenić, Ilija
2015-01-01
We propose novel metrics based on the Kolmogorov complexity for use in complex system behavior studies and time series analysis. We consider the origins of the Kolmogorov complexity and discuss its physical meaning. To get better insights into the nature of complex systems and time series analysis we introduce three novel measures based on the Kolmogorov complexity: (i) the Kolmogorov complexity spectrum, (ii) the Kolmogorov complexity spectrum highest value and (iii) the overall Kolmogorov complexity. The characteristics of these measures have been tested using a generalized logistic equation. Finally, the proposed measures have been applied to different time series originating from: a model output (the biochemical substance exchange in a multi-cell system), four different geophysical phenomena (dynamics of: river flow, long term precipitation, indoor 222Rn concentration and UV radiation dose) and the economy (stock price dynamics). The results obtained offer deeper insights into the complexity of system dynamics and time series analysis with the proposed complexity measures.
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
NASA Astrophysics Data System (ADS)
Neeti, N.; Eastman, R.
2012-12-01
Extended Principal Components Analysis (EPCA) aims to examine the patterns of variability shared among multiple image time series. Conventionally, this is done by virtually extending the spatial dimension of the time series by spatially concatenating the different time series and then performing S-mode PCA. In S-mode analysis, samples in space are the statistical variables and samples in time are the statistical observations. This paper introduces the concept of temporal concatenation of multiple image time series to perform EPCA. EPCA can also be done with T-mode orientation in which samples in time are the statistical variables and samples in space are the statistical observations. This leads to a total of four orientations in which EPCA can be carried out. This research explores these four orientations and their implications in investigating spatio-temporal relationships among multiple time series. This research demonstrates that EPCA carried out with temporal concatenation of the multiple time series with T-mode (tT) is able to identify similar spatial patterns among multiple time series. The conventional S-mode EPCA with spatial concatenation (sS) identifies similar temporal patterns among multiple time series. The other two modes, namely T-mode with spatial concatenation (sT) and S-mode with temporal concatenation (tS), are able to identify patterns which share consistent temporal phase relationships and consistent spatial phase relationships with each other, respectively. In a case study using three sets of precipitation time series data from GPCP, CMAP and NCEP-DOE, the results show that examination of all four modes provides an effective basis comparison of the series.
Time Series Analysis OF SAR Image Fractal Maps: The Somma-Vesuvio Volcanic Complex Case Study
NASA Astrophysics Data System (ADS)
Pepe, Antonio; De Luca, Claudio; Di Martino, Gerardo; Iodice, Antonio; Manzo, Mariarosaria; Pepe, Susi; Riccio, Daniele; Ruello, Giuseppe; Sansosti, Eugenio; Zinno, Ivana
2016-04-01
The fractal dimension is a significant geophysical parameter describing natural surfaces representing the distribution of the roughness over different spatial scale; in case of volcanic structures, it has been related to the specific nature of materials and to the effects of active geodynamic processes. In this work, we present the analysis of the temporal behavior of the fractal dimension estimates generated from multi-pass SAR images relevant to the Somma-Vesuvio volcanic complex (South Italy). To this aim, we consider a Cosmo-SkyMed data-set of 42 stripmap images acquired from ascending orbits between October 2009 and December 2012. Starting from these images, we generate a three-dimensional stack composed by the corresponding fractal maps (ordered according to the acquisition dates), after a proper co-registration. The time-series of the pixel-by-pixel estimated fractal dimension values show that, over invariant natural areas, the fractal dimension values do not reveal significant changes; on the contrary, over urban areas, it correctly assumes values outside the natural surfaces fractality range and show strong fluctuations. As a final result of our analysis, we generate a fractal map that includes only the areas where the fractal dimension is considered reliable and stable (i.e., whose standard deviation computed over the time series is reasonably small). The so-obtained fractal dimension map is then used to identify areas that are homogeneous from a fractal viewpoint. Indeed, the analysis of this map reveals the presence of two distinctive landscape units corresponding to the Mt. Vesuvio and Gran Cono. The comparison with the (simplified) geological map clearly shows the presence in these two areas of volcanic products of different age. The presented fractal dimension map analysis demonstrates the ability to get a figure about the evolution degree of the monitored volcanic edifice and can be profitably extended in the future to other volcanic systems with
FunPat: function-based pattern analysis on RNA-seq time series data
2015-01-01
Background Dynamic expression data, nowadays obtained using high-throughput RNA sequencing, are essential to monitor transient gene expression changes and to study the dynamics of their transcriptional activity in the cell or response to stimuli. Several methods for data selection, clustering and functional analysis are available; however, these steps are usually performed independently, without exploiting and integrating the information derived from each step of the analysis. Methods Here we present FunPat, an R package for time series RNA sequencing data that integrates gene selection, clustering and functional annotation into a single framework. FunPat exploits functional annotations by performing for each functional term, e.g. a Gene Ontology term, an integrated selection-clustering analysis to select differentially expressed genes that share, besides annotation, a common dynamic expression profile. Results FunPat performance was assessed on both simulated and real data. With respect to a stand-alone selection step, the integration of the clustering step is able to improve the recall without altering the false discovery rate. FunPat also shows high precision and recall in detecting the correct temporal expression patterns; in particular, the recall is significantly higher than hierarchical, k-means and a model-based clustering approach specifically designed for RNA sequencing data. Moreover, when biological replicates are missing, FunPat is able to provide reproducible lists of significant genes. The application to real time series expression data shows the ability of FunPat to select differentially expressed genes with high reproducibility, indirectly confirming high precision and recall in gene selection. Moreover, the expression patterns obtained as output allow an easy interpretation of the results. Conclusions A novel analysis pipeline was developed to search the main temporal patterns in classes of genes similarly annotated, improving the sensitivity of
NASA Astrophysics Data System (ADS)
Chen, Yu; Remy, Dominique; Froger, Jean-Luc; Darrozes, José; Bonvalot, Sylvain
2015-04-01
Piton de la Fournaise, located on the south-eastern side of Réunion Island in the Indian Ocean, is a hotspot oceanic basaltic shield volcano whose activity began more than 500,000 years ago. It is one of the most active volcanoes in the world with a high eruptive frequency on average one eruption every 9 months since 1998. In April 2007, Piton de la Fournaise experienced an exceptional eruption which is considered as the largest historical eruption ever observed during the 20th and 21th centuries, characterized by an effusion of 210 ×106 m3 volume of lava with a 340 m consequent collapse of the Dolomieu crater and the onset of a landslide on the eastern flank. ENVISAT and ALOS data analysis showed that the subsidence of central cone and landslide of eastern flank continued deforming after this eruption at least until June 2008, but no clear ground deformation has been detected after this date from Band-C or Band-L radar images. We so perform a detailed spatio-temporal analysis of ground motions on Piton de la Fournaise using X-band InSAR time series acquired from 2009 to 2014. X-Band was chosen because it provides high spatial resolution (up to 1 m), short revisit period (minimum 11 days) and a highest sensibility to ground deformation. Our large dataset of X-band radar images is composed of 106 COSMO-SkyMed and 96 TerraSAR-X Single-Look Complex images acquired in ascending and descending orbits. The interferograms were generated using DORIS. A high resolution reference Digital Elevation Model (DEM) (5m x 5m Lidar DEM) was used to model and remove the topographic contribution from the interferograms. We employed next StaMPS/MTI (Hooper et al., 2012) to generate the displacement time series and we analyzed the time-dependant behavior of surface displacement using a principal component analysis (PCA) decomposition. This analysis clearly reveals that the large eastward motion affecting the eastern flank of Piton de la Fournaise remained active (LOS velocity of about
Time series analysis of Cenozoic era sea level and paleotemperature data
Rosenfield, George H.; Huffman, Tod E.
1983-01-01
A statistical analysis of Cenozoic era sea level and paleotemperature data was performed to determine the cycles of each data set and the correspondence between them. Accordingly, each of the four time series were first analyzed independently in the univariate mode of a spectral analysis. The two basic data sets were then analyzed in a paired cross-spectral analysis. The prominent periodic cycles remaining in the data sets after linear trend removal, were: sea level surface from seismic stratigraphy--9.6 million years, updated version of sea level surface from seismic stratigraphy--9.5 million years, continental paleotemperatures from paleobotanical interpretations--9.6 million years, and marine paleotemperatures from foraminiferal isotopic data--12.7 million years. The cross-correlation properties between the data sets of continental paleotemperatures from paleobotanical interpretations and sea level surface from seismic stratigraphy at the common prominent period of 9.6 million years were: (1) The squared coherency value which measures cross correlation between the two data sets has the value 0.30, and (2) the amount by which the continental paleotemperatures from paleobotanical interpretations data lags the sea level surface from seismic stratigraphy data is 2.70 million years.
ZWD time series analysis derived from NRT data processing. A regional study of PW in Greece.
NASA Astrophysics Data System (ADS)
Pikridas, Christos; Balidakis, Kyriakos; Katsougiannopoulos, Symeon
2015-04-01
ZWD (Zenith Wet/non-hydrostatic Delay) estimates are routinely derived Near Real Time from the new established Analysis Center in the Department of Geodesy and Surveying of Aristotle University of Thessaloniki (DGS/AUT-AC), in the framework of E-GVAP (EUMETNET GNSS water vapour project) since October 2014. This process takes place on an hourly basis and yields, among else, station coordinates and tropospheric parameter estimates for a network of 90+ permanent GNSS (Global Navigation Satellite System) stations. These are distributed at the wider part of Hellenic region. In this study, temporal and spatial variability of ZWD estimates were examined, as well as their relation with coordinate series extracted from both float and fixed solution of the initial phase ambiguities. For this investigation, Bernese GNSS Software v5.2 was used for the acquisition of the 6 month dataset from the aforementioned network. For time series analysis we employed techniques such as the Generalized Lomb-Scargle periodogram and Burg's maximum entropy method due to inefficiencies of the Discrete Fourier Transform application in the test dataset. Through the analysis, interesting results for further geophysical interpretation were drawn. In addition, the spatial and temporal distributions of Precipitable Water vapour (PW) obtained from both ZWD estimates and ERA-Interim reanalysis grids were investigated.
Time-series analysis of temperature profiles from VIRTIS Venus Express data
NASA Astrophysics Data System (ADS)
Grassi, D.; Migliorini, A.; Politi, R.; Montabone, L.; Piccioni, G.; Drossart, P.
2012-04-01
Nighttime infrared observations of the VIRTIS instrument on board Venus Express have already demonstrated their potential in the study of air temperature fields of the Venusian mesosphere. The entire available dataset acquired by the VIRTIS-M IR channel was processed at moderate spatial resolution (i.e. averaging pixels in 8x8 boxes) to derive an unprecedented dataset of air temperature profiles in the pressure range 100-0.1 mbar, covering mostly the latitudes south of 45S. We presented in Grassi et al. (2010, doi:10.1029/2009JE003553) an analysis of the mean properties of temperature profiles, once binned in the latitude/local time/pressure space. Here we discuss the preliminary findings of time-series analysis of data from individual bins. Despite the sparsity of most series, Lomb-Scargle periodogram can be effectively applied in the regions south of 70S, where better coverage is made possible by specific properties of Venus Express orbit. Here the algorithm is able to extract a clear signature related to a period of about 115-120 Earth days, i.e. one Venus solar day, particularly strong at the level around 10 mbar. Further analysis of average temperature fields in the latitude - longitude space demonstrated, for different local times during night, that air temperatures east of Lada Terra (most specifically in a region centered around 130°E and about 60° wide) are about 10K warmer than in other longitudes at 75S.
NASA Technical Reports Server (NTRS)
Hailperin, M.
1993-01-01
This thesis provides design and analysis of techniques for global load balancing on ensemble architectures running soft-real-time object-oriented applications with statistically periodic loads. It focuses on estimating the instantaneous average load over all the processing elements. The major contribution is the use of explicit stochastic process models for both the loading and the averaging itself. These models are exploited via statistical time-series analysis and Bayesian inference to provide improved average load estimates, and thus to facilitate global load balancing. This thesis explains the distributed algorithms used and provides some optimality results. It also describes the algorithms' implementation and gives performance results from simulation. These results show that the authors' techniques allow more accurate estimation of the global system loading, resulting in fewer object migrations than local methods. The authors' method is shown to provide superior performance, relative not only to static load-balancing schemes but also to many adaptive load-balancing methods. Results from a preliminary analysis of another system and from simulation with a synthetic load provide some evidence of more general applicability.
3-dimensional (orthogonal) structural complexity of time-series data using low-order moment analysis
NASA Astrophysics Data System (ADS)
Law, Victor J.; O'Neill, Feidhlim T.; Dowling, Denis P.
2012-09-01
The recording of atmospheric pressure plasmas (APP) electro-acoustic emission data has been developed as a plasma metrology tool in the last couple of years. The industrial applications include automotive and aerospace industry for surface activation of polymers prior to bonding [1, 2, and 3]. It has been shown that as the APP jets proceeds over a treatment surface, at a various fixed heights, two contrasting acoustic signatures are produced which correspond to two very different plasma-surface entropy states (blow arc ˜ 1700 ± 100 K; and; afterglow ˜ 300-400 K) [4]. The metrology challenge is now to capture deterministic data points within data clusters. For this to be achieved new real-time data cluster measurement techniques needs to be developed [5]. The cluster information must be extracted within the allotted process time period if real-time process control is to be achieved. This abstract describes a theoretical structural complexity analysis (in terms crossing points) of 2 and 3-dimentional line-graphs that contain time-series data. In addition LabVIEW implementation of the 3-dimensional data analysis is performed. It is also shown the cluster analysis technique can be transfer to other (non-acoustic) datasets.
Nonlinear Analysis of Time Series in Genome-Wide Linkage Disequilibrium Data
NASA Astrophysics Data System (ADS)
Hernández-Lemus, Enrique; Estrada-Gil, Jesús K.; Silva-Zolezzi, Irma; Fernández-López, J. Carlos; Hidalgo-Miranda, Alfredo; Jiménez-Sánchez, Gerardo
2008-02-01
The statistical study of large scale genomic data has turned out to be a very important tool in population genetics. Quantitative methods are essential to understand and implement association studies in the biomedical and health sciences. Nevertheless, the characterization of recently admixed populations has been an elusive problem due to the presence of a number of complex phenomena. For example, linkage disequilibrium structures are thought to be more complex than their non-recently admixed population counterparts, presenting the so-called ancestry blocks, admixed regions that are not yet smoothed by the effect of genetic recombination. In order to distinguish characteristic features for various populations we have implemented several methods, some of them borrowed or adapted from the analysis of nonlinear time series in statistical physics and quantitative physiology. We calculate the main fractal dimensions (Kolmogorov's capacity, information dimension and correlation dimension, usually named, D0, D1 and D2). We also have made detrended fluctuation analysis and information based similarity index calculations for the probability distribution of correlations of linkage disequilibrium coefficient of six recently admixed (mestizo) populations within the Mexican Genome Diversity Project [1] and for the non-recently admixed populations in the International HapMap Project [2]. Nonlinear correlations showed up as a consequence of internal structure within the haplotype distributions. The analysis of these correlations as well as the scope and limitations of these procedures within the biomedical sciences are discussed.
NASA Astrophysics Data System (ADS)
Baldysz, Zofia; Nykiel, Grzegorz; Figurski, Mariusz; Szafranek, Karolina; Kroszczynski, Krzysztof; Araszkiewicz, Andrzej
2015-04-01
In recent years, the GNSS system began to play an increasingly important role in the research related to the climate monitoring. Based on the GPS system, which has the longest operational capability in comparison with other systems, and a common computational strategy applied to all observations, long and homogeneous ZTD (Zenith Tropospheric Delay) time series were derived. This paper presents results of analysis of 16-year ZTD time series obtained from the EPN (EUREF Permanent Network) reprocessing performed by the Military University of Technology. To maintain the uniformity of data, analyzed period of time (1998-2013) is exactly the same for all stations - observations carried out before 1998 were removed from time series and observations processed using different strategy were recalculated according to the MUT LAC approach. For all 16-year time series (59 stations) Lomb-Scargle periodograms were created to obtain information about the oscillations in ZTD time series. Due to strong annual oscillations which disturb the character of oscillations with smaller amplitude and thus hinder their investigation, Lomb-Scargle periodograms for time series with the deleted annual oscillations were created in order to verify presence of semi-annual, ter-annual and quarto-annual oscillations. Linear trend and seasonal components were estimated using LSE (Least Square Estimation) and Mann-Kendall trend test were used to confirm the presence of linear trend designated by LSE method. In order to verify the effect of the length of time series on the estimated size of the linear trend, comparison between two different length of ZTD time series was performed. To carry out a comparative analysis, 30 stations which have been operating since 1996 were selected. For these stations two periods of time were analyzed: shortened 16-year (1998-2013) and full 18-year (1996-2013). For some stations an additional two years of observations have significant impact on changing the size of linear
NASA Astrophysics Data System (ADS)
Bock, Y.; Crowell, B. W.; Dong, D.; Fang, P.; Kedar, S.; Liu, Z.; Moore, A. W.; Owen, S. E.; Prawirodirdjo, L. M.; Squibb, M. B.; Webb, F.
2011-12-01
As part of a NASA MEaSUREs project and its contribution to EarthScope, we are producing a combined 24-hour position time series for more than 1000 stations in Western North America based on independent analyses of continuous GPS data at JPL (using GIPSY software) and at SIO (using GAMIT software), using the SOPAC archive as a common source of metadata. Included are all EarthScope/PBO stations as well as stations from other networks still active (SCIGN, BARD and PANGA), and pre-PBO era data some already two decades old. The time series are appended weekly and the entire data set is filtered once a week using a modified principle component analysis (PCA) algorithm using the st_filter software. Both the unfiltered and filtered data undergo a time series analysis with the analyze_tseri software. All relevant time series are available through NASA's GPS Explorer data portal and its interactive Java-based time series utility. After a comprehensive process of re-analysis and quality control, we have evaluated the time series for transient deformation, that is, time series that deviate from linear behavior due to coseismic and postseismic deformation, slow slip events, volcanic events, and strain anomalies. In addition, we have observed non-tectonic effects from hydrologic, magmatic and anthropogenic sources which are manifested primarily in the vertical but sometimes bleed over into the horizontal and make tectonic interpretation and transient detection difficult. Other sources of anomalous deformation are due to dam deformation such as Diamond Valley Lake an important water reservoir in Southern California, and structural deformation including the Harvest oil platform used by NASA for altimeter calibrations. We present a compendium of transient deformation discovered in our time series analysis, including duration, geographical extent and magnitudes.
McKenna, Thomas M; Bawa, Gagandeep; Kumar, Kamal; Reifman, Jaques
2007-04-01
The physiology analysis system (PAS) was developed as a resource to support the efficient warehousing, management, and analysis of physiology data, particularly, continuous time-series data that may be extensive, of variable quality, and distributed across many files. The PAS incorporates time-series data collected by many types of data-acquisition devices, and it is designed to free users from data management burdens. This Web-based system allows both discrete (attribute) and time-series (ordered) data to be manipulated, visualized, and analyzed via a client's Web browser. All processes occur on a server, so that the client does not have to download data or any application programs, and the PAS is independent of the client's computer operating system. The PAS contains a library of functions, written in different computer languages that the client can add to and use to perform specific data operations. Functions from the library are sequentially inserted into a function chain-based logical structure to construct sophisticated data operators from simple function building blocks, affording ad hoc query and analysis of time-series data. These features support advanced mining of physiology data. PMID:17287043
A New Modified Histogram Matching Normalization for Time Series Microarray Analysis
Astola, Laura; Molenaar, Jaap
2014-01-01
Microarray data is often utilized in inferring regulatory networks. Quantile normalization (QN) is a popular method to reduce array-to-array variation. We show that in the context of time series measurements QN may not be the best choice for this task, especially not if the inference is based on continuous time ODE model. We propose an alternative normalization method that is better suited for network inference from time series data.
Documentation of a spreadsheet for time-series analysis and drawdown estimation
Halford, Keith J.
2006-01-01
Drawdowns during aquifer tests can be obscured by barometric pressure changes, earth tides, regional pumping, and recharge events in the water-level record. These stresses can create water-level fluctuations that should be removed from observed water levels prior to estimating drawdowns. Simple models have been developed for estimating unpumped water levels during aquifer tests that are referred to as synthetic water levels. These models sum multiple time series such as barometric pressure, tidal potential, and background water levels to simulate non-pumping water levels. The amplitude and phase of each time series are adjusted so that synthetic water levels match measured water levels during periods unaffected by an aquifer test. Differences between synthetic and measured water levels are minimized with a sum-of-squares objective function. Root-mean-square errors during fitting and prediction periods were compared multiple times at four geographically diverse sites. Prediction error equaled fitting error when fitting periods were greater than or equal to four times prediction periods. The proposed drawdown estimation approach has been implemented in a spreadsheet application. Measured time series are independent so that collection frequencies can differ and sampling times can be asynchronous. Time series can be viewed selectively and magnified easily. Fitting and prediction periods can be defined graphically or entered directly. Synthetic water levels for each observation well are created with earth tides, measured time series, moving averages of time series, and differences between measured and moving averages of time series. Selected series and fitting parameters for synthetic water levels are stored and drawdowns are estimated for prediction periods. Drawdowns can be viewed independently and adjusted visually if an anomaly skews initial drawdowns away from 0. The number of observations in a drawdown time series can be reduced by averaging across user
Water quality management using statistical analysis and time-series prediction model
NASA Astrophysics Data System (ADS)
Parmar, Kulwinder Singh; Bhardwaj, Rashmi
2014-12-01
This paper deals with water quality management using statistical analysis and time-series prediction model. The monthly variation of water quality standards has been used to compare statistical mean, median, mode, standard deviation, kurtosis, skewness, coefficient of variation at Yamuna River. Model validated using R-squared, root mean square error, mean absolute percentage error, maximum absolute percentage error, mean absolute error, maximum absolute error, normalized Bayesian information criterion, Ljung-Box analysis, predicted value and confidence limits. Using auto regressive integrated moving average model, future water quality parameters values have been estimated. It is observed that predictive model is useful at 95 % confidence limits and curve is platykurtic for potential of hydrogen (pH), free ammonia, total Kjeldahl nitrogen, dissolved oxygen, water temperature (WT); leptokurtic for chemical oxygen demand, biochemical oxygen demand. Also, it is observed that predicted series is close to the original series which provides a perfect fit. All parameters except pH and WT cross the prescribed limits of the World Health Organization /United States Environmental Protection Agency, and thus water is not fit for drinking, agriculture and industrial use.
Variability of African Farming Systems from Phenological Analysis of NDVI Time Series
NASA Technical Reports Server (NTRS)
Vrieling, Anton; deBeurs, K. M.; Brown, Molly E.
2011-01-01
Food security exists when people have access to sufficient, safe and nutritious food at all times to meet their dietary needs. The natural resource base is one of the many factors affecting food security. Its variability and decline creates problems for local food production. In this study we characterize for sub-Saharan Africa vegetation phenology and assess variability and trends of phenological indicators based on NDVI time series from 1982 to 2006. We focus on cumulated NDVI over the season (cumNDVI) which is a proxy for net primary productivity. Results are aggregated at the level of major farming systems, while determining also spatial variability within farming systems. High temporal variability of cumNDVI occurs in semiarid and subhumid regions. The results show a large area of positive cumNDVI trends between Senegal and South Sudan. These correspond to positive CRU rainfall trends found and relate to recovery after the 1980's droughts. We find significant negative cumNDVI trends near the south-coast of West Africa (Guinea coast) and in Tanzania. For each farming system, causes of change and variability are discussed based on available literature (Appendix A). Although food security comprises more than the local natural resource base, our results can perform an input for food security analysis by identifying zones of high variability or downward trends. Farming systems are found to be a useful level of analysis. Diversity and trends found within farming system boundaries underline that farming systems are dynamic.
Chamlin, Mitchell B.; Andreev, Evgeny
2013-01-01
Objectives. We took advantage of a natural experiment to assess the impact on suicide mortality of a suite of Russian alcohol policies. Methods. We obtained suicide counts from anonymous death records collected by the Russian Federal State Statistics Service. We used autoregressive integrated moving average (ARIMA) interrupted time series techniques to model the effect of the alcohol policy (implemented in January 2006) on monthly male and female suicide counts between January 2000 and December 2010. Results. Monthly male and female suicide counts decreased during the period under study. Although the ARIMA analysis showed no impact of the policy on female suicide mortality, the results revealed an immediate and permanent reduction of about 9% in male suicides (Ln ω0 = −0.096; P = .01). Conclusions. Despite a recent decrease in mortality, rates of alcohol consumption and suicide in Russia remain among the highest in the world. Our analysis revealed that the 2006 alcohol policy in Russia led to a 9% reduction in male suicide mortality, meaning the policy was responsible for saving 4000 male lives annually that would otherwise have been lost to suicide. Together with recent similar findings elsewhere, our results suggest an important role for public health and other population level interventions, including alcohol policy, in reducing alcohol-related harm. PMID:24028249
Statistical Analysis of fMRI Time-Series: A Critical Review of the GLM Approach.
Monti, Martin M
2011-01-01
Functional magnetic resonance imaging (fMRI) is one of the most widely used tools to study the neural underpinnings of human cognition. Standard analysis of fMRI data relies on a general linear model (GLM) approach to separate stimulus induced signals from noise. Crucially, this approach relies on a number of assumptions about the data which, for inferences to be valid, must be met. The current paper reviews the GLM approach to analysis of fMRI time-series, focusing in particular on the degree to which such data abides by the assumptions of the GLM framework, and on the methods that have been developed to correct for any violation of those assumptions. Rather than biasing estimates of effect size, the major consequence of non-conformity to the assumptions is to introduce bias into estimates of the variance, thus affecting test statistics, power, and false positive rates. Furthermore, this bias can have pervasive effects on both individual subject and group-level statistics, potentially yielding qualitatively different results across replications, especially after the thresholding procedures commonly used for inference-making. PMID:21442013
Nonlinear time series analysis: towards an effective forecast of rogue waves
NASA Astrophysics Data System (ADS)
Steinmeyer, Günter; Birkholz, Simon; Brée, Carsten; Demircan, Ayhan
2016-03-01
Rogue waves are extremely large waves that exceed any expectation based on long-term observation and Gaussian statistics. Ocean rogue waves exceed the significant wave height in the ocean by a factor 2. Similar phenomena have been observed in a multiplicity of optical systems. While the optical systems show a much higher frequency of rogue events than the ocean, it appears nevertheless questionable what conclusions can be drawn for the prediction of ocean rogue waves. Here we tackle the problem from a different perspective and analyze the predictability of rogue events in two optical systems as well as in the ocean using nonlinear time-series analysis. Our analysis is exclusively based on experimental data. The results appear rather surprising as the optical rogue wave scenario of fiber-based supercontinuum generation does not allow any prediction whereas real ocean rogue waves and a multifilament scenario do bear a considerable amount of determinism, which allows, at least in principle, the prediction of extreme events. It becomes further clear that there exist two fundamentally different types of rogue-wave supporting systems. One class of rogue waves is obviously seeded by quantum fluctuations whereas in the other class, linear random interference of waves seems to prevail.
Perpinan, O.; Lorenzo, E.
2011-01-15
The irradiance fluctuations and the subsequent variability of the power output of a PV system are analysed with some mathematical tools based on the wavelet transform. It can be shown that the irradiance and power time series are nonstationary process whose behaviour resembles that of a long memory process. Besides, the long memory spectral exponent {alpha} is a useful indicator of the fluctuation level of a irradiance time series. On the other side, a time series of global irradiance on the horizontal plane can be simulated by means of the wavestrapping technique on the clearness index and the fluctuation behaviour of this simulated time series correctly resembles the original series. Moreover, a time series of global irradiance on the inclined plane can be simulated with the wavestrapping procedure applied over a signal previously detrended by a partial reconstruction with a wavelet multiresolution analysis, and, once again, the fluctuation behaviour of this simulated time series is correct. This procedure is a suitable tool for the simulation of irradiance incident over a group of distant PV plants. Finally, a wavelet variance analysis and the long memory spectral exponent show that a PV plant behaves as a low-pass filter. (author)
NASA Astrophysics Data System (ADS)
Wesfreid, Eva; Billat, Véronique
2009-02-01
Data power law scaling behavior is observed in many fields. Velocity of fully developed turbulent flow, telecommunication traffic in networks, financial time series are some examples among many others. The goal of the present contribution is to show the scaling behavior of physiological time series in marathon races using wavelet leaders and the Detrended Fluctuation Analysis. Marathon race is an exhausting exercise, it is referenced as being a model for studying the limits of human ambulatory abilities. We analyzed the athlete's heart rate and speed time series recorded simultaneously. We find that the heart cost time series, number of heart beats per meter, increases with the fatigue appearing during the marathon race, its tendency grows in the second half of the race for all athletes. For most physiological time series, we observed a concave behavior of the wavelet leaders scaling exponents which suggests a multifractal behavior. Otherwise, the Detrended Fluctuation Analysis shows short and long range time-scale power law exponents with the same break point for each physiological time series and each athlete. The short range time-scale exponent increases with fatigue in most physiological signals.
A time series analysis of international immigration and suicide mortality in Canada.
Trovato, F
1986-01-01
A neglected topic concerning suicide as a sociological phenomenon is the relationship between international immigration and suicide mortality. This study examines the association between these variables using time series data for the period 1950 to 1982 in Canada. The central hypothesis, derived from the Durkheimian theory of social integration and suicide, is that the higher the immigration rate, the higher the rate of suicide. Two statistical controls, the unemployment rate and age composition, drawn from the "economic anomie" and "social demographic" perspectives respectively were introduced into a multiple regression model involving immigration and suicide. While some of the results in the initial stages of the analysis appear to contradict the established literature concerning the relevance of immigration and unemployment in predicting suicide, more refined breakdowns which allow for the separate investigation of male and female suicide propensities, generally support the social integration and economic anomie theories. It is concluded that the 15-34 male cohort is highly sensitive to changes in their economic prospects and in their immigration experience and therefore, have higher suicide rates than women in the same age group. PMID:3733348
Daily ambient temperature and renal colic incidence in Guangzhou, China: a time-series analysis.
Yang, Changyuan; Chen, Xinyu; Chen, Renjie; Cai, Jing; Meng, Xia; Wan, Yue; Kan, Haidong
2016-08-01
Few previous studies have examined the association between temperature and renal colic in developing regions, especially in China, the largest developing country in the world. We collected daily emergency ambulance dispatches (EADs) for renal colic from Guangzhou Emergency Center from 1 January 2008 to 31 December 2012. We used a distributed-lag nonlinear model in addition to the over-dispersed generalized additive model to investigate the association between daily ambient temperature and renal colic incidence after controlling for seasonality, humidity, public holidays, and day of the week. We identified 3158 EADs for renal colic during the study period. This exposure-response curve was almost flat when the temperature was low and moderate and elevated when the temperature increased over 21 °C. For heat-related effects, the significant risk occurred on the concurrent day and diminished until lag day 7. The cumulative relative risk of hot temperatures (90th percentile) and extremely hot temperatures (99th percentile) over lag days 0-7 was 1.92 (95 % confidence interval, 1.21, 3.05) and 2.45 (95 % confidence interval, 1.50, 3.99) compared with the reference temperature of 21 °C. This time-series analysis in Guangzhou, China, suggested a nonlinear and lagged association between high outdoor temperatures and daily EADs for renal colic. Our findings might have important public health significance to prevent renal colic. PMID:26581758
Forecasting Container Throughput at the Doraleh Port in Djibouti through Time Series Analysis
NASA Astrophysics Data System (ADS)
Mohamed Ismael, Hawa; Vandyck, George Kobina
The Doraleh Container Terminal (DCT) located in Djibouti has been noted as the most technologically advanced container terminal on the African continent. DCT's strategic location at the crossroads of the main shipping lanes connecting Asia, Africa and Europe put it in a unique position to provide important shipping services to vessels plying that route. This paper aims to forecast container throughput through the Doraleh Container Port in Djibouti by Time Series Analysis. A selection of univariate forecasting models has been used, namely Triple Exponential Smoothing Model, Grey Model and Linear Regression Model. By utilizing the above three models and their combination, the forecast of container throughput through the Doraleh port was realized. A comparison of the different forecasting results of the three models, in addition to the combination forecast is then undertaken, based on commonly used evaluation criteria Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE). The study found that the Linear Regression forecasting Model was the best prediction method for forecasting the container throughput, since its forecast error was the least. Based on the regression model, a ten (10) year forecast for container throughput at DCT has been made.
Time-Series Analysis of Seasonal Changes of the O Inversion Polymorphism of Drosophila Subobscura
Rodriguez-Trelles, F.; Alvarez, G.; Zapata, C.
1996-01-01
We have studied seasonal variation (spring, early summer, last summer and autumn) of inversion polymorphisms of the O chromosome of Drosophila subobscura in a natural population over 15 years. The length of the study allowed us to investigate the temporal behavior (short-term seasonal changes and long-term directional trends) of the O arrangements by the powerful statistical method of time series analysis. It is shown that the O inversion polymorphisms varied on two different time scales: short-term seasonal changes repeated over the years superimposed on long-term directional trends. All the common arrangements (O(3+4+7), O(ST), O(3+4) and O(3+4+8)) showed significant cyclic seasonal changes, and all but one of these arrangements (O(3+4+7)) showed significant long-term trends. Moreover, the degree of seasonality was different for different arrangements. Thus, O(3+4+7) and O(ST) showed the highest seasonality, which accounted for ~61 and 47% of their total variances, respectively. The seasonal changes in the frequencies of chromosome arrangements were significantly associated with the seasonal variation of the climate (temperature, rainfall, humidity and insolation). In particular, O(3+4+7) and O(ST), the arrangements with the greatest seasonal component, showed the strongest association with all climatic factors investigated, especially to the seasonal changes of extreme temperature and humidity. PMID:8770595
Multiscale InSAR Time Series (MInTS) analysis of surface deformation
NASA Astrophysics Data System (ADS)
Hetland, E. A.; Musé, P.; Simons, M.; Lin, Y. N.; Agram, P. S.; Dicaprio, C. J.
2012-02-01
We present a new approach to extracting spatially and temporally continuous ground deformation fields from interferometric synthetic aperture radar (InSAR) data. We focus on unwrapped interferograms from a single viewing geometry, estimating ground deformation along the line-of-sight. Our approach is based on a wavelet decomposition in space and a general parametrization in time. We refer to this approach as MInTS (Multiscale InSAR Time Series). The wavelet decomposition efficiently deals with commonly seen spatial covariances in repeat-pass InSAR measurements, since the coefficients of the wavelets are essentially spatially uncorrelated. Our time-dependent parametrization is capable of capturing both recognized and unrecognized processes, and is not arbitrarily tied to the times of the SAR acquisitions. We estimate deformation in the wavelet-domain, using a cross-validated, regularized least squares inversion. We include a model-resolution-based regularization, in order to more heavily damp the model during periods of sparse SAR acquisitions, compared to during times of dense acquisitions. To illustrate the application of MInTS, we consider a catalog of 92 ERS and Envisat interferograms, spanning 16 years, in the Long Valley caldera, CA, region. MInTS analysis captures the ground deformation with high spatial density over the Long Valley region.
Multiscale InSAR Time Series (MInTS) analysis of surface deformation
NASA Astrophysics Data System (ADS)
Hetland, E. A.; Muse, P.; Simons, M.; Lin, Y. N.; Agram, P. S.; DiCaprio, C. J.
2011-12-01
We present a new approach to extracting spatially and temporally continuous ground deformation fields from interferometric synthetic aperture radar (InSAR) data. We focus on unwrapped interferograms from a single viewing geometry, estimating ground deformation along the line-of-sight. Our approach is based on a wavelet decomposition in space and a general parametrization in time. We refer to this approach as MInTS (Multiscale InSAR Time Series). The wavelet decomposition efficiently deals with commonly seen spatial covariances in repeat-pass InSAR measurements, such that coefficients of the wavelets are essentially spatially uncorrelated. Our time-dependent parametrization is capable of capturing both recognized and unrecognized processes, and is not arbitrarily tied to the times of the SAR acquisitions. We estimate deformation in the wavelet-domain, using a cross-validated, regularized least-squares inversion. We include a model-resolution-based regularization, in order to more heavily damp the model during periods of sparse SAR acquisitions, compared to during times of dense acquisitions. To illustrate the application of MInTS, we consider a catalog of 92 ERS and Envisat interferograms, spanning 16 years, in the Long Valley caldera, CA, region. MInTS analysis captures the ground deformation with high spatial density over the Long Valley region.
Sumatriptan and lost productivity time: a time series analysis of diary data.
Miller, D W; Martin, B C; Loo, C M
1996-01-01
Two previously conducted clinical studies assessed lost nonworkplace activity time and lost workplace productivity time due to migraine symptoms in subjects using sumatriptan for 6 months to treat their migraines after a 12- to 18-week period of using their usual therapy without sumatriptan. Although statistically significant differences in lost nonworkplace activity time and lost workplace productivity time between the usual therapy and sumatriptan treatment periods were detected using the Wilcoxon signed-rank test, this test could not determine whether differences were attributable to inherent trends in the data. This current study employed time series analysis, which detects and controls for preexisting trends in data, to further explore the possibility that the observed reductions in lost time in the two clinical studies were related to management of the subjects with sumatriptan. The intercepts and slopes of the computed linear models suggest that the initiation of sumatriptan therapy produced savings of 0.8 hours of nonworkplace activity time and 0.5 hours of workplace productivity time per patient per week. These savings were sustained throughout the sumatriptan treatment period. Preexisting trends in the data were not detected in the models. Thus the productivity gains are not associated with either time effects or the statistical phenomenon of regression to the mean, but variables that are extreme in initial measurements will tend to be closer to the center of the distribution in subsequent measurements. This strengthens the hypothesis that management of migraine with sumatriptan is associated with reductions in lost productivity time. PMID:9001842
A Long-Term Prediction Model of Beijing Haze Episodes Using Time Series Analysis.
Yang, Xiaoping; Zhang, Zhongxia; Zhang, Zhongqiu; Sun, Liren; Xu, Cui; Yu, Li
2016-01-01
The rapid industrial development has led to the intermittent outbreak of pm2.5 or haze in developing countries, which has brought about great environmental issues, especially in big cities such as Beijing and New Delhi. We investigated the factors and mechanisms of haze change and present a long-term prediction model of Beijing haze episodes using time series analysis. We construct a dynamic structural measurement model of daily haze increment and reduce the model to a vector autoregressive model. Typical case studies on 886 continuous days indicate that our model performs very well on next day's Air Quality Index (AQI) prediction, and in severely polluted cases (AQI ≥ 300) the accuracy rate of AQI prediction even reaches up to 87.8%. The experiment of one-week prediction shows that our model has excellent sensitivity when a sudden haze burst or dissipation happens, which results in good long-term stability on the accuracy of the next 3-7 days' AQI prediction. PMID:27597861
Kam, Hye Jin; Sung, Jin Ok
2010-01-01
Objectives To develop and evaluate time series models to predict the daily number of patients visiting the Emergency Department (ED) of a Korean hospital. Methods Data were collected from the hospital information system database. In order to develop a forecasting model, we used, 2 years of data from January 2007 to December 2008 data for the following 3 consecutive months were processed for validation. To establish a Forecasting Model, calendar and weather variables were utilized. Three forecasting models were established: 1) average; 2) univariate seasonal auto-regressive integrated moving average (SARIMA); and 3) multivariate SARIMA. To evaluate goodness-of-fit, residual analysis, Akaike information criterion and Bayesian information criterion were compared. The forecast accuracy for each model was evaluated via mean absolute percentage error (MAPE). Results The multivariate SARIMA model was the most appropriate for forecasting the daily number of patients visiting the ED. Because it's MAPE was 7.4%, this was the smallest among the models, and for this reason was selected as the final model. Conclusions This study applied explanatory variables to a multivariate SARIMA model. The multivariate SARIMA model exhibits relativelyhigh reliability and forecasting accuracy. The weather variables play a part in predicting daily ED patient volume. PMID:21818435
Mapping mountain pine beetle mortality through growth trend analysis of time-series landsat data
Liang, Lu; Chen, Yanlei; Hawbaker, Todd J.; Zhu, Zhi-Liang; Gong, Peng
2014-01-01
Disturbances are key processes in the carbon cycle of forests and other ecosystems. In recent decades, mountain pine beetle (MPB; Dendroctonus ponderosae) outbreaks have become more frequent and extensive in western North America. Remote sensing has the ability to fill the data gaps of long-term infestation monitoring, but the elimination of observational noise and attributing changes quantitatively are two main challenges in its effective application. Here, we present a forest growth trend analysis method that integrates Landsat temporal trajectories and decision tree techniques to derive annual forest disturbance maps over an 11-year period. The temporal trajectory component successfully captures the disturbance events as represented by spectral segments, whereas decision tree modeling efficiently recognizes and attributes events based upon the characteristics of the segments. Validated against a point set sampled across a gradient of MPB mortality, 86.74% to 94.00% overall accuracy was achieved with small variability in accuracy among years. In contrast, the overall accuracies of single-date classifications ranged from 37.20% to 75.20% and only become comparable with our approach when the training sample size was increased at least four-fold. This demonstrates that the advantages of this time series work flow exist in its small training sample size requirement. The easily understandable, interpretable and modifiable characteristics of our approach suggest that it could be applicable to other ecoregions.
Bayesian time series analysis of segments of the Rocky Mountain trumpeter swan population
Wright, Christopher K.; Sojda, Richard S.; Goodman, Daniel
2002-01-01
A Bayesian time series analysis technique, the dynamic linear model, was used to analyze counts of Trumpeter Swans (Cygnus buccinator) summering in Idaho, Montana, and Wyoming from 1931 to 2000. For the Yellowstone National Park segment of white birds (sub-adults and adults combined) the estimated probability of a positive growth rate is 0.01. The estimated probability of achieving the Subcommittee on Rocky Mountain Trumpeter Swans 2002 population goal of 40 white birds for the Yellowstone segment is less than 0.01. Outside of Yellowstone National Park, Wyoming white birds are estimated to have a 0.79 probability of a positive growth rate with a 0.05 probability of achieving the 2002 objective of 120 white birds. In the Centennial Valley in southwest Montana, results indicate a probability of 0.87 that the white bird population is growing at a positive rate with considerable uncertainty. The estimated probability of achieving the 2002 Centennial Valley objective of 160 white birds is 0.14 but under an alternative model falls to 0.04. The estimated probability that the Targhee National Forest segment of white birds has a positive growth rate is 0.03. In Idaho outside of the Targhee National Forest, white birds are estimated to have a 0.97 probability of a positive growth rate with a 0.18 probability of attaining the 2002 goal of 150 white birds.
NASA Astrophysics Data System (ADS)
Ku, Taeyun; Lee, Jungsul; Choi, Chulhee
2010-02-01
Measurement of cerebral perfusion is important for study of various brain disorders such as stroke, epilepsy, and vascular dementia; however, efficient and convenient methods which can provide quantitative information about cerebral blood flow are not developed. Here we propose an optical imaging method using time-series analysis of dynamics of indocyanine green (ICG) fluorescence to generate cerebral blood flow maps. In scalp-removed mice, ICG was injected intravenously, and 740nm LED light was illuminated for fluorescence emission signals around 820nm acquired by cooled-CCD. Time-lapse 2-dimensional images were analyzed by custom-built software, and the maximal time point of fluorescent influx in each pixel was processed as a blood flow-related parameter. The generated map exactly reflected the shape of the brain without any interference of the skull, the dura mater, and other soft tissues. This method may be further applicable for study of other disease models in which the cerebral hemodynamics is changed either acutely or chronically.
The Terror Attacks of 9/11 and Suicides in Germany: A Time Series Analysis.
Medenwald, Daniel
2016-04-01
Data on the effect of the September 11, 2001 (9/11) terror attacks on suicide rates remain inconclusive. Reportedly, even people located far from the attack site have considerable potential for personalizing the events that occurred on 9/11. Durkheim's theory states that suicides decrease during wartime; thus, a decline in suicides might have been expected after 9/11. We conducted a time series analysis of 164,136 officially recorded suicides in Germany between 1995 and 2009 using the algorithm introduced by Box and Jenkins. Compared with the average death rate, we observed no relevant change in the suicide rate of either sex after 9/11. Our estimates of an excess of suicides approached the null effect value on and within a 7-day period after 9/11, which also held when subsamples of deaths in urban or rural settings were examined. No evidence of Durkheim's theory attributable to the 9/11attacks was found in this sample. PMID:27082561
Analysis of long time series of precipitable water vapour from GPS, DORIS and NWP models
NASA Astrophysics Data System (ADS)
Bock, Olivier; Willis, Pascal
2013-04-01
The analysis of GPS and DORIS measurements provides accurate estimates of zenith tropospheric delay (ZTD) and total column water vapour (TCWV). Such measurements are now available for more than 15 years from permanent ground-based stations which cover quite homogenously the globe and receive increasing interest for meteorology and climate research. This work assesses the quality of operational and reprocessed GPS and DORIS datasets. Regarding GPS, two solutions produced by JPL as contributions to IGS (repro1, covering period 1995-2007, and trop_new, covering period 2001-2010) are compared. An independent reprocessed solution produced by IGN (sgn_repro1, covering period 2004-2010) is also used in the intercomparison. Differences due to different data processing procedures and errors in metadata and discontinuities due to changes in data processing procedures are evidenced in the operational solution. A reprocessed DORIS solution (IGN solution, period 1993-2008) is also compared to GPS and to the ECMWF reanalysis (ERA-Interim). The impact of changes in GPS or DORIS equipment on the quality of the ZTD estimates is investigated. The reprocessed GPS and DORIS ZTD estimates are converted into TCWV and analysed globally and for different regions. The TCWV time series reveal significant variability at various timescales (inter-annual, seasonal, intra-seasonal and synoptic) and look very promising for validating independent observational datasets (e.g., radiosondes and satellite products) and models (reanalyses, climate models).
NASA Astrophysics Data System (ADS)
Cooper, Crystal Diane
A computer program was modified to model the dynamics of morphogen concentrations in a developing eye of a Xenopus laevis frog. The dynamics were modelled because it is believed that the behavior of the morphogen concentrations determine how the developing eye maps to the brain. The eye in the xenophus grows as a series of rings, and thus this is the model used. The basis for the simulation are experiments done by Sullivan et al. Following the experiment, aIl eye ring is 'split' in half, inverted, and then 'pasted' onto a donor half. The purpose of the program is to replicate and analyze the results that were found experimentally: a graft made on a north to south axis (dorsal to ventral) produces a change in vision along the east to west axis (anterior to posterior). Four modified Gierer-Meinhardt reaction- diffusion equations are used to simulate the operation. In the second part of the research, the program was further modified and a time series analysis was done on the results. It was found that the modified Gierer- Meinhardt equations demonstrated chaotic behavior under certain conditions. The dynamics included fixed points, limit cycles, transient chaos, intermittent chaos, and strange attractors. The creation and destruction of fractal torii was found.
Assessing coal-mine safety regulation: A pooled time-series analysis
Chun Youngpyoung.
1991-01-01
This study attempts to assess the independent, relative, and conjoint effects of four types of variables on coal-mine safety: administrative (mine inspections, mine investigations, and mine safety grants); political (state party competition, gubernatorial party affiliation, and deregulation); economic (state per-capita income and unemployment rates); task-related (mine size, technology, and type of mining), and state dummy variables. Trend, Pearson correlation, and pooled time-series analyses are performed on fatal and nonfatal injury rates reported in 25 coal-producing states during the 1975-1985 time period. These are then interpreted in light of three competing theories of regulation: capture, nonmarket failure, and threshold. Analysis reveals: (1) distinctions in the total explanatory power of the model across different types of injuries, as well as across presidential administrations; (2) a consistently more powerful impact on safety of informational implementation tools (safety education grants) over command-and-control approaches (inspections and investigations) or political variables; and (3) limited, albeit conjectural, support for a threshold theory of regulation in the coal mine safety arena.
Advanced SuperDARN meteor wind observations based on raw time series analysis technique
NASA Astrophysics Data System (ADS)
Tsutsumi, M.; Yukimatu, A. S.; Holdsworth, D. A.; Lester, M.
2009-04-01
The meteor observation technique based on SuperDARN raw time series analysis has been upgraded. This technique extracts meteor information as biproducts and does not degrade the quality of normal SuperDARN operations. In the upgrade the radar operating system (RADOPS) has been modified so that it can oversample every 15 km during the normal operations, which have a range resolution of 45 km. As an alternative method for better range determination a frequency domain interferometry (FDI) capability was also coded in RADOPS, where the operating radio frequency can be changed every pulse sequence. Test observations were conducted using the CUTLASS Iceland East and Finland radars, where oversampling and FDI operation (two frequencies separated by 3 kHz) were simultaneously carried out. Meteor ranges obtained in both ranging techniques agreed very well. The ranges were then combined with the interferometer data to estimate meteor echo reflection heights. Although there were still some ambiguities in the arrival angles of echoes because of the rather long antenna spacing of the interferometers, the heights and arrival angles of most of meteor echoes were more accurately determined than previously. Wind velocities were successfully estimated over the height range of 84 to 110 km. The FDI technique developed here can be further applied to the common SuperDARN operation, and study of fine horizontal structures of F region plasma irregularities is expected in the future.
Snow depth on Arctic sea ice derived from radar: In situ comparisons and time series analysis
NASA Astrophysics Data System (ADS)
Holt, Benjamin; Johnson, Michael P.; Perkovic-Martin, Dragana; Panzer, Ben
2015-06-01
The snow radar being flown on NASA's Operation IceBridge, ongoing aircraft campaigns to the Arctic and the Antarctic are providing unique observations of the depth of snow on the sea ice cover. In this paper, we focus on the radar-derived snow depth results from the 2009-2012 Arctic campaigns. We develop and evaluate the use of a distinct snow layer tracker to measure snow depth based on a Support Vector Machine (SVM) supervised learning algorithm. The snow radar is designed to detect both the air-snow and snow-ice interfaces using ultrawideband frequencies from 2 to 8 GHz. The quality, errors, and repeatability of the snow radar snow depth estimates are examined, based on comparisons with in situ data obtained during two separate sea ice field campaigns, the GreenArc 2009 and the CryoVEx 2011 campaigns off Greenland in the Lincoln Sea. Finally, we analyze 4 years (2009-2012) of three annually repeated sea ice flight lines obtained in early spring, located off Greenland and the Canadian Arctic. We examine the annual variations of snow depth differences between perennial and seasonal ice when available. Overall, the snow layer tracker produced consistent, accurate results for snow depths between 0.10 and ˜0.60 m. This was confirmed with comparisons with the two data sets from the in situ measurement campaigns as well as with the time series analysis, and is consistent with other published results.
Inferring the connectivity of coupled oscillators from time-series statistical similarity analysis
Tirabassi, Giulio; Sevilla-Escoboza, Ricardo; Buldú, Javier M.; Masoller, Cristina
2015-01-01
A system composed by interacting dynamical elements can be represented by a network, where the nodes represent the elements that constitute the system, and the links account for their interactions, which arise due to a variety of mechanisms, and which are often unknown. A popular method for inferring the system connectivity (i.e., the set of links among pairs of nodes) is by performing a statistical similarity analysis of the time-series collected from the dynamics of the nodes. Here, by considering two systems of coupled oscillators (Kuramoto phase oscillators and Rössler chaotic electronic oscillators) with known and controllable coupling conditions, we aim at testing the performance of this inference method, by using linear and non linear statistical similarity measures. We find that, under adequate conditions, the network links can be perfectly inferred, i.e., no mistakes are made regarding the presence or absence of links. These conditions for perfect inference require: i) an appropriated choice of the observed variable to be analysed, ii) an appropriated interaction strength, and iii) an adequate thresholding of the similarity matrix. For the dynamical units considered here we find that the linear statistical similarity measure performs, in general, better than the non-linear ones. PMID:26042395
A Long-Term Prediction Model of Beijing Haze Episodes Using Time Series Analysis
Zhang, Zhongqiu; Sun, Liren; Xu, Cui
2016-01-01
The rapid industrial development has led to the intermittent outbreak of pm2.5 or haze in developing countries, which has brought about great environmental issues, especially in big cities such as Beijing and New Delhi. We investigated the factors and mechanisms of haze change and present a long-term prediction model of Beijing haze episodes using time series analysis. We construct a dynamic structural measurement model of daily haze increment and reduce the model to a vector autoregressive model. Typical case studies on 886 continuous days indicate that our model performs very well on next day's Air Quality Index (AQI) prediction, and in severely polluted cases (AQI ≥ 300) the accuracy rate of AQI prediction even reaches up to 87.8%. The experiment of one-week prediction shows that our model has excellent sensitivity when a sudden haze burst or dissipation happens, which results in good long-term stability on the accuracy of the next 3–7 days' AQI prediction. PMID:27597861
Daily ambient temperature and renal colic incidence in Guangzhou, China: a time-series analysis
NASA Astrophysics Data System (ADS)
Yang, Changyuan; Chen, Xinyu; Chen, Renjie; Cai, Jing; Meng, Xia; Wan, Yue; Kan, Haidong
2015-11-01
Few previous studies have examined the association between temperature and renal colic in developing regions, especially in China, the largest developing country in the world. We collected daily emergency ambulance dispatches (EADs) for renal colic from Guangzhou Emergency Center from 1 January 2008 to 31 December 2012. We used a distributed-lag nonlinear model in addition to the over-dispersed generalized additive model to investigate the association between daily ambient temperature and renal colic incidence after controlling for seasonality, humidity, public holidays, and day of the week. We identified 3158 EADs for renal colic during the study period. This exposure-response curve was almost flat when the temperature was low and moderate and elevated when the temperature increased over 21 °C. For heat-related effects, the significant risk occurred on the concurrent day and diminished until lag day 7. The cumulative relative risk of hot temperatures (90th percentile) and extremely hot temperatures (99th percentile) over lag days 0-7 was 1.92 (95 % confidence interval, 1.21, 3.05) and 2.45 (95 % confidence interval, 1.50, 3.99) compared with the reference temperature of 21 °C. This time-series analysis in Guangzhou, China, suggested a nonlinear and lagged association between high outdoor temperatures and daily EADs for renal colic. Our findings might have important public health significance to prevent renal colic.
NASA Astrophysics Data System (ADS)
Galway, L. P.; Allen, D. M.; Parkes, M. W.; Li, L.; Takaro, T. K.
2015-02-01
Using epidemiologic time series analysis, we examine associations between three hydroclimatic variables (temperature, precipitation, and streamflow) and waterborne acute gastro-intestinal illness (AGI) in two communities in the province of British Columbia (BC), Canada. The communities were selected to represent the major hydroclimatic regimes that characterize BC: rainfall-dominated and snowfall dominated. Our results show that the number of monthly cases of AGI increased with increasing temperature, precipitation, and streamflow in the same month in the context of a rainfall-dominated regime, and with increasing streamflow in the previous month in the context of a snowfall-dominated regime. These results suggest that hydroclimatology plays a role in driving the occurrence and variability of AGI in these settings. Further, this study highlights that the nature and magnitude of the effects of hydroclimatic variability on AGI are different in the context of a snowfall-dominated regime versus a rainfall-dominated regimes. We conclude by proposing that the watershed may be an appropriate context for enhancing our understanding of the complex linkages between hydroclimatic variability and waterborne illness in the context of a changing climate.
Detection of fault creep around NAF by InSAR time series analysis using PALSAR data
NASA Astrophysics Data System (ADS)
Deguchi, Tomonori
2011-11-01
North Anatolian Fault (NAF) has several records of a huge earthquake occurrence in the last one century, which is well-known as a risky active fault. Some signs indicating a creep displacement could be observed on the Ismetpasa segment. It is reported so far that the San Andreas Fault in California, the Longitudinal Valley fault in Taiwan and the Valley Fault System in Metro Manila also exhibit fault creep. The fault with creep deformation is aseismic and never generates the large-scale earthquakes. But the scale and rate of fault creep are important factors to watch the fault behavior and to understand the cycle of earthquake. The purpose of this study is to investigate the distribution of spatial and temporal change on the ground motion due to fault creep in the surrounding of the Ismetpasa, NAF. DInSAR is capable to catch a subtle land displacement less than a centimeter and observe a wide area at a high spatial resolution. We applied InSAR time series analysis using PALSAR data in order to measure long-term ground deformation from 2007 until 2011. As a result, the land deformation that the northern and southern parts of the fault have slipped to east and west at a rate of 7.5 and 6.5 mm/year in line of sight respectively were obviously detected. In addition, it became clear that the fault creep along the NAF extended 61 km in east to west direction.
Clarifying a cloudy issue: time-series analysis of atmospheric deposition
Aramburu, J.C.; Parkhurst, W.J.
1983-01-01
The Tennessee Valley Authority monitored biweekly rainfall samples over a 10-year period to see if there were any long- or short-term trends, cycles, or tendencies, and to detect and describe any significant change in the mean level of sulfate (SO/sub 4/), nitrate (NO/sub 3/), and hydrogen (H/sub +/) ion wet deposition. The project also sought to develop forecast models based on empirical information. When all 10 stations in the sampling network revealed similar cycles and levels of deposition, the time-series analysis and forecast model development focused on only two plants. No trends developed during the study, leading to the conclusion that there had been no change in the wet deposition of SO/sub 4/, NO/sub 3/, and H/sub +/ in the region surrounding the Cumberland Steam Plant during the period. Although one forecast model proved valid, the wet deposition of SO/sub 4/, NO/sub 3/, and H/sub +/ data did not generate useful forecasts. 10 references, 5 figures.
Daily ambient temperature and renal colic incidence in Guangzhou, China: a time-series analysis
NASA Astrophysics Data System (ADS)
Yang, Changyuan; Chen, Xinyu; Chen, Renjie; Cai, Jing; Meng, Xia; Wan, Yue; Kan, Haidong
2016-08-01
Few previous studies have examined the association between temperature and renal colic in developing regions, especially in China, the largest developing country in the world. We collected daily emergency ambulance dispatches (EADs) for renal colic from Guangzhou Emergency Center from 1 January 2008 to 31 December 2012. We used a distributed-lag nonlinear model in addition to the over-dispersed generalized additive model to investigate the association between daily ambient temperature and renal colic incidence after controlling for seasonality, humidity, public holidays, and day of the week. We identified 3158 EADs for renal colic during the study period. This exposure-response curve was almost flat when the temperature was low and moderate and elevated when the temperature increased over 21 °C. For heat-related effects, the significant risk occurred on the concurrent day and diminished until lag day 7. The cumulative relative risk of hot temperatures (90th percentile) and extremely hot temperatures (99th percentile) over lag days 0-7 was 1.92 (95 % confidence interval, 1.21, 3.05) and 2.45 (95 % confidence interval, 1.50, 3.99) compared with the reference temperature of 21 °C. This time-series analysis in Guangzhou, China, suggested a nonlinear and lagged association between high outdoor temperatures and daily EADs for renal colic. Our findings might have important public health significance to prevent renal colic.
Trend analysis of time-series phenology derived from satellite data
Reed, B.C.; Brown, J.F.
2005-01-01
Remote sensing information has been used in studies of the seasonal dynamics (phenology) of the land surface for the past 15 years. While our understanding of remote sensing phenology is still in development, it is regarded as a key to understanding land surface processes over large areas. Repeat observations from satellite-borne multispectral sensors provide a mechanism to move from plant-specific to regional scale studies of phenology. In addition, we now have sufficient time-series (since 1982 at 8-km resolution covering the globe and since 1989 at 1-km resolution over the conterminous US) to study seasonal and interannual trends from satellite data. Phenology metrics including start of season, end of season, duration of season, and seasonally integrated greenness were derived from 8 km AVHRR data over North America spanning the years 1982-2003. Trend analysis was performed on the annual summaries of the metrics to determine areas with increasing or decreasing trends for the time period under study. Results show only small areas of changing start of season, but the end of season is coming later over well defined areas of New England and SE Canada, principally as a result of land use changes. The total greenness metric is most striking at the shrub/tundra boundary of North America, indicating increasing vegetation vigor or possible vegetation conversion as a result of warming. ?? 2005 IEEE.
Time Series Analysis of Particle Tracking Data for Molecular Motion on the Cell Membrane
Ying, Wenxia; Huerta, Gabriel; Steinberg, Stanly; Zúñiga, Martha
2013-01-01
Biophysicists use single particle tracking (SPT) methods to probe the dynamic behavior of individual proteins and lipids in cell membranes. The mean squared displacement (MSD) has proven to be a powerful tool for analyzing the data and drawing conclusions about membrane organization, including features like lipid rafts, protein islands, and confinement zones defined by cytoskeletal barriers. Here, we implement time series analysis as a new analytic tool to analyze further the motion of membrane proteins. The experimental data track the motion of 40 nm gold particles bound to Class I major histocompatibility complex (MHCI) molecules on the membranes of mouse hepatoma cells. Our first novel result is that the tracks are significantly autocorrelated. Because of this, we developed linear autoregressive models to elucidate the autocorrelations. Estimates of the signal to noise ratio for the models show that the autocorrelated part of the motion is significant. Next, we fit the probability distributions of jump sizes with four different models. The first model is a general Weibull distribution that shows that the motion is characterized by an excess of short jumps as compared to a normal random walk. We also fit the data with a chi distribution which provides a natural estimate of the dimension d of the space in which a random walk is occurring. For the biological data, the estimates satisfy 1 < d < 2, implying that particle motion is not confined to a line, but also does not occur freely in the plane. The dimension gives a quantitative estimate of the amount of nanometer scale obstruction met by a diffusing molecule. We introduce a new distribution and use the generalized extreme value distribution to show that the biological data also have an excess of long jumps as compared to normal diffusion. These fits provide novel estimates of the microscopic diffusion constant. Previous MSD analyses of SPT data have provided evidence for nanometer-scale confinement zones that
Increase in dust storm related PM10 concentrations: A time series analysis of 2001-2015.
Krasnov, Helena; Katra, Itzhak; Friger, Michael
2016-06-01
Over the last decades, changes in dust storms characteristics have been observed in different parts of the world. The changing frequency of dust storms in the southeastern Mediterranean has led to growing concern regarding atmospheric PM10 levels. A classic time series additive model was used in order to describe and evaluate the changes in PM10 concentrations during dust storm days in different cities in Israel, which is located at the margins of the global dust belt. The analysis revealed variations in the number of dust events and PM10 concentrations during 2001-2015. A significant increase in PM10 concentrations was identified since 2009 in the arid city of Beer Sheva, southern Israel. Average PM10 concentrations during dust days before 2009 were 406, 312, and 364 μg m(-3) (median 337, 269,302) for Beer Sheva, Rehovot (central Israel) and Modi'in (eastern Israel), respectively. After 2009 the average concentrations in these cities during dust storms were 536, 466, and 428 μg m(-3) (median 382, 335, 338), respectively. Regression analysis revealed associations between PM10 variations and seasonality, wind speed, as well as relative humidity. The trends and periodicity are stronger in the southern part of Israel, where higher PM10 concentrations are found. Since 2009 dust events became more extreme with much higher daily and hourly levels. The findings demonstrate that in the arid area variations of dust storms can be quantified easier through PM10 levels over a relatively short time scale of several years. PMID:26874873
Non-Linear Time Series Analysis of Dissolved Oxygen in Five Diverse Aquatic Environments
NASA Astrophysics Data System (ADS)
Simpson, K. E.; Barton, C. C.; Smigelski, J. R.; Tebbens, S. F.
2008-12-01
Temporal variations in the concentration of Dissolved oxygen (DO) can create catastrophic conditions for organisms that rely on aerobic metabolic processes for survival. Dissolved oxygen (DO) is an aquatic parameter whose concentration is controlled by physical, biological, and chemical processes. The concentration of DO in an aquatic system is important to organisms that rely on aerobic metabolic processes for survival. A power-spectral-density analysis of time series of DO concentration is used to quantify persistence (the degree of internal correlation) over durations of 3 months to 19 years. The interval between data points was either 15 minutes or one hour. The data are from ten different water bodies throughout the United States. Four of these sites are large, slow moving bodies of water including three estuaries: Chesapeake Bay (Virginia), Winyah Bay (North Carolina) and Elkhorn Slough (California); and one reservoir: the Cheney Reservoir in Kansas. The other six sites are small, fast moving water bodies. They included four rivers: Christina River (Delaware), St. Croix River (Maine), Ramapo River (New Jersey), and Passaic River, New Jersey; one stream: Green Pond Brook (New Jersey); and one man-made channel: Reynolds Channel (New York). The analysis quantifies persistence as the power scaling exponent (β), which for all ten water bodies β ranges between 1.2 and 1.6 meaning that the signal is persistent and non-stationary. Rivers and streams, exhibit higher β-values of 1.5 < β<1.6 (greater persistence) than estuaries and lakes, which have β-values of 1.2< β <1.4t.
Determinants of healthcare expenditures in Iran: evidence from a time series analysis
Rezaei, Satar; Fallah, Razieh; Kazemi Karyani, Ali; Daroudi, Rajabali; Zandiyan, Hamed; Hajizadeh, Mohammad
2016-01-01
Background: A dramatic increase in healthcare expenditures is a major health policy concern worldwide. Understanding factors that underlie the growth in healthcare expenditures is essential to assist decision-makers in finding best policies to manage healthcare costs. We aimed to examine the determinants of healthcare spending in Iran over the periods of 1978-2011. Methods: A time series analysis was used to examine the effect of selected socio-economic, demographic and health service input on per capita healthcare expenditures (HCE) in Iran from 1978 to 2011. Data were retrieved from the Central Bank of Iran, Iranian Statistical Center and World Bank. Autoregressive distributed lag approach and error correction method were employed to examine long- and short-run effects of covariates. Results: Our findings indicated that the GDP per capita, degree of urbanization and illiteracy rate increase healthcare expenditures, while physician per 10,000 populations and proportion of population aged≥ 65 years decrease healthcare expenditures. In addition, we found that healthcare spending is a "necessity good" with long- and short-run income (GDP per capita), elasticities of 0.46 (p<0.01) and 0.67 (p = 0.01), respectively. Conclusion: Our analysis identified GDP per capita, illiteracy rate, degree of urbanization and number of physicians as some of the driving forces behind the persistent increase in HCE in Iran. These findings provide important insights into the growth in HCE in Iran. In addition, since we found that health spending is a "necessity good" in Iran, healthcare services should thus be the object of public funding and government intervention PMID:27390683
Analysis of initial drainage network evolution from aerial photography and a DEM time series
NASA Astrophysics Data System (ADS)
Schneider, Anna; Gerke, Horst H.; Maurer, Thomas; Nenov, Rossen; Raab, Thomas
2013-04-01
The evolution of erosion rill or gully networks is a formative process in initial landscape development. Digital representations of drainage networks are often derived from Digital Elevation Models (DEMs) based on morphometric parameters, or mapped in field surveys or from aerial photographs. This study attempted to reconstruct and analyze the first five years of erosion rill network evolution in the 6 ha artificial catchment 'Hühnerwasser', which serves as a real world-laboratory to study patterns and processes of initial ecosystem development. The drainage network was characterized in a twofold approach, based on the analysis of remotely-sensed data. We used high-resolution drone-based aerial photographs to map the actively eroding rill network for four states of development, and a time series of ten Digital Elevation Models to characterize the morphology of the surface. Rill network maps and morphometric parameters were combined to allow for region-specific analyses of morphometry for different parts of the rill network. After a rapid growth of the erosion rill network during the first two years of development, a reduction of the area of actively eroding rills was observed. Region-specific analysis of morphometry indicates an increase in flow accumulation in the central parts of the rill network, which suggests that locally evolving feedback cycles between flow accumulation and erosion affected rill network development, in addition to the effects of precipitation characteristics and the growth of vegetation cover. The combination of drainage network characterization from aerial photography and DEMs could improve analyses of initial drainage network development in experimental studies, as it allows for critical comparisons of flow accumulation patterns and the actual patterns of erosion rills or gullies.
Efficient Transfer Entropy Analysis of Non-Stationary Neural Time Series
Vicente, Raul; Díaz-Pernas, Francisco J.; Wibral, Michael
2014-01-01
Information theory allows us to investigate information processing in neural systems in terms of information transfer, storage and modification. Especially the measure of information transfer, transfer entropy, has seen a dramatic surge of interest in neuroscience. Estimating transfer entropy from two processes requires the observation of multiple realizations of these processes to estimate associated probability density functions. To obtain these necessary observations, available estimators typically assume stationarity of processes to allow pooling of observations over time. This assumption however, is a major obstacle to the application of these estimators in neuroscience as observed processes are often non-stationary. As a solution, Gomez-Herrero and colleagues theoretically showed that the stationarity assumption may be avoided by estimating transfer entropy from an ensemble of realizations. Such an ensemble of realizations is often readily available in neuroscience experiments in the form of experimental trials. Thus, in this work we combine the ensemble method with a recently proposed transfer entropy estimator to make transfer entropy estimation applicable to non-stationary time series. We present an efficient implementation of the approach that is suitable for the increased computational demand of the ensemble method's practical application. In particular, we use a massively parallel implementation for a graphics processing unit to handle the computationally most heavy aspects of the ensemble method for transfer entropy estimation. We test the performance and robustness of our implementation on data from numerical simulations of stochastic processes. We also demonstrate the applicability of the ensemble method to magnetoencephalographic data. While we mainly evaluate the proposed method for neuroscience data, we expect it to be applicable in a variety of fields that are concerned with the analysis of information transfer in complex biological, social, and
Analysis of time series of cattle rabies cases in Minas Gerais, Brazil, 2006-2013.
Lopes, Edna; Sáfadi, Thelma; Da Rocha, Christiane Maria Barcellos Magalhaes; Cardoso, Denis Lucio
2015-04-01
Vampire bats are potential transmitters of rabies in rural areas. Cattle rabies is relevant in the state of Minas Gerais due to the increasing cattle herds and geographical features of the area, which are favorable to bat populations. This study evaluated the occurrence of rabies in state cattle by analyzing the time series of monthly values, 2006-2012, describing some aspects of the areas and species affected. The study also pointed out the disease prediction for January-December 2013. We used monthly data of cases reported to the Continental Epidemiological Surveillance System (SIVCONT) of the Ministry of Agriculture, Livestock, and Supply (MAPA), January 2006-March 2013. We also collected data on municipalities and other animal species affected by rabies for a descriptive analysis of the disease. The results indicate that cattle rabies is endemic in the State, with different intensities in different regions. The variables frequency of notifications and bat shelters had a positive and regular correlation (P = 0.035; r = 0.567) between them. With respect to data series, there was a fluctuation of the number of cases (5 to 29 cases per month) over 2006 and 2013, without trend or seasonality, although there would visually appear to be a downward trend. The results also suggest that the forecasting method is suitable for predicting future cases. Bovine species had the highest number of reporting, with 1007 cases (88.88 %), followed by equine species with 112 (9.89 %). The information provided by this study may help understand disease occurrence and find the most effective measures for rabies control in endemic areas. PMID:25698529
TIME SERIES ANALYSIS OF REMOTELY-SENSED TIR EMISSION: linking anomalies to physical processes
NASA Astrophysics Data System (ADS)
Pavlidou, E.; van der Meijde, M.; Hecker, C.; van der Werff, H.; Ettema, J.
2013-12-01
In the last 15 years, remote sensing has been evaluated for detecting thermal anomalies as precursor to earthquakes. Important issues that need yet to be tackled include definition of: (a) thermal anomaly, taking into account weather conditions, observation settings and ';natural' variability caused by background sources (b) the length of observations required for this purpose; and (c) the location of detected anomalies, which should be physically related to the tectonic activity. To determine whether thermal anomalies are statistical noise, mere meteorological conditions, or actual earthquake-related phenomena, we apply a novel approach. We use brightness temperature (top-of-atmosphere) data from thermal infrared imagery acquired at a hypertemporal (sub-hourly) interval, from geostationary weather satellites over multiple years. The length of the time series allows for analysis of meteorological effects (diurnal, seasonal or annual trends) and background variability, through the application of a combined spatial and temporal filter to distinguish extreme occurrences from trends. The definition of potential anomalies is based on statistical techniques, taking into account published (geo)physical characteristics of earthquake related thermal anomalies. We use synthetic data to test the performance of the proposed detection method and track potential factors affecting the results. Subsequently, we apply the method on original data from Iran and Turkey, in quiescent and earthquake-struck periods alike. We present our findings with main focus to assess resulting anomalies in relation to physical processes thereby considering: (a) meteorological effects, (b) the geographical, geological and environmental settings, and (c) physically realistic distances and potential physical relations with the activity of causative faults.
Hybrid analysis for indicating patients with breast cancer using temperature time series.
Silva, Lincoln F; Santos, Alair Augusto S M D; Bravo, Renato S; Silva, Aristófanes C; Muchaluat-Saade, Débora C; Conci, Aura
2016-07-01
Breast cancer is the most common cancer among women worldwide. Diagnosis and treatment in early stages increase cure chances. The temperature of cancerous tissue is generally higher than that of healthy surrounding tissues, making thermography an option to be considered in screening strategies of this cancer type. This paper proposes a hybrid methodology for analyzing dynamic infrared thermography in order to indicate patients with risk of breast cancer, using unsupervised and supervised machine learning techniques, which characterizes the methodology as hybrid. The dynamic infrared thermography monitors or quantitatively measures temperature changes on the examined surface, after a thermal stress. In the dynamic infrared thermography execution, a sequence of breast thermograms is generated. In the proposed methodology, this sequence is processed and analyzed by several techniques. First, the region of the breasts is segmented and the thermograms of the sequence are registered. Then, temperature time series are built and the k-means algorithm is applied on these series using various values of k. Clustering formed by k-means algorithm, for each k value, is evaluated using clustering validation indices, generating values treated as features in the classification model construction step. A data mining tool was used to solve the combined algorithm selection and hyperparameter optimization (CASH) problem in classification tasks. Besides the classification algorithm recommended by the data mining tool, classifiers based on Bayesian networks, neural networks, decision rules and decision tree were executed on the data set used for evaluation. Test results support that the proposed analysis methodology is able to indicate patients with breast cancer. Among 39 tested classification algorithms, K-Star and Bayes Net presented 100% classification accuracy. Furthermore, among the Bayes Net, multi-layer perceptron, decision table and random forest classification algorithms, an
DynPeak: An Algorithm for Pulse Detection and Frequency Analysis in Hormonal Time Series
Vidal, Alexandre; Zhang, Qinghua; Médigue, Claire; Fabre, Stéphane; Clément, Frédérique
2012-01-01
The endocrine control of the reproductive function is often studied from the analysis of luteinizing hormone (LH) pulsatile secretion by the pituitary gland. Whereas measurements in the cavernous sinus cumulate anatomical and technical difficulties, LH levels can be easily assessed from jugular blood. However, plasma levels result from a convolution process due to clearance effects when LH enters the general circulation. Simultaneous measurements comparing LH levels in the cavernous sinus and jugular blood have revealed clear differences in the pulse shape, the amplitude and the baseline. Besides, experimental sampling occurs at a relatively low frequency (typically every 10 min) with respect to LH highest frequency release (one pulse per hour) and the resulting LH measurements are noised by both experimental and assay errors. As a result, the pattern of plasma LH may be not so clearly pulsatile. Yet, reliable information on the InterPulse Intervals (IPI) is a prerequisite to study precisely the steroid feedback exerted on the pituitary level. Hence, there is a real need for robust IPI detection algorithms. In this article, we present an algorithm for the monitoring of LH pulse frequency, basing ourselves both on the available endocrinological knowledge on LH pulse (shape and duration with respect to the frequency regime) and synthetic LH data generated by a simple model. We make use of synthetic data to make clear some basic notions underlying our algorithmic choices. We focus on explaining how the process of sampling affects drastically the original pattern of secretion, and especially the amplitude of the detectable pulses. We then describe the algorithm in details and perform it on different sets of both synthetic and experimental LH time series. We further comment on how to diagnose possible outliers from the series of IPIs which is the main output of the algorithm. PMID:22802933
Reduction of maternal mortality due to preeclampsia in Colombia-an interrupted time-series analysis
Herrera-Medina, Rodolfo; Herrera-Escobar, Juan Pablo; Nieto-Díaz, Aníbal
2014-01-01
Introduction: Preeclampsia is the most important cause of maternal mortality in developing countries. A comprehensive prenatal care program including bio-psychosocial components was developed and introduced at a national level in Colombia. We report on the trends in maternal mortality rates and their related causes before and after implementation of this program. Methods: General and specific maternal mortality rates were monitored for nine years (1998-2006). An interrupted time-series analysis was performed with monthly data on cases of maternal mortality that compared trends and changes in national mortality rates and the impact of these changes attributable to the introduction of a bio-psychosocial model. Multivariate analyses were performed to evaluate correlations between the interventions. Results: Five years after (2002 - 2006) its introduction the general maternal mortality rate was significantly reduced to 23% (OR=0.77, CI 95% 0.71-0.82).The implementation of BPSM also reduced the incidence of preeclampsia in 22% (OR= 0.78, CI 95% 0.67-0.88), as also the labor complications by hemorrhage in 25% (OR=0.75, CI 95% 0.59-0.90) associated with the implementation of red code. The other causes of maternal mortality did not reveal significant changes. Biomedical, nutritional, psychosocial assessments, and other individual interventions in prenatal care were not correlated to maternal mortality (p= 0.112); however, together as a model we observed a significant association (p= 0.042). Conclusions: General maternal mortality was reduced after the implementation of a comprehensive national prenatal care program. Is important the evaluation of this program in others populations. PMID:24970956
Trend analysis of long-term temperature time series in the Greater Toronto Area (GTA)
NASA Astrophysics Data System (ADS)
Mohsin, Tanzina; Gough, William A.
2010-08-01
As the majority of the world’s population is living in urban environments, there is growing interest in studying local urban climates. In this paper, for the first time, the long-term trends (31-162 years) of temperature change have been analyzed for the Greater Toronto Area (GTA). Annual and seasonal time series for a number of urban, suburban, and rural weather stations are considered. Non-parametric statistical techniques such as Mann-Kendall test and Theil-Sen slope estimation are used primarily for the assessing of the significance and detection of trends, and the sequential Mann test is used to detect any abrupt climate change. Statistically significant trends for annual mean and minimum temperatures are detected for almost all stations in the GTA. Winter is found to be the most coherent season contributing substantially to the increase in annual minimum temperature. The analyses of the abrupt changes in temperature suggest that the beginning of the increasing trend in Toronto started after the 1920s and then continued to increase to the 1960s. For all stations, there is a significant increase of annual and seasonal (particularly winter) temperatures after the 1980s. In terms of the linkage between urbanization and spatiotemporal thermal patterns, significant linear trends in annual mean and minimum temperature are detected for the period of 1878-1978 for the urban station, Toronto, while for the rural counterparts, the trends are not significant. Also, for all stations in the GTA that are situated in all directions except south of Toronto, substantial temperature change is detected for the periods of 1970-2000 and 1989-2000. It is concluded that the urbanization in the GTA has significantly contributed to the increase of the annual mean temperatures during the past three decades. In addition to urbanization, the influence of local climate, topography, and larger scale warming are incorporated in the analysis of the trends.
Multi Band Insar Analysis of Subsidence Development Based on the Long Period Time Series
NASA Astrophysics Data System (ADS)
Çomut, F. C.; Ustun, A.; Lazecky, M.; Aref, M. M.
2015-12-01
The SAR Interferometry (InSAR) application has shown great potential in monitoring of land terrain changes and in detection of land deformations such as subsidence. Longer time analysis can lead to understand longer trends and changes. Using different bands of SAR satellite (C- from ERS 1-2 and Envisat, L- from ALOS) over the study area, we achieve knowledge of movements in long-term and evaluation of its dynamic changes within observed period of time. Results from InSAR processing fit with the position changes in vertical direction based on GPS network established over the basin as an effective geodetic network. Time series (StaMPS PS+SB) of several points over Çumra County in eastern part of Konya City show a general trend of the deformation that is expected to be approximately between -13 to -17 mm/year. Northern part of Karaman is affected by faster subsidence, borders of the subsidence trough were identified from Envisat. Presenting InSAR results together with GIS information about locations and time of occurrence of sudden subsidence, urban/industrial growth in time and climate changes helps in better understanding of the situation. This way, the impact of natural and man-made changes will be shown for urban planning thanks to InSAR and GIS comparisons with hydrogeological modeling. In this study we present results of differential and multitemporal InSAR series using different bands and GIS conjunction associated with seasonal and temporal groundwater level changes in Konya Closed Basin.
A time-series analysis of mortality and air temperature in Greater Beirut.
El-Zein, Abbas; Tewtel-Salem, Mylene; Nehme, Gebran
2004-09-01
The literature on the association between health and weather in the temperate to semi-arid cities of the Eastern Mediterranean is scarce. The quantification of the relationship between temperature and daily mortality can be useful for developing policy interventions such as heat-warning systems. A time-series analysis of total daily mortality and weather data for the city of Beirut was carried out. The study covered the period between 1997 and 1999. Poisson auto-regressive models were constructed, with mean daily temperature and mean daily humidity as explanatory variables. Delayed effects, up to 2 weeks, were accounted for. The regression models were used next to assess the effect of an average increase in temperature on yearly mortality. The association between temperature and mortality was found to be significant. A relatively high minimum-mortality temperature (TMM) of 27.5 degrees C was calculated. A 1 degrees C rise in temperature yielded a 12.3% increase (95% confidence interval: 5.7-19.4%) and 2.9% decrease (95% confidence interval: 2-3.7%) in mortality, above and below TMM, respectively. Lag temperature variables were found to be significant below TMM but not above it. Where the temperature change was less than 0.5 degrees C, annual above-TMM losses were offset by below-TMM gains, within a 95% confidence interval. TMM for Beirut fell within the range usually associated with warm climates. However, the mild below-TMM and steep above-TMM slopes were more typical of cities with temperate to cold climates. Our findings suggest that heat-related mortality at moderately high temperatures can be a significant public health issue in countries with warm climates. Moreover, at the projected climate change over the next 50 years, heat-related losses are unlikely to be offset by cold-related gains. PMID:15325159
Efficient transfer entropy analysis of non-stationary neural time series.
Wollstadt, Patricia; Martínez-Zarzuela, Mario; Vicente, Raul; Díaz-Pernas, Francisco J; Wibral, Michael
2014-01-01
Information theory allows us to investigate information processing in neural systems in terms of information transfer, storage and modification. Especially the measure of information transfer, transfer entropy, has seen a dramatic surge of interest in neuroscience. Estimating transfer entropy from two processes requires the observation of multiple realizations of these processes to estimate associated probability density functions. To obtain these necessary observations, available estimators typically assume stationarity of processes to allow pooling of observations over time. This assumption however, is a major obstacle to the application of these estimators in neuroscience as observed processes are often non-stationary. As a solution, Gomez-Herrero and colleagues theoretically showed that the stationarity assumption may be avoided by estimating transfer entropy from an ensemble of realizations. Such an ensemble of realizations is often readily available in neuroscience experiments in the form of experimental trials. Thus, in this work we combine the ensemble method with a recently proposed transfer entropy estimator to make transfer entropy estimation applicable to non-stationary time series. We present an efficient implementation of the approach that is suitable for the increased computational demand of the ensemble method's practical application. In particular, we use a massively parallel implementation for a graphics processing unit to handle the computationally most heavy aspects of the ensemble method for transfer entropy estimation. We test the performance and robustness of our implementation on data from numerical simulations of stochastic processes. We also demonstrate the applicability of the ensemble method to magnetoencephalographic data. While we mainly evaluate the proposed method for neuroscience data, we expect it to be applicable in a variety of fields that are concerned with the analysis of information transfer in complex biological, social, and
Ground Deformation Mapping of Houston-Galveston, Texas Using InSAR Time-Series Analysis
NASA Astrophysics Data System (ADS)
QU, F.; Lu, Z.; Bawden, G. W.; Kim, J. W.
2014-12-01
Houston-Galveston region in Texas has been subsiding due to the combined effects of groundwater withdrawal, hydrocarbon extraction, soil compaction, and active faulting. This human- and partially nature-induced ground deformation has gradually threatened the stability of urban infrastructure and caused the loss of wetland habitat along the Gulf of Mexico. Interferometric synthetic aperture radar (InSAR) exploiting multiple SAR images has the capability of obtaining ground motions in high spatial resolution over large coverage. In this study, ERS-1/2 (1993-2000), ENVISAT (2004-2010), and ALOS (2007-2011) datasets are used to unravel the characteristics of ground deformation from 1993 to 2011 over the Houston-Galveston area. The persistent scatterer InSAR (PSInSAR) time-series analysis technique is employed to estimate the spatial and temporal variations of ground motions during 20 years. The ERS-1/2 PSInSAR products have measured subsidence (up to 5 cm/yr) in the northwest Houston area as well as a slight uplift (1 cm/yr) in the southeast region from 1993 to 2000. The subsidence rate (up to 2 cm/yr) between 2004 and 2011 has been obtained from ENVISAT and ALOS data. Our results indicate that the pattern of ground deformation was nearly concentric around the location of intense groundwater withdrawal and the subsiding area has been shrinking and migrating toward the northeast after 2000. In addition, an approximately 2 cm of differential subsidence across faults are observed. Presence of faults can induce localized surface displacements, aggravate localized subsidence, discontinue the integrity of ground water flow, and limit the horizontal spread of subsidence funnels. Finally, our long-term measurement of ground deformation has also been validated by GPS observations in study area.
Time-series analysis of mortality effects from airborne particulate matter size fractions in Beijing
NASA Astrophysics Data System (ADS)
Li, Pei; Xin, Jinyuan; Wang, Yuesi; Wang, Shigong; Shang, Kezheng; Liu, Zirui; Li, Guoxing; Pan, Xiaochuan; Wei, Linbo; Wang, Mingzhen
2013-12-01
Evidence concerning the health risk of fine and coarse particles is limited in developing Asian countries. The modifying effect between particles and temperature and season also remains unclear. Our study is one of the first to investigate the acute effect of particles size fractions, modifying effects and interannual variations of relative risk in a developing megacity where particulate levels are extraordinarily high compared to other Asian cities. After controlling for potential confounding, the results of a time-series analysis during the period 2005-2009 show that a 10 μg m-3 increase in PM2.5 levels is associated with a 0.65% (95% CI: 0.29-0.80%), 0.63% (95% CI: 0.25-0.83%), and 1.38% (95% CI: 0.51-1.71%) increase in non-accidental mortality, respiratory mortality, and circulatory mortality, respectively, while a 10 μg m-3 increase in PM10 is similarly associated with increases of 0.15% (95% CI: 0.04-0.22%), 0.08% (95% CI: 0.01-0.18%), and 0.44% (95% CI: 0.12-0.63%). We did not find a significant effect of PM2.5-10 on daily mortality outcomes. Our analyses conclude that temperature and particulates, exposures to both of which are expected to increase with climate change, might act together to worsen human health in Beijing, especially in the cool seasons. The level of the estimated percentage increase assume an escalating tendency during the study period, in addition to having a low value in 2008, and after the Olympic Games, the values increased significantly as the temporary atmospheric pollution control measures were terminated mostly.
NASA Astrophysics Data System (ADS)
Tirabassi, Giulio; Masoller, Cristina
2016-07-01
Many natural systems can be represented by complex networks of dynamical units with modular structure in the form of communities of densely interconnected nodes. Unraveling this community structure from observed data requires the development of appropriate tools, particularly when the nodes are embedded in a regular space grid and the datasets are short and noisy. Here we propose two methods to identify communities, and validate them with the analysis of climate datasets recorded at a regular grid of geographical locations covering the Earth surface. By identifying mutual lags among time-series recorded at different grid points, and by applying symbolic time-series analysis, we are able to extract meaningful regional communities, which can be interpreted in terms of large-scale climate phenomena. The methods proposed here are valuable tools for the study of other systems represented by networks of dynamical units, allowing the identification of communities, through time-series analysis of the observed output signals.
Tirabassi, Giulio; Masoller, Cristina
2016-01-01
Many natural systems can be represented by complex networks of dynamical units with modular structure in the form of communities of densely interconnected nodes. Unraveling this community structure from observed data requires the development of appropriate tools, particularly when the nodes are embedded in a regular space grid and the datasets are short and noisy. Here we propose two methods to identify communities, and validate them with the analysis of climate datasets recorded at a regular grid of geographical locations covering the Earth surface. By identifying mutual lags among time-series recorded at different grid points, and by applying symbolic time-series analysis, we are able to extract meaningful regional communities, which can be interpreted in terms of large-scale climate phenomena. The methods proposed here are valuable tools for the study of other systems represented by networks of dynamical units, allowing the identification of communities, through time-series analysis of the observed output signals. PMID:27406342
Tirabassi, Giulio; Masoller, Cristina
2016-01-01
Many natural systems can be represented by complex networks of dynamical units with modular structure in the form of communities of densely interconnected nodes. Unraveling this community structure from observed data requires the development of appropriate tools, particularly when the nodes are embedded in a regular space grid and the datasets are short and noisy. Here we propose two methods to identify communities, and validate them with the analysis of climate datasets recorded at a regular grid of geographical locations covering the Earth surface. By identifying mutual lags among time-series recorded at different grid points, and by applying symbolic time-series analysis, we are able to extract meaningful regional communities, which can be interpreted in terms of large-scale climate phenomena. The methods proposed here are valuable tools for the study of other systems represented by networks of dynamical units, allowing the identification of communities, through time-series analysis of the observed output signals. PMID:27406342
APPLICATION OF TIME-SERIES INTERVENTION ANALYSIS TO FISH VENTILATORY RESPONSE DATA
The development of environmental monitors based on ventilation behavior of fishes has produced large masses of data for which no standard analytical procedures exist. This report demonstrates the application of time-series models to this type of data. It also demonstrates the use...
Fractal analysis of the short time series in a visibility graph method
NASA Astrophysics Data System (ADS)
Li, Ruixue; Wang, Jiang; Yu, Haitao; Deng, Bin; Wei, Xile; Chen, Yingyuan
2016-05-01
The aim of this study is to evaluate the performance of the visibility graph (VG) method on short fractal time series. In this paper, the time series of Fractional Brownian motions (fBm), characterized by different Hurst exponent H, are simulated and then mapped into a scale-free visibility graph, of which the degree distributions show the power-law form. The maximum likelihood estimation (MLE) is applied to estimate power-law indexes of degree distribution, and in this progress, the Kolmogorov-Smirnov (KS) statistic is used to test the performance of estimation of power-law index, aiming to avoid the influence of droop head and heavy tail in degree distribution. As a result, we find that the MLE gives an optimal estimation of power-law index when KS statistic reaches its first local minimum. Based on the results from KS statistic, the relationship between the power-law index and the Hurst exponent is reexamined and then amended to meet short time series. Thus, a method combining VG, MLE and KS statistics is proposed to estimate Hurst exponents from short time series. Lastly, this paper also offers an exemplification to verify the effectiveness of the combined method. In addition, the corresponding results show that the VG can provide a reliable estimation of Hurst exponents.
Kolmogorov Complexity Spectrum for Use in Analysis of Uv-B Radiation Time Series
NASA Astrophysics Data System (ADS)
Mihailović, Dragutin T.; Malinović-Milićević, Slavica; Arsenić, Ilija; Drešković, Nusret; Bukosa, Beata
2013-10-01
In this paper, we have used the Kolmogorov complexity and sample entropy measures to estimate the complexity of the UV-B radiation time series in the Vojvodina region (Serbia) for the period 1990-2007. We have defined the Kolmogorov complexity spectrum and have introduced the Kolmogorov complexity spectrum highest value (KCH). We have established the UV-B radiation time series on the basis of their daily sum (dose) for seven representative places in this region using: (i) measured data, (ii) data calculated via a derived empirical formula and (iii) data obtained by a parametric UV radiation model. We have calculated the Kolmogorov complexity (KC) based on the Lempel-Ziv algorithm (LZA), KCH and sample entropy (SE) values for each time series. We have divided the period 1990-2007 into two subintervals: (i) 1990-1998 and (ii) 1999-2007 and calculated the KC, KCH and SE values for the various time series in these subintervals. It is found that during the period 1999-2007, there is a decrease in the KC, KCH and SE, compared to the period 1990-1998. This complexity loss may be attributed to (i) the increased human intervention in the post civil war period causing increase of the air pollution and (ii) the increased cloudiness due to climate changes.
Hatch, C.E.; Fisher, A.T.; Revenaugh, J.S.; Constantz, J.; Ruehl, C.
2006-01-01
We present a method for determining streambed seepage rates using time series thermal data. The new method is based on quantifying changes in phase and amplitude of temperature variations between pairs of subsurface sensors. For a reasonable range of streambed thermal properties and sensor spacings the time series method should allow reliable estimation of seepage rates for a range of at least ??10 m d-1 (??1.2 ?? 10-2 m s-1), with amplitude variations being most sensitive at low flow rates and phase variations retaining sensitivity out to much higher rates. Compared to forward modeling, the new method requires less observational data and less setup and data handling and is faster, particularly when interpreting many long data sets. The time series method is insensitive to streambed scour and sedimentation, which allows for application under a wide range of flow conditions and allows time series estimation of variable streambed hydraulic conductivity. This new approach should facilitate wider use of thermal methods and improve understanding of the complex spatial and temporal dynamics of surface water-groundwater interactions. Copyright 2006 by the American Geophysical Union.
The application of artificial neural networks to magnetotelluric time-series analysis
NASA Astrophysics Data System (ADS)
Manoj, C.; Nagarajan, Nandini
2003-05-01
Magnetotelluric (MT) signals are often contaminated with noise from natural or man-made processes that may not fit a normal distribution or are highly correlated. This may lead to serious errors in computed MT transfer functions and result in erroneous interpretation. A substantial improvement is possible when the time-series are presented as clean as possible for further processing. Cleaning of MT time-series is often done by manual editing. Editing of magnetotelluric time-series is subjective in nature and time consuming. Automation of such a process is difficult to achieve by statistical methods. Artificial neural networks (ANNs) are widely used to automate processes that require human intelligence. The objective here is to automate MT long-period time-series editing using ANN. A three-layer feed-forward artificial neural network (FANN) was adopted for the problem. As ANN-based techniques are computationally intensive, a novel approach was made, which involves editing of five simultaneously measured MT time-series that have been subdivided into stacks (a stack=5 × 256 data points). Neural network training was done at two levels. Signal and noise patterns of individual channels were taught first. Five channel parameters along with interchannel correlation and amplitude ratios formed the input for a final network, which predicts the quality of a stack. A large database (5000 traces for pattern training and 900 vectors for interchannel training) was prepared to train the network. There were two error parameters to minimize while training: training error and testing error. Training was stopped when both errors were below an acceptable level. The sensitivity of the neural network to the signal-to-noise ratio and the relative significance of its inputs were tested to ensure that the training was correct. MT time-series from four stations with varying degrees of noise contamination were used to demonstrate the application of the network. The application brought out
Comprehensive time series analysis of the transiting extrasolar planet WASP-33b
NASA Astrophysics Data System (ADS)
Kovács, G.; Kovács, T.; Hartman, J. D.; Bakos, G. Á.; Bieryla, A.; Latham, D.; Noyes, R. W.; Regály, Zs.; Esquerdo, G. A.
2013-05-01
://www.aanda.orgPhotometric time series and lightcurves are only available at the CDS via anonymous ftp to cdsarc.u-strasbg.fr (ftp://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/553/A44
Time series analysis and feature extraction techniques for structural health monitoring applications
NASA Astrophysics Data System (ADS)
Overbey, Lucas A.
Recently, advances in sensing and sensing methodologies have led to the deployment of multiple sensor arrays on structures for structural health monitoring (SHM) applications. Appropriate feature extraction, detection, and classification methods based on measurements obtained from these sensor networks are vital to the SHM paradigm. This dissertation focuses on a multi-input/multi-output approach to novel data processing procedures to produce detailed information about the integrity of a structure in near real-time. The studies employ nonlinear time series analysis techniques to extract three different types of features for damage diagnostics: namely, nonlinear prediction error, transfer entropy, and the generalized interdependence. These features form reliable measures of generalized correlations between multiple measurements to capture aspects of the dynamics related to the presence of damage. Several analyses are conducted on each of these features. Specifically, variations of nonlinear prediction error are introduced, analyzed, and validated, including the use of a stochastic excitation to augment generality, introduction of local state-space models for sensitivity enhancement, and the employment of comparisons between multiple measurements for localization capability. A modification and enhancement to transfer entropy is created and validated for improved sensitivity. In addition, a thorough analysis of the effects of variability to transfer entropy estimation is made. The generalized interdependence is introduced into the literature and validated as an effective measure of damage presence, extent, and location. These features are validated on a multi-degree-of-freedom dynamic oscillator and several different frame experiments. The evaluated features are then fed into four different classification schemes to obtain a concurrent set of outputs that categorize the integrity of the structure, e.g. the presence, extent, location, and type of damage, taking
ERIC Educational Resources Information Center
Doerann-George, Judith
The Integrated Moving Average (IMA) model of time series, and the analysis of intervention effects based on it, assume random shocks which are normally distributed. To determine the robustness of the analysis to violations of this assumption, empirical sampling methods were employed. Samples were generated from three populations; normal,…
Pancheliuga, V A; Pancheliuga, M S
2013-01-01
In the present work a methodological background for the histogram method of time series analysis is developed. Connection between shapes of smoothed histograms constructed on the basis of short segments of time series of fluctuations and the fractal dimension of the segments is studied. It is shown that the fractal dimension possesses all main properties of the histogram method. Based on it a further development of fractal dimension determination algorithm is proposed. This algorithm allows more precision determination of the fractal dimension by using the "all possible combination" method. The application of the method to noise-like time series analysis leads to results, which could be obtained earlier only by means of the histogram method based on human expert comparisons of histograms shapes. PMID:23755565
NASA Astrophysics Data System (ADS)
Telesca, Luciano; Lovallo, Michele; Shaban, Amin; Darwich, Talal; Amacha, Nabil
2013-09-01
In this study, the time dynamics of water flow from Anjar Spring was investigated, which is one of the major issuing springs in the central part of Lebanon. Likewise, many water sources in Lebanon, this spring has no continuous records for the discharge, and this would prevent the application of standard time series analysis tools. Furthermore, the highly nonstationary character of the series implies that suited methodologies can be employed to get insight into its dynamical features. Therefore, the Singular Spectrum Analysis (SSA) and Fisher-Shannon (FS) method, which are useful methods to disclose dynamical features in noisy nonstationary time series with gaps, are jointly applied to analyze the Anjar Spring water flow series. The SSA revealed that the series can be considered as the superposition of meteo-climatic periodic components, low-frequency trend and noise-like high-frequency fluctuations. The FS method allowed to extract and to identify among all the SSA reconstructed components the long-term trend of the series. The long-term trend is characterized by higher Fisher Information Measure (FIM) and lower Shannon entropy, and thus, represents the main informative component of the whole series. Generally water discharge time series presents very complex time structure, therefore the joint application of the SSA and the FS method would be very useful in disclosing the main informative part of such kind of data series in the view of existing climatic variability and/or anthropogenic challenges.
Changes in Youth Smoking, 1976–2002: A Time-Series Analysis
Pampel, Fred C.; Aguilar, Jade
2009-01-01
During the past several decades, smoking prevalence among youth has fluctuated in puzzling and unexpected ways. To help understand these changes, this study tests seven explanations: (a) compositional changes, (b) sample selection, (c) adult smoking, (d) social strain, (e) cigarette prices, (f) tobacco advertising, and (g) other drug use. Figures on smoking prevalence come from the Monitoring the Future (MTF) Surveys from 1976–2002, whereas figures on aggregate determinants for the same time period come from government publications. Graphs of the time-series trends to determine temporal correspondence and time-series regression models to test for statistical influence reveal two variables that have expected effects. Increases in cigarette prices reduce smoking, particularly in the most recent years, and higher marijuana initiation (or use) is associated with greater smoking during most of the time period. However, much of the change in youth smoking, particularly the most recent rise and fall, remains unexplained. PMID:19652692
Time-series analysis of nonstationary plasma fluctuations using wavelet transforms
Santoso, S.; Powers, E.J.; Bengtson, R.D.; Ouroua, A.
1997-01-01
A wavelet or time-scale approach to analyzing a single time series and two time series, in which the fluctuating quantities are statistically nonstationary, is presented. The time scale and scale {open_quotes}power spectra{close_quotes} are introduced and utilized to analyze transient potential fluctuations measured at the core of sawtoothing TEXT-U plasmas. The results show features that have not been previously observed using any Fourier techniques. In addition, the linear time-scale {open_quotes}coherence spectrum{close_quotes} is developed to quantify the degree of linear relationship between two nonstationary fluctuating quantities in the time-scale domain. Such a spectrum is also useful in tracking the time-varying phase difference. A numerical example is provided to demonstrate the efficacy of the time-scale spectra. {copyright} {ital 1997 American Institute of Physics.}
The study of coastal groundwater depth and salinity variation using time-series analysis
Tularam, G.A. . E-mail: a.tularam@griffith.edu.au; Keeler, H.P. . E-mail: p.keeler@ms.unimelb.edu.au
2006-10-15
A time-series approach is applied to study and model tidal intrusion into coastal aquifers. The authors examine the effect of tidal behaviour on groundwater level and salinity intrusion for the coastal Brisbane region using auto-correlation and spectral analyses. The results show a close relationship between tidal behaviour, groundwater depth and salinity levels for the Brisbane coast. The known effect can be quantified and incorporated into new models in order to more accurately map salinity intrusion into coastal groundwater table.
Rivera, Diego; Lillo, Mario; Granda, Stalin
2014-12-01
The concept of time stability has been widely used in the design and assessment of monitoring networks of soil moisture, as well as in hydrological studies, because it is as a technique that allows identifying of particular locations having the property of representing mean values of soil moisture in the field. In this work, we assess the effect of time stability calculations as new information is added and how time stability calculations are affected at shorter periods, subsampled from the original time series, containing different amounts of precipitation. In doing so, we defined two experiments to explore the time stability behavior. The first experiment sequentially adds new data to the previous time series to investigate the long-term influence of new data in the results. The second experiment applies a windowing approach, taking sequential subsamples from the entire time series to investigate the influence of short-term changes associated with the precipitation in each window. Our results from an operating network (seven monitoring points equipped with four sensors each in a 2-ha blueberry field) show that as information is added to the time series, there are changes in the location of the most stable point (MSP), and that taking the moving 21-day windows, it is clear that most of the variability of soil water content changes is associated with both the amount and intensity of rainfall. The changes of the MSP over each window depend on the amount of water entering the soil and the previous state of the soil water content. For our case study, the upper strata are proxies for hourly to daily changes in soil water content, while the deeper strata are proxies for medium-range stored water. Thus, different locations and depths are representative of processes at different time scales. This situation must be taken into account when water management depends on soil water content values from fixed locations. PMID:25249045
An analysis of multifractal characteristics of API time series in Nanjing, China
NASA Astrophysics Data System (ADS)
Shen, Chen-hua; Huang, Yi; Yan, Ya-ni
2016-06-01
This paper describes multifractal characteristics of daily air pollution index (API) records in Nanjing from 2001 to 2012. The entire daily API time series is first divided into 12 parts that serve as research objects, and the generalized Hurst exponent is calculated for each series. And then, the multifractal sources are analyzed and singularity spectra are shown. Next, based on a singularity spectrum, the multifractal-characteristics parameters (maximum exponent α0, spectrum width Δ α, and asymmetry Δ αas) are introduced. The results show that the fractality of daily API for each year is multifractal. The multifractal sources originate from both a broad probability density function and different long-range correlations with small and large fluctuations. The strength of the distribution multifractality is stronger than that of the correlation multifractality. The variation in the structure of API time series with increasing years is mainly related to long-range correlations. The structure of API time series in some years is richer. These findings can provide a scientific basis for further probing into the complexity of API.
Correlation analysis for long time series by robustly estimated autoregressive stochastic processes
NASA Astrophysics Data System (ADS)
Schuh, Wolf-Dieter; Brockmann, Jan-Martin; Kargoll, Boris
2015-04-01
Modern sensors and satellite missions deliver huge data sets and long time series of observations. These data sets have to be handled with care because of changing correlations, conspicuous data and possible outliers. Tailored concepts for data selection and robust techniques to estimate the correlation characteristics allow for a better/optimal exploitation of the information of these measurements. In this presentation we give an overview of standard techniques for estimating correlations occurring in long time series in the time domain as well as in the frequency domain. We discuss the pros and cons especially with the focus on the intensified occurrence of conspicuous data and outliers. We present a concept to classify the measurements and isolate conspicuous data. We propose to describe the varying correlation behavior of the measurement series by an autoregressive stochastic process and give some hints how to construct adaptive filters to decorrelate the measurement series and to handle the huge covariance matrices. As study object we use time series from gravity gradient data collected during the GOCE low orbit operation campaign (LOOC). Due to the low orbit these data from 13-Jun-2014 to 21-Oct-2014 have more or less the same potential to recover the Earth gravity field with the same accuracy than all the data from the rest of the entire mission. Therefore these data are extraordinarily valuable but hard to handle, because of conspicuous data due to maneuvers during the orbit lowering phases, overall increase in drag, saturation of ion thrusters and other (currently) unexplained effects.
Nease, Brian R. Ueki, Taro
2009-12-10
A time series approach has been applied to the nuclear fission source distribution generated by Monte Carlo (MC) particle transport in order to calculate the non-fundamental mode eigenvalues of the system. The novel aspect is the combination of the general technical principle of projection pursuit for multivariate data with the neutron multiplication eigenvalue problem in the nuclear engineering discipline. Proof is thoroughly provided that the stationary MC process is linear to first order approximation and that it transforms into one-dimensional autoregressive processes of order one (AR(1)) via the automated choice of projection vectors. The autocorrelation coefficient of the resulting AR(1) process corresponds to the ratio of the desired mode eigenvalue to the fundamental mode eigenvalue. All modern MC codes for nuclear criticality calculate the fundamental mode eigenvalue, so the desired mode eigenvalue can be easily determined. This time series approach was tested for a variety of problems including multi-dimensional ones. Numerical results show that the time series approach has strong potential for three dimensional whole reactor core. The eigenvalue ratio can be updated in an on-the-fly manner without storing the nuclear fission source distributions at all previous iteration cycles for the mean subtraction. Lastly, the effects of degenerate eigenvalues are investigated and solutions are provided.
NASA Technical Reports Server (NTRS)
Menenti, M.; Azzali, S.; Verhoef, W.; Van Swol, R.
1993-01-01
Examples are presented of applications of a fast Fourier transform algorithm to analyze time series of images of Normalized Difference Vegetation Index values. The results obtained for a case study on Zambia indicated that differences in vegetation development among map units of an existing agroclimatic map were not significant, while reliable differences were observed among the map units obtained using the Fourier analysis.
NASA Astrophysics Data System (ADS)
Stanley, R. H. R.; Jenkins, W. J.; Doney, S. C.; Lott, D. E., III
2015-09-01
Significant rates of primary production occur in the oligotrophic ocean, without any measurable nutrients present in the mixed layer, fueling a scientific paradox that has lasted for decades. Here, we provide a new determination of the annual mean physical supply of nitrate to the euphotic zone in the western subtropical North Atlantic. We combine a 3-year time series of measurements of tritiugenic 3He from 2003 to 2006 in the surface ocean at the Bermuda Atlantic Time-series Study (BATS) site with a sophisticated noble gas calibrated air-sea gas exchange model to constrain the 3He flux across the sea-air interface, which must closely mirror the upward 3He flux into the euphotic zone. The product of the 3He flux and the observed subsurface nitrate-3He relationship provides an estimate of the minimum rate of new production in the BATS region. We also apply the gas model to an earlier time series of 3He measurements at BATS in order to recalculate new production fluxes for the 1985 to 1988 time period. The observations, despite an almost 3-fold difference in the nitrate-3He relationship, yield a roughly consistent estimate of nitrate flux. In particular, the nitrate flux from 2003 to 2006 is estimated to be 0.65 ± 0.14 mol m-2 yr-1, which is ~40 % smaller than the calculated flux for the period from 1985 to 1988. The difference in nitrate flux between the time periods may be signifying a real difference in new production resulting from changes in subtropical mode water formation. Overall, the nitrate flux is larger than most estimates of export fluxes or net community production fluxes made locally for the BATS site, which is likely a reflection of the larger spatial scale covered by the 3He technique and potentially also by the decoupling of 3He and nitrate during the obduction of water masses from the main thermocline into the upper ocean. The upward nitrate flux is certainly large enough to support observed rates of primary production at BATS and more generally
Nonlinear time-series analysis of current signal in cathodic contact glow discharge electrolysis
NASA Astrophysics Data System (ADS)
Allagui, Anis; Rojas, Andrea Espinel; Bonny, Talal; Elwakil, Ahmed S.; Abdelkareem, Mohammad Ali
2016-05-01
In the standard two-electrode configuration employed in electrolytic process, when the control dc voltage is brought to a critical value, the system undergoes a transition from conventional electrolysis to contact glow discharge electrolysis (CGDE), which has also been referred to as liquid-submerged micro-plasma, glow discharge plasma electrolysis, electrode effect, electrolytic plasma, etc. The light-emitting process is associated with the development of an irregular and erratic current time-series which has been arbitrarily labelled as "random," and thus dissuaded further research in this direction. Here, we examine the current time-series signals measured in cathodic CGDE configuration in a concentrated KOH solution at different dc bias voltages greater than the critical voltage. We show that the signals are, in fact, not random according to the NIST SP. 800-22 test suite definition. We also demonstrate that post-processing low-pass filtered sequences requires less time than the native as-measured sequences, suggesting a superposition of low frequency chaotic fluctuations and high frequency behaviors (which may be produced by more than one possible source of entropy). Using an array of nonlinear time-series analyses for dynamical systems, i.e., the computation of largest Lyapunov exponents and correlation dimensions, and re-construction of phase portraits, we found that low-pass filtered datasets undergo a transition from quasi-periodic to chaotic to quasi-hyper-chaotic behavior, and back again to chaos when the voltage controlling-parameter is increased. The high frequency part of the signals is discussed in terms of highly nonlinear turbulent motion developed around the working electrode.
NASA Astrophysics Data System (ADS)
Sawant, S. A.; Chakraborty, M.; Suradhaniwar, S.; Adinarayana, J.; Durbha, S. S.
2016-06-01
Satellite based earth observation (EO) platforms have proved capability to spatio-temporally monitor changes on the earth's surface. Long term satellite missions have provided huge repository of optical remote sensing datasets, and United States Geological Survey (USGS) Landsat program is one of the oldest sources of optical EO datasets. This historical and near real time EO archive is a rich source of information to understand the seasonal changes in the horticultural crops. Citrus (Mandarin / Nagpur Orange) is one of the major horticultural crops cultivated in central India. Erratic behaviour of rainfall and dependency on groundwater for irrigation has wide impact on the citrus crop yield. Also, wide variations are reported in temperature and relative humidity causing early fruit onset and increase in crop water requirement. Therefore, there is need to study the crop growth stages and crop evapotranspiration at spatio-temporal scale for managing the scarce resources. In this study, an attempt has been made to understand the citrus crop growth stages using Normalized Difference Time Series (NDVI) time series data obtained from Landsat archives (http://earthexplorer.usgs.gov/). Total 388 Landsat 4, 5, 7 and 8 scenes (from year 1990 to Aug. 2015) for Worldwide Reference System (WRS) 2, path 145 and row 45 were selected to understand seasonal variations in citrus crop growth. Considering Landsat 30 meter spatial resolution to obtain homogeneous pixels with crop cover orchards larger than 2 hectare area was selected. To consider change in wavelength bandwidth (radiometric resolution) with Landsat sensors (i.e. 4, 5, 7 and 8) NDVI has been selected to obtain continuous sensor independent time series. The obtained crop growth stage information has been used to estimate citrus basal crop coefficient information (Kcb). Satellite based Kcb estimates were used with proximal agrometeorological sensing system
A Regularized Linear Dynamical System Framework for Multivariate Time Series Analysis
Liu, Zitao; Hauskrecht, Milos
2015-01-01
Linear Dynamical System (LDS) is an elegant mathematical framework for modeling and learning Multivariate Time Series (MTS). However, in general, it is difficult to set the dimension of an LDS’s hidden state space. A small number of hidden states may not be able to model the complexities of a MTS, while a large number of hidden states can lead to overfitting. In this paper, we study learning methods that impose various regularization penalties on the transition matrix of the LDS model and propose a regularized LDS learning framework (rLDS) which aims to (1) automatically shut down LDSs’ spurious and unnecessary dimensions, and consequently, address the problem of choosing the optimal number of hidden states; (2) prevent the overfitting problem given a small amount of MTS data; and (3) support accurate MTS forecasting. To learn the regularized LDS from data we incorporate a second order cone program and a generalized gradient descent method into the Maximum a Posteriori framework and use Expectation Maximization to obtain a low-rank transition matrix of the LDS model. We propose two priors for modeling the matrix which lead to two instances of our rLDS. We show that our rLDS is able to recover well the intrinsic dimensionality of the time series dynamics and it improves the predictive performance when compared to baselines on both synthetic and real-world MTS datasets. PMID:25905027
[Improving data warehouse environments for efficient analysis of long time-series data].
Kataoka, Hiromi; Hatakeyama, Yutaka; Okuhara, Yoshiyasu; Sugiura, Tetsuro
2012-07-01
Medical records contain enormous amounts of data. It is important to extract useful evidence from such data and feedback to clinical medicine. Evidence-based medicine (EBM) was introduced in the 1990s and has been widely used for more than 20 years, however, hospital information system environments that take advantage of the ideas of EBM have not yet been established. Recently, the numbers of medical institutions with multilateral search systems for the medical records stored in data warehouses (DWHs) have been increasing, but these institutions' systems cannot deal fully with issues such as data reliability and high-dimensional, high-speed searches. DWHs can control long time-series data. Although, the measurement methods and analytical equipment used have been modified and improved with advances in testing techniques, this may have induced shifting and/or fragmentation of these types of data. Furthermore, database design has to be flexible to satisfy the various demands of information retrieval; systems must therefore have the structures to deal with such demands. We report here our new system infrastructure, which exchanges data in order to absorb the data shifting associated with changes in the testing methods. The system enables the preparation of DWH environments that can be used to seamlessly analyze long time-series data, record in knowledge databases the results of comprehensive analyses of institutions' characteristics of laboratory diagnoses, and use the data in education, research and clinical practice. PMID:22973733
Embedding mRNA Stability in Correlation Analysis of Time-Series Gene Expression Data
Farina, Lorenzo; De Santis, Alberto; Salvucci, Samanta; Morelli, Giorgio; Ruberti, Ida
2008-01-01
Current methods for the identification of putatively co-regulated genes directly from gene expression time profiles are based on the similarity of the time profile. Such association metrics, despite their central role in gene network inference and machine learning, have largely ignored the impact of dynamics or variation in mRNA stability. Here we introduce a simple, but powerful, new similarity metric called lead-lag R2 that successfully accounts for the properties of gene dynamics, including varying mRNA degradation and delays. Using yeast cell-cycle time-series gene expression data, we demonstrate that the predictive power of lead-lag R2 for the identification of co-regulated genes is significantly higher than that of standard similarity measures, thus allowing the selection of a large number of entirely new putatively co-regulated genes. Furthermore, the lead-lag metric can also be used to uncover the relationship between gene expression time-series and the dynamics of formation of multiple protein complexes. Remarkably, we found a high lead-lag R2 value among genes coding for a transient complex. PMID:18670596
Beyond Fractals and 1/f Noise: Multifractal Analysis of Complex Physiological Time Series
NASA Astrophysics Data System (ADS)
Ivanov, Plamen Ch.; Amaral, Luis A. N.; Ashkenazy, Yosef; Stanley, H. Eugene; Goldberger, Ary L.; Hausdorff, Jeffrey M.; Yoneyama, Mitsuru; Arai, Kuniharu
2001-03-01
We investigate time series with 1/f-like spectra generated by two physiologic control systems --- the human heartbeat and human gait. We show that physiological fluctuations exhibit unexpected ``hidden'' structures often described by scaling laws. In particular, our studies indicate that when analyzed on different time scales the heartbeat fluctuations exhibit cascades of branching patterns with self-similar (fractal) properties, characterized by long-range power-law anticorrelations. We find that these scaling features change during sleep and wake phases, and with pathological perturbations. Further, by means of a new wavelet-based technique, we find evidence of multifractality in the healthy human heartbeat even under resting conditions, and show that the multifractal character and nonlinear properties of the healthy heart are encoded in the Fourier phases. We uncover a loss of multifractality for a life-threatening condition, congestive heart failure. In contrast to the heartbeat, we find that the interstride interval time series of healthy human gait, a voluntary process under neural regulation, is described by a single fractal dimension (such as classical 1/f noise) indicating monofractal behavior. Thus our approach can help distinguish physiological and physical signals with comparable frequency spectra and two-point correlations, and guide modeling of their control mechanisms.
NASA Technical Reports Server (NTRS)
Scargle, J. D.
1982-01-01
Detection of a periodic signal hidden in noise is frequently a goal in astronomical data analysis. This paper does not introduce a new detection technique, but instead studies the reliability and efficiency of detection with the most commonly used technique, the periodogram, in the case where the observation times are unevenly spaced. This choice was made because, of the methods in current use, it appears to have the simplest statistical behavior. A modification of the classical definition of the periodogram is necessary in order to retain the simple statistical behavior of the evenly spaced case. With this modification, periodogram analysis and least-squares fitting of sine waves to the data are exactly equivalent. Certain difficulties with the use of the periodogram are less important than commonly believed in the case of detection of strictly periodic signals. In addition, the standard method for mitigating these difficulties (tapering) can be used just as well if the sampling is uneven. An analysis of the statistical significance of signal detections is presented, with examples
NASA Astrophysics Data System (ADS)
Alexander, Robert L.; O'Modhrain, Sile; Roberts, D. Aaron; Gilbert, Jason A.; Zurbuchen, Thomas H.
2014-07-01
The effective navigation, mining, and analysis of large time series data sets presents a recurring challenge throughout heliophysics. Audification, a specific form of auditory analysis commonly used in other fields of research (such as geoseismology), provides a promising technique for the evaluation of spectral features in long heliospheric time series data sets. Following a standard research methodology for the development of new analysis techniques, this paper presents a detailed case study in which audification was introduced into the working process of an experienced heliophysics research scientist and used for the identification and classification of features in high-resolution magnetometer data during a structured analysis task. Auditory evaluation successfully led to the detection of artificial, instrument-induced noise that was not previously observed by the scientist and also the identification of wave activity embedded within turbulent solar wind data. A follow-up interview indicated that the scientist continued using these auditory analysis methods in the assessment of every large data set during the 2 months after the study was completed. These findings indicate that audification can be valuable and enabling for researchers in forming a deeper understanding of both microstructures and macrostructures within large time series. Additionally, as both a standalone methodology and a supplement to visual analysis methods, audification can expedite certain stages of the data survey, analysis, and mining process and provide new qualitative insight into the spectral content of time-varying signals.
Wang, Wei; Shen, Hao; Xie, Jingjing; Zhou, Qiang; Chen, Yu; Lu, Hua
2014-06-01
The present study was aimed to explore possible key genes and bioprocess affected by age during fracture healing. GSE589, GSE592 and GSE1371 were downloaded from gene expression omnibus database. The time-series genes of three age levels rats were firstly identified with hclust function in R. Then functional and pathway enrichment analysis for selected time-series genes were performed. Finally, the VennDiagram package of R language was used to screen overlapping n time-series genes. The expression changes of time-series genes in the rats of three age levels were classified into two types: one was higher expressed at 0 day, decreased at 3 day to 2 week, and increased from 4 to 6 week; the other was the opposite. Functional and pathways enrichment analysis showed that 12 time-series genes of adult and old rats were significantly involved in ECM-receptor interaction pathway. The expression changes of 11 genes were consistent with time axis, 10 genes were up-regulated at 3 days after fracture, and increased slowly in 6 week, while Itga2b was down-regulated. The functions of 106 overlapping genes were all associated with growth and development of bone after fracture. The key genes in ECM-receptor interaction pathway including Spp1, Ibsp, Tnn and Col3a1 have been reported to be related to fracture in literatures. The difference during fracture healing in three age levels rats is mainly related to age. The Spp1, Ibsp, Tnn and Col3a1 are possible potential age-related genes and ECM-receptor interaction pathway is the potential age-related process during fracture healing. PMID:24627361
Mayaud, C.; Wagner, T.; Benischke, R.; Birk, S.
2014-01-01
Summary The Lurbach karst system (Styria, Austria) is drained by two major springs and replenished by both autogenic recharge from the karst massif itself and a sinking stream that originates in low permeable schists (allogenic recharge). Detailed data from two events recorded during a tracer experiment in 2008 demonstrate that an overflow from one of the sub-catchments to the other is activated if the discharge of the main spring exceeds a certain threshold. Time series analysis (autocorrelation and cross-correlation) was applied to examine to what extent the various available methods support the identification of the transient inter-catchment flow observed in this binary karst system. As inter-catchment flow is found to be intermittent, the evaluation was focused on single events. In order to support the interpretation of the results from the time series analysis a simplified groundwater flow model was built using MODFLOW. The groundwater model is based on the current conceptual understanding of the karst system and represents a synthetic karst aquifer for which the same methods were applied. Using the wetting capability package of MODFLOW, the model simulated an overflow similar to what has been observed during the tracer experiment. Various intensities of allogenic recharge were employed to generate synthetic discharge data for the time series analysis. In addition, geometric and hydraulic properties of the karst system were varied in several model scenarios. This approach helps to identify effects of allogenic recharge and aquifer properties in the results from the time series analysis. Comparing the results from the time series analysis of the observed data with those of the synthetic data a good agreement was found. For instance, the cross-correlograms show similar patterns with respect to time lags and maximum cross-correlation coefficients if appropriate hydraulic parameters are assigned to the groundwater model. The comparable behaviors of the real and
Mayaud, C; Wagner, T; Benischke, R; Birk, S
2014-04-16
The Lurbach karst system (Styria, Austria) is drained by two major springs and replenished by both autogenic recharge from the karst massif itself and a sinking stream that originates in low permeable schists (allogenic recharge). Detailed data from two events recorded during a tracer experiment in 2008 demonstrate that an overflow from one of the sub-catchments to the other is activated if the discharge of the main spring exceeds a certain threshold. Time series analysis (autocorrelation and cross-correlation) was applied to examine to what extent the various available methods support the identification of the transient inter-catchment flow observed in this binary karst system. As inter-catchment flow is found to be intermittent, the evaluation was focused on single events. In order to support the interpretation of the results from the time series analysis a simplified groundwater flow model was built using MODFLOW. The groundwater model is based on the current conceptual understanding of the karst system and represents a synthetic karst aquifer for which the same methods were applied. Using the wetting capability package of MODFLOW, the model simulated an overflow similar to what has been observed during the tracer experiment. Various intensities of allogenic recharge were employed to generate synthetic discharge data for the time series analysis. In addition, geometric and hydraulic properties of the karst system were varied in several model scenarios. This approach helps to identify effects of allogenic recharge and aquifer properties in the results from the time series analysis. Comparing the results from the time series analysis of the observed data with those of the synthetic data a good agreement was found. For instance, the cross-correlograms show similar patterns with respect to time lags and maximum cross-correlation coefficients if appropriate hydraulic parameters are assigned to the groundwater model. The comparable behaviors of the real and the
NASA Astrophysics Data System (ADS)
Mayaud, C.; Wagner, T.; Benischke, R.; Birk, S.
2014-04-01
The Lurbach karst system (Styria, Austria) is drained by two major springs and replenished by both autogenic recharge from the karst massif itself and a sinking stream that originates in low permeable schists (allogenic recharge). Detailed data from two events recorded during a tracer experiment in 2008 demonstrate that an overflow from one of the sub-catchments to the other is activated if the discharge of the main spring exceeds a certain threshold. Time series analysis (autocorrelation and cross-correlation) was applied to examine to what extent the various available methods support the identification of the transient inter-catchment flow observed in this binary karst system. As inter-catchment flow is found to be intermittent, the evaluation was focused on single events. In order to support the interpretation of the results from the time series analysis a simplified groundwater flow model was built using MODFLOW. The groundwater model is based on the current conceptual understanding of the karst system and represents a synthetic karst aquifer for which the same methods were applied. Using the wetting capability package of MODFLOW, the model simulated an overflow similar to what has been observed during the tracer experiment. Various intensities of allogenic recharge were employed to generate synthetic discharge data for the time series analysis. In addition, geometric and hydraulic properties of the karst system were varied in several model scenarios. This approach helps to identify effects of allogenic recharge and aquifer properties in the results from the time series analysis. Comparing the results from the time series analysis of the observed data with those of the synthetic data a good agreement was found. For instance, the cross-correlograms show similar patterns with respect to time lags and maximum cross-correlation coefficients if appropriate hydraulic parameters are assigned to the groundwater model. The comparable behaviors of the real and the
PreAnalyseExtended: A graphical tool for (geophysical) time series analysis
NASA Astrophysics Data System (ADS)
Gebauer, André
2016-04-01
Time depending records of different geophysical and geodetic measurement systems require screening and post-processing, often combining the primary observable with additional measurement quantities from other external sensors or geophysical models. The ring laser 'G' located at the Geodetic Observatory Wettzell for example observes rotational ground motions depending on the sensor orientation. Hence tilt effects need to be corrected from the raw measurements of rotation. While the local tilt is taken from an independent time series of an auxiliary sensor, solid Earth tides and polar motion are corrected based on appropriate models. PreAnalyseExtended is a powerful software tool that combines the screening and processing of geophysical measurements of a variety of input sensors with a unique set of at least seven fully included models. This talk provides an introduction the important features of this open source tool.
A genetic programming approach for time-series analysis and prediction in space physics.
NASA Astrophysics Data System (ADS)
Jorgensen, A. M.; Brumby, S. P.; Henderson, M. G.
2004-12-01
A central theme in space weather prediction is the ability to predict time-series of relevant quantities, both empirically, and from physics-based models. Empirical models are often based on educated guesses, or intuition. The task of finding an empirical relationship relating quantities can be tedious and time-consuming, especially when a large number of parameters are involved. Genetic Programming (GP) provides a method for automating the guesswork, and can in some instances automatically find functional relationships between data streams. GP is an evolutionary computation technique which is an extension of the Genetic Algorithm framework used for function optimization. In GP an evolutionary algorithm combines elementary function operators in an attempt to build a function which is able to reproduce a training example from a set of input data. We will illustrate how a GP algorithm can be used in space physics by addressing two relevant topics: The prediction of relativistic electron fluxes, and prediction of Dst.
Solving Logistic Regression with Group Cardinality Constraints for Time Series Analysis
Zhang, Yong; Pohl, Kilian M.
2016-01-01
We propose an algorithm to distinguish 3D+t images of healthy from diseased subjects by solving logistic regression based on cardinality constrained, group sparsity. This method reduces the risk of overfitting by providing an elegant solution to identifying anatomical regions most impacted by disease. It also ensures that consistent identification across the time series by grouping each image feature across time and counting the number of non-zero groupings. While popular in medical imaging, group cardinality constrained problems are generally solved by relaxing counting with summing over the groupings. We instead solve the original problem by generalizing a penalty decomposition algorithm, which alternates between minimizing a logistic regression function with a regularizer based on the Frobenius norm and enforcing sparsity. Applied to 86 cine MRIs of healthy cases and subjects with Tetralogy of Fallot (TOF), our method correctly identifies regions impacted by TOF and obtains a statistically significant higher classification accuracy than logistic regression without and relaxed grouped sparsity constraint.
NASA Technical Reports Server (NTRS)
Scargle, Jeffrey D.
1990-01-01
While chaos arises only in nonlinear systems, standard linear time series models are nevertheless useful for analyzing data from chaotic processes. This paper introduces such a model, the chaotic moving average. This time-domain model is based on the theorem that any chaotic process can be represented as the convolution of a linear filter with an uncorrelated process called the chaotic innovation. A technique, minimum phase-volume deconvolution, is introduced to estimate the filter and innovation. The algorithm measures the quality of a model using the volume covered by the phase-portrait of the innovation process. Experiments on synthetic data demonstrate that the algorithm accurately recovers the parameters of simple chaotic processes. Though tailored for chaos, the algorithm can detect both chaos and randomness, distinguish them from each other, and separate them if both are present. It can also recover nonminimum-delay pulse shapes in non-Gaussian processes, both random and chaotic.
A time-series analysis of flood disaster around Lena river using Landsat TM/ETM+
NASA Astrophysics Data System (ADS)
Sakai, Toru; Hatta, Shigemi; Okumura, Makoto; Takeuchi, Wataru; Hiyama, Tetsuya; Inoue, Gen
2010-05-01
Landsat satellite has provided a continuous record of earth observation since 1972, gradually improving sensors (i.e. MSS, TM and ETM+). Already processed archives of Landsat image are now available free of charge from the internet. The Landsat image of 30 m spatial resolution with multiple spectral bands between 450 and 2350 nm is appropriate for detailed mapping of natural resource at wide geographical areas. However, one of the biggest concerns in the use of Landsat image is the uncertainty in the timing of acquisitions. Although detection of land cover change usually requires acquisitions before and after the change, the Landsat image is often unavailable because of the long-term intervals (16 days) and variation in atmosphere. Nearly cloud-free image is acquired at least once per year (total of 22 or 23 scenes per year). Therefore, it may be difficult to acquire appropriate images for monitoring natural disturbances caused at short-term intervals (e.g., flood, forest fire and hurricanes). Our objectives are: (1) to examine whether a time-series of Landsat image is available for monitoring a flood disaster, and (2) to evaluate the impact and timing of the flood disaster around Lena river in Siberia. A set of Landsat TM/ETM+ satellite images was used to enable acquisition of cloud-free image, although Landsat ETM+ images include failure of the Scan Line Corrector (SLC) from May 2003. The overlap area of a time series of 20 Landsat TM/ETM+ images (path 120-122, row 17) from April 2007 to August 2007 was clipped (approximately 33 km × 90 km), and the other area was excluded from the analyses. Image classification was performed on each image separately using an unsupervised ISODATA method, and each Landsat TM/ETM+ image was classified into three land cover types: (1) ice, (2) water, and (3) land. From three land cover types, the area of Lena river was estimated. The area of Lena river dramatically changed after spring breakup. The middle part of Lena river around
Emerencia, Ando C; Bos, Elisabeth H; Rosmalen, Judith GM; Riese, Harriëtte; Aiello, Marco; Sytema, Sjoerd; de Jonge, Peter
2015-01-01
Background Health promotion can be tailored by combining ecological momentary assessments (EMA) with time series analysis. This combined method allows for studying the temporal order of dynamic relationships among variables, which may provide concrete indications for intervention. However, application of this method in health care practice is hampered because analyses are conducted manually and advanced statistical expertise is required. Objective This study aims to show how this limitation can be overcome by introducing automated vector autoregressive modeling (VAR) of EMA data and to evaluate its feasibility through comparisons with results of previously published manual analyses. Methods We developed a Web-based open source application, called AutoVAR, which automates time series analyses of EMA data and provides output that is intended to be interpretable by nonexperts. The statistical technique we used was VAR. AutoVAR tests and evaluates all possible VAR models within a given combinatorial search space and summarizes their results, thereby replacing the researcher’s tasks of conducting the analysis, making an informed selection of models, and choosing the best model. We compared the output of AutoVAR to the output of a previously published manual analysis (n=4). Results An illustrative example consisting of 4 analyses was provided. Compared to the manual output, the AutoVAR output presents similar model characteristics and statistical results in terms of the Akaike information criterion, the Bayesian information criterion, and the test statistic of the Granger causality test. Conclusions Results suggest that automated analysis and interpretation of times series is feasible. Compared to a manual procedure, the automated procedure is more robust and can save days of time. These findings may pave the way for using time series analysis for health promotion on a larger scale. AutoVAR was evaluated using the results of a previously conducted manual analysis
Trend analysis of air temperature and precipitation time series over Greece: 1955-2010
NASA Astrophysics Data System (ADS)
Marougianni, G.; Melas, D.; Kioutsioukis, I.; Feidas, H.; Zanis, P.; Anandranistakis, E.
2012-04-01
In this study, a database of air temperature and precipitation time series from the network of Hellenic National Meteorological Service has been developed in the framework of the project GEOCLIMA, co-financed by the European Union and Greek national funds through the Operational Program "Competitiveness and Entrepreneurship" of the Research Funding Program COOPERATION 2009. Initially, a quality test was applied to the raw data and then missing observations have been imputed with a regularized, spatial-temporal expectation - maximization algorithm to complete the climatic record. Next, a quantile - matching algorithm was applied in order to verify the homogeneity of the data. The processed time series were used for the calculation of temporal annual and seasonal trends of air temperature and precipitation. Monthly maximum and minimum surface air temperature and precipitation means at all available stations in Greece were analyzed for temporal trends and spatial variation patterns for the longest common time period of homogenous data (1955 - 2010), applying the Mann-Kendall test. The majority of the examined stations showed a significant increase in the summer maximum and minimum temperatures; this could be possibly physically linked to the Etesian winds, because of the less frequent expansion of the low over the southeastern Mediterranean. Summer minimum temperatures have been increasing at a faster rate than that of summer maximum temperatures, reflecting an asymmetric change of extreme temperature distributions. Total annual precipitation has been significantly decreased at the stations located in western Greece, as well as in the southeast, while the remaining areas exhibit a non-significant negative trend. This reduction is very likely linked to the positive phase of the NAO that resulted in an increase in the frequency and persistence of anticyclones over the Mediterranean.
Farag, Tamer H.; Faruque, Abu S.; Wu, Yukun; Das, Sumon K.; Hossain, Anowar; Ahmed, Shahnawaz; Ahmed, Dilruba; Nasrin, Dilruba; Kotloff, Karen L.; Panchilangam, Sandra; Nataro, James P.; Cohen, Dani; Blackwelder, William C.; Levine, Myron M.
2013-01-01
Background Shigella infections are a public health problem in developing and transitional countries because of high transmissibility, severity of clinical disease, widespread antibiotic resistance and lack of a licensed vaccine. Whereas Shigellae are known to be transmitted primarily by direct fecal-oral contact and less commonly by contaminated food and water, the role of the housefly Musca domestica as a mechanical vector of transmission is less appreciated. We sought to assess the contribution of houseflies to Shigella-associated moderate-to-severe diarrhea (MSD) among children less than five years old in Mirzapur, Bangladesh, a site where shigellosis is hyperendemic, and to model the potential impact of a housefly control intervention. Methods Stool samples from 843 children presenting to Kumudini Hospital during 2009–2010 with new episodes of MSD (diarrhea accompanied by dehydration, dysentery or hospitalization) were analyzed. Housefly density was measured twice weekly in six randomly selected sentinel households. Poisson time series regression was performed and autoregression-adjusted attributable fractions (AFs) were calculated using the Bruzzi method, with standard errors via jackknife procedure. Findings Dramatic springtime peaks in housefly density in 2009 and 2010 were followed one to two months later by peaks of Shigella-associated MSD among toddlers and pre-school children. Poisson time series regression showed that housefly density was associated with Shigella cases at three lags (six weeks) (Incidence Rate Ratio = 1.39 [95% CI: 1.23 to 1.58] for each log increase in fly count), an association that was not confounded by ambient air temperature. Autocorrelation-adjusted AF calculations showed that a housefly control intervention could have prevented approximately 37% of the Shigella cases over the study period. Interpretation Houseflies may play an important role in the seasonal transmission of Shigella in some developing country ecologies
Edgelist phase unwrapping algorithm for time series InSAR analysis.
Shanker, A Piyush; Zebker, Howard
2010-03-01
We present here a new integer programming formulation for phase unwrapping of multidimensional data. Phase unwrapping is a key problem in many coherent imaging systems, including time series synthetic aperture radar interferometry (InSAR), with two spatial and one temporal data dimensions. The minimum cost flow (MCF) [IEEE Trans. Geosci. Remote Sens. 36, 813 (1998)] phase unwrapping algorithm describes a global cost minimization problem involving flow between phase residues computed over closed loops. Here we replace closed loops by reliable edges as the basic construct, thus leading to the name "edgelist." Our algorithm has several advantages over current methods-it simplifies the representation of multidimensional phase unwrapping, it incorporates data from external sources, such as GPS, where available to better constrain the unwrapped solution, and it treats regularly sampled or sparsely sampled data alike. It thus is particularly applicable to time series InSAR, where data are often irregularly spaced in time and individual interferograms can be corrupted with large decorrelated regions. We show that, similar to the MCF network problem, the edgelist formulation also exhibits total unimodularity, which enables us to solve the integer program problem by using efficient linear programming tools. We apply our method to a persistent scatterer-InSAR data set from the creeping section of the Central San Andreas Fault and find that the average creep rate of 22 mm/Yr is constant within 3 mm/Yr over 1992-2004 but varies systematically with ground location, with a slightly higher rate in 1992-1998 than in 1999-2003. PMID:20208954
Ordinal time series analysis for Air Quality Index (AQI) in San Bernardino County
NASA Astrophysics Data System (ADS)
Chitakasempornkul, Kessinee
Ambient pollutant, especially ground level ozone that causes respiratory diseases, has been a great concern in Southern California. U.S. Environmental Protection Agency provides the Air Quality Index (AQI) as a tool to assist the public of health warnings. AQI for ozone is currently divided into six states depending on the level of public health concern. In statistical point of view AQI can be characterized as nonstationary ordinal-valued time series. The purpose of this study is to implement statistical models for short-term forecasting of AQI. This thesis presents a generalized linear type modeling to handle the autocorrelated ordinal time series. The model is applied with four different link functions: identity, logit, probit, and complementary log-log and their forecast performance are compared. Random time-varying covariates include past AQI state, various meteorological processes, and periodic component. Data used in this study are AQI for ozone from five monitoring stations in San Bernardino County, CA for 2004 to 2006. For the purpose of evaluating the performance of one-day-ahead forecast, the 2007 data from the same place are used. The meteorological data are from the nearby Barstow city in San Bernardino County. The portmanteau test is used to test error autocorrelations. The partial likelihood ratio test, Akaike information criterion (AIC), and Bayesian information criterion (BIC) are used to measure the goodness of fit and compare the models. The results show the model well captures the nonstationarity in ozone process and remove the nonstationarity in residuals. Both logit and probit models correctly forecast about 85% of the observed AQI.
Array magnetics modal analysis for the DIII-D tokamak based on localized time-series modelling
NASA Astrophysics Data System (ADS)
Olofsson, K. E. J.; Hanson, J. M.; Shiraki, D.; Volpe, F. A.; Humphreys, D. A.; La Haye, R. J.; Lanctot, M. J.; Strait, E. J.; Welander, A. S.; Kolemen, E.; Okabayashi, M.
2014-09-01
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. 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.
NASA Astrophysics Data System (ADS)
Lu, Wen Xi; Zhao, Ying; Chu, Hai Bo; Yang, Lei Lei
2014-09-01
To enhance our understanding of the dynamic characteristics of groundwater level in the western Jilin Province of China, two models of decomposition method in time series analysis, additive model and multiplicative model, are employed in this study. The data used in the models are the monthly groundwater levels of three wells observed from 1986 to 2011. Moreover, the analysis of three wells, located in the upper, middle and downstream of the groundwater flow path, helps to obtain the variation in each well and the mutual comparison among them. The final results indicate that the groundwater levels show a decreasing trend and the period of variation last for about 7 years. In addition, hydrographs of the three wells manifest the impacts of human behavior on groundwater level increases since 1995. Furthermore, compared with the autoregressive integrated moving average model, the decomposition method is recommended in the analysis and prediction of groundwater levels.
Scottish Keep Well health check programme: an interrupted time series analysis
Geue, Claudia; Lewsey, James D; MacKay, Daniel F; Antony, Grace; Fischbacher, Colin M; Muirie, Jill; McCartney, Gerard
2016-01-01
Background Effective interventions are available to reduce cardiovascular risk. Recently, health check programmes have been implemented to target those at high risk of cardiovascular disease (CVD), but there is much debate whether these are likely to be effective at population level. This paper evaluates the impact of wave 1 of Keep Well, a Scottish health check programme, on cardiovascular outcomes. Methods Interrupted time series analyses were employed, comparing trends in outcomes in participating and non-participating practices before and after the introduction of health checks. Health outcomes are defined as CVD mortality, incident hospitalisations and prescribing of cardiovascular drugs. Results After accounting for secular trends and seasonal variation, coronary heart disease mortality and hospitalisations changed by 0.4% (95% CI −5.2% to 6.3%) and −1.1% (−3.4% to 1.3%) in Keep Well practices and by −0.3% (−2.7% to 2.2%) and −0.1% (−1.8% to 1.7%) in non-Keep Well practices, respectively, following the intervention. Adjusted changes in prescribing in Keep Well and non-Keep Well practices were 0.4% (−10.4% to 12.5%) and −1.5% (−9.4% to 7.2%) for statins; −2.5% (−12.3% to 8.4%) and −1.6% (−7.1% to 4.3%) for antihypertensive drugs; and −0.9% (−6.5% to 5.0%) and −2.4% (−10.1% to 6.0%) for antiplatelet drugs. Conclusions Any impact of the Keep Well health check intervention on CVD outcomes and prescribing in Scotland was very small. Findings do not support the use of the screening approach used by current health check programmes to address CVD. We used an interrupted time series method, but evaluation methods based on randomisation are feasible and preferable and would have allowed more reliable conclusions. These should be considered more often by policymakers at an early stage in programme design when there is uncertainty regarding programme effectiveness. PMID:27072868
NASA Astrophysics Data System (ADS)
Satoh, Y.; Yoshimura, K.; Pokhrel, Y. N.; KIM, H.; Oki, T.
2014-12-01
Human society have altered terrestrial hydrological cycles by water management infrastructure, such as reservoirs and weirs for irrigation, in order to enable stable water use against natural variability. On the other hand, anthropogenic climate change is projected to alter the hydro-meteorological cycles, and it is projected that drought frequency and/or intensity will increase in some regions. Thus reliable projection is a critical issue for our society in order to adapt for the change. However, only few studies have investigated the effect of anthropogenic intervention on drought under climate change. This study focuses on hydrological drought, particularly on stream flow, as stream flow is one of the most easy-to-access water resource. HiGW-MAT, a state of arts land surface model capable to reproduce energy and water cycle considering the anthropogenic water management, is used to simulate the historical and future terrestrial water cycles. The model includes reservoir operation, water withdrawal and irrigation process. Five CMIP5 GCM outputs with bias-correction provided by ISI-MIP for 1980-2099 are used to force a set of simulations. Time series data of global hydrological drought for 120 years, with and without human activity, is analyzed in order to estimate the impact of climate change and the adaptation capacity of anthropogenic water management. It is identified that Europe, Central and Eastern Asia, East and West part of USA, Chile, Amazon basin and Congo basin will have large increases of drought more than 90 days. According to uncertainty check particular increases in Central USA and Southern and Eastern South America have high robustness. Dividing global land into 26 regions, we characterized the variation of drought time series for each region. Drought does not show abrupt change and show almost linear increase in many regions. Also, it is found that human activity effectively reduces the increasing rate and suppresses the natural variability under
NASA Astrophysics Data System (ADS)
Cho, S.; Woo, N. C.; Lee, J. M.
2015-12-01
This study is aimed at developing process to analyze and predict groundwater drought potentials for Winter and Spring droughts in Korea using a long-term groundwater monitoring data. So far, most drought researches have been focused on precipitation and stream-flow data, although these data are considered to be non-linear. Subsequently, the prediction of drought events has been very difficult in practice. In this study, we targets to analyze the groundwater system as an intermediate stage between precipitation and stream-flow, but still has semi-linear characteristics. By the analysis of past trends of groundwater time-series compared with drought events, we will identify characteristics of fluctuation between groundwater-level and precipitation of the year before the droughts. Then, the characteristics will be tested with recent drought events in Korea. For this analysis, The updated ATGT (Analysis Tool for Groundwater Time-series data program version 1.0 based on JAVA), that was developed for analyzing and presenting groundwater time-series data, basically to identify abnormal changes in groundwater fluctuations, will be presented with additional functions including cross-correlation between groundwater and drought based on the PYTHON language.
Time series analysis to monitor and assess water resources: a moving average approach.
Reghunath, Rajesh; Murthy, T R Sreedhara; Raghavan, B R
2005-10-01
An understanding of the behavior of the groundwater body and its long-term trends are essential for making any management decision in a given watershed. Geostatistical methods can effectively be used to derive the long-term trends of the groundwater body. Here an attempt has been made to find out the long-term trends of the water table fluctuations of a river basin through a time series approach. The method was found to be useful for demarcating the zones of discharge and of recharge of an aquifer. The recharge of the aquifer is attributed to the return flow from applied irrigation. In the study area, farmers mainly depend on borewells for water and water is pumped from the deep aquifer indiscriminately. The recharge of the shallow aquifer implies excessive pumping of the deep aquifer. Necessary steps have to be taken immediately at appropriate levels to control the irrational pumping of deep aquifer groundwater, which is needed as a future water source. The study emphasizes the use of geostatistics for the better management of water resources and sustainable development of the area. PMID:16240189
Li, Jing; Zipper, Carl E; Donovan, Patricia F; Wynne, Randolph H; Oliphant, Adam J
2015-09-01
Surface mining disturbances have attracted attention globally due to extensive influence on topography, land use, ecosystems, and human populations in mineral-rich regions. We analyzed a time series of Landsat satellite imagery to produce a 28-year disturbance history for surface coal mining in a segment of eastern USA's central Appalachian coalfield, southwestern Virginia. The method was developed and applied as a three-step sequence: vegetation index selection, persistent vegetation identification, and mined-land delineation by year of disturbance. The overall classification accuracy and kappa coefficient were 0.9350 and 0.9252, respectively. Most surface coal mines were identified correctly by location and by time of initial disturbance. More than 8 % of southwestern Virginia's >4000-km(2) coalfield area was disturbed by surface coal mining over the 28-year period. Approximately 19.5 % of the Appalachian coalfield surface within the most intensively mined county (Wise County) has been disturbed by mining. Mining disturbances expanded steadily and progressively over the study period. Information generated can be applied to gain further insight concerning mining influences on ecosystems and other essential environmental features. PMID:26251060
Analysis of the time series of the EEG frequency spectra and of EEG spectral power densities.
Dvorák, J; Formánek, J; Kubát, J; Plevová, J; Vanícková, M; Fires, M; Andél, J; Cipra, T; Tomásek, L; Prásková, Z; Holoubková, E; Fabián, Z
1981-06-01
Some examples of the use of the principal component model for the economic description of the structure of the multiple time series and for the data reduction in the quantitative EEG studies are presented. The broad-band EEG frequency spectra were measured with the use of an electronic system designed by J. Dvorák. The EEG spectral power densities were computed via the discrete Fourier Transform (namely FFT) algorithm. The estimated two or three first principal components account for the major part of the total variance of individual EEG variables: The results hold for the used elementary epoch of measurement, i.e. 5 sec. - With the use of the algorithms and FORTRAN IV programs developed by J. Andĕl, T. Cipra and L. Tomásek a data reduction by a factor of 1:2000 can be achieved without any substantial loss of biological information. - The described methods help to obtain a better insight into the structure of the data and represent a powerful tool for data reduction at least in a certain class of experimental EEG studies (experimental toxicology, pharmacology, experimental neurology). PMID:7270023
Reservoir computing and extreme learning machines for non-linear time-series data analysis.
Butcher, J B; Verstraeten, D; Schrauwen, B; Day, C R; Haycock, P W
2013-02-01
Random projection architectures such as Echo state networks (ESNs) and Extreme Learning Machines (ELMs) use a network containing a randomly connected hidden layer and train only the output weights, overcoming the problems associated with the complex and computationally demanding training algorithms traditionally used to train neural networks, particularly recurrent neural networks. In this study an ESN is shown to contain an antagonistic trade-off between the amount of non-linear mapping and short-term memory it can exhibit when applied to time-series data which are highly non-linear. To overcome this trade-off a new architecture, Reservoir with Random Static Projections (R(2)SP) is investigated, that is shown to offer a significant improvement in performance. A similar approach using an ELM whose input is presented through a time delay (TD-ELM) is shown to further enhance performance where it significantly outperformed the ESN and R(2)SP as well other architectures when applied to a novel task which allows the short-term memory and non-linearity to be varied. The hard-limiting memory of the TD-ELM appears to be best suited for the data investigated in this study, although ESN-based approaches may offer improved performance when processing data which require a longer fading memory. PMID:23275138
NASA Astrophysics Data System (ADS)
Diliberto, Iole Serena
2013-08-01
The exhalation activity at the La Fossa cone (Vulcano Island, Aeolian Archipelago, Italy) has been ongoing for more than 1 century. Many of the monitored geochemical and geophysical parameters have showed transient variations of energy release. The time-series analyses of fumarole temperatures presented in this paper enabled the sequence of observations to be defined and information from different monitoring stations to be integrated. The motion of fluids feeding the fumaroles of the La Fossa cone is driven by the thermal and kinetic energies that balance the seismic and volcanic forces active in the region, and the temperatures of the fumaroles reflect the local response of the hydrothermal system to these forces. During a 14-year period of observation, from 1998 to 2012, fumarole temperatures showed various trends but also cyclic variations characterized by sharp increases. The repetition of these variations during periods with different trends indicates that no physical variation occurred from the hydrothermal source to the surface during the analyzed period, and after each periodic geochemical crisis the previous thermal conditions were restored. Although the continuous monitoring of high-temperature fumaroles was limited to only a few sites, the observed trends characterized the most important fumaroles in the area of Vulcano Island. An evaluation of thermal-energy release based on these spatially discrete measurements would be a speculative exercise in thermodynamics, but the analyses of the recorded data represent a step forward in interpreting the signals from ongoing volcanic activity and in assessing the seismic risk.
Analysis of Crop Phenology Using Time-Series MODIS Data and Climate Data
NASA Astrophysics Data System (ADS)
Ren, J.; Campbell, J. B.; Thomas, R. Q.; Shao, Y.
2014-12-01
Understanding crop phenology is fundamental to agricultural production, management, planning and decision-making. In the continental United States, key phenological stages are strongly influenced by meteorological and climatological conditions. This study is conducted in the Midwestern United States to estimate phonological information for corn and soybean. A time series of the Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) 16-day composites from 2001 to 2013 was analyzed with the TIMESAT program to automatically retrieve key phenological stages. The temperature data from CRUNCEP was analyzed with R based on the crop model to calculate potential planting date and harvest date by AgroIBIS crop phenology algorithm. With these two methods, start of season (planting date), end of season (harvesting date), and length of growing season from 2001 to 2013 were determined and compared. The results showed a good relationship between estimates derived from satellites and estimates calculated by the crop model formula. Crop progress reports from USDA NASS were used to validate our estimates. We will present the relationship between our estimates and validation data. We will select some specific sites to investigate finer scale local changes of crop phenology during the last decade.
Sample Preparation for Phosphoproteomic Analysis of Circadian Time Series in Arabidopsis thaliana
Krahmer, Johanna; Hindle, Matthew M.; Martin, Sarah F.; Le Bihan, Thierry; Millar, Andrew J.
2015-01-01
Systems biological approaches to study the Arabidopsis thaliana circadian clock have mainly focused on transcriptomics while little is known about the proteome, and even less about posttranslational modifications. Evidence has emerged that posttranslational protein modifications, in particular phosphorylation, play an important role for the clock and its output. Phosphoproteomics is the method of choice for a large-scale approach to gain more knowledge about rhythmic protein phosphorylation. Recent plant phosphoproteomics publications have identified several thousand phosphopeptides. However, the methods used in these studies are very labor-intensive and therefore not suitable to apply to a well-replicated circadian time series. To address this issue, we present and compare different strategies for sample preparation for phosphoproteomics that are compatible with large numbers of samples. Methods are compared regarding number of identifications, variability of quantitation, and functional categorization. We focus on the type of detergent used for protein extraction as well as methods for its removal. We also test a simple two-fraction separation of the protein extract. PMID:25662467
Non-linear time series analysis of precipitation events using regional climate networks for Germany
NASA Astrophysics Data System (ADS)
Rheinwalt, Aljoscha; Boers, Niklas; Marwan, Norbert; Kurths, Jürgen; Hoffmann, Peter; Gerstengarbe, Friedrich-Wilhelm; Werner, Peter
2016-02-01
Synchronous occurrences of heavy rainfall events and the study of their relation in time and space are of large socio-economical relevance, for instance for the agricultural and insurance sectors, but also for the general well-being of the population. In this study, the spatial synchronization structure is analyzed as a regional climate network constructed from precipitation event series. The similarity between event series is determined by the number of synchronous occurrences. We propose a novel standardization of this number that results in synchronization scores which are not biased by the number of events in the respective time series. Additionally, we introduce a new version of the network measure directionality that measures the spatial directionality of weighted links by also taking account of the effects of the spatial embedding of the network. This measure provides an estimate of heavy precipitation isochrones by pointing out directions along which rainfall events synchronize. We propose a climatological interpretation of this measure in terms of propagating fronts or event traces and confirm it for Germany by comparing our results to known atmospheric circulation patterns.
Overland Flow Analysis Using Time Series of Suas-Derived Elevation Models
NASA Astrophysics Data System (ADS)
Jeziorska, J.; Mitasova, H.; Petrasova, A.; Petras, V.; Divakaran, D.; Zajkowski, T.
2016-06-01
With the advent of the innovative techniques for generating high temporal and spatial resolution terrain models from Unmanned Aerial Systems (UAS) imagery, it has become possible to precisely map overland flow patterns. Furthermore, the process has become more affordable and efficient through the coupling of small UAS (sUAS) that are easily deployed with Structure from Motion (SfM) algorithms that can efficiently derive 3D data from RGB imagery captured with consumer grade cameras. We propose applying the robust overland flow algorithm based on the path sampling technique for mapping flow paths in the arable land on a small test site in Raleigh, North Carolina. By comparing a time series of five flights in 2015 with the results of a simulation based on the most recent lidar derived DEM (2013), we show that the sUAS based data is suitable for overland flow predictions and has several advantages over the lidar data. The sUAS based data captures preferential flow along tillage and more accurately represents gullies. Furthermore the simulated water flow patterns over the sUAS based terrain models are consistent throughout the year. When terrain models are reconstructed only from sUAS captured RGB imagery, however, water flow modeling is only appropriate in areas with sparse or no vegetation cover.
Sun, Bruce Qiang; Zhang, Jie
2016-03-01
For the effects of social integration on suicides, there have been different and even contradictive conclusions. In this study, the selected economic and social risks of suicide for different age groups and genders in the United Kingdom were identified and the effects were estimated by the multilevel time series analyses. To our knowledge, there exist no previous studies that estimated a dynamic model of suicides on the time series data together with multilevel analysis and autoregressive distributed lags. The investigation indicated that unemployment rate, inflation rate, and divorce rate are all significantly and positively related to the national suicide rates in the United Kingdom from 1981 to 2011. Furthermore, the suicide rates of almost all groups above 40 years are significantly associated with the risk factors of unemployment and inflation rate, in comparison with the younger groups. PMID:27404607
Macchiato, M. ); Serio, C. ); Lapenna, V. ); Rotonda, L.La. )
1993-07-01
The statistical analysis of cold air temperatures (cold spells) and hot air temperatures (hot spells) is discussed. Air temperature time series observed at 50 stations in southern Italy are investigated. The deterministic and stochastic components of the time series are identified and described by a dynamic-stochastic model that is periodic in the deterministic part (the annual cycle) and Markovian (first-order autoregressive) in the stochastic part. The annual cycle is described by only a few Fourier coefficients. Based on the model fitted to the data, the theoretical probability of cold (hot) spells is computed and compared to that estimated from the observed data. Spatial patterns of identified that make it possible to extrapolate the probability of cold (hot) spells at locations where no direct observations are available. 19 refs., 13 figs., 2 tabs.
NASA Astrophysics Data System (ADS)
Schultz, Michael; Verbesselt, Jan; Herold, Martin; Avitabile, Valerio
2013-10-01
Researchers who use remotely sensed data can spend half of their total effort analysing prior data. If this data preprocessing does not match the application, this time spent on data analysis can increase considerably and can lead to inaccuracies. Despite the existence of a number of methods for pre-processing Landsat time series, each method has shortcomings, particularly for mapping forest changes under varying illumination, data availability and atmospheric conditions. Based on the requirements of mapping forest changes as defined by the United Nations (UN) Reducing Emissions from Forest Degradation and Deforestation (REDD) program, the accurate reporting of the spatio-temporal properties of these changes is necessary. We compared the impact of three fundamentally different radiometric preprocessing techniques Moderate Resolution Atmospheric TRANsmission (MODTRAN), Second Simulation of a Satellite Signal in the Solar Spectrum (6S) and simple Dark Object Subtraction (DOS) on mapping forest changes using Landsat time series data. A modification of Breaks For Additive Season and Trend (BFAST) monitor was used to jointly map the spatial and temporal agreement of forest changes at test sites in Ethiopia and Viet Nam. The suitability of the pre-processing methods for the occurring forest change drivers was assessed using recently captured Ground Truth and high resolution data (1000 points). A method for creating robust generic forest maps used for the sampling design is presented. An assessment of error sources has been performed identifying haze as a major source for time series analysis commission error.
Time-Series Analysis of Remotely-Sensed SeaWiFS Chlorophyll in River-Influenced Coastal Regions
NASA Technical Reports Server (NTRS)
Acker, James G.; McMahon, Erin; Shen, Suhung; Hearty, Thomas; Casey, Nancy
2009-01-01
The availability of a nearly-continuous record of remotely-sensed chlorophyll a data (chl a) from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) mission, now longer than ten years, enables examination of time-series trends for multiple global locations. Innovative data analysis technology available on the World Wide Web facilitates such analyses. In coastal regions influenced by river outflows, chl a is not always indicative of actual trends in phytoplankton chlorophyll due to the interference of colored dissolved organic matter and suspended sediments; significant chl a timeseries trends for coastal regions influenced by river outflows may nonetheless be indicative of important alterations of the hydrologic and coastal environment. Chl a time-series analysis of nine marine regions influenced by river outflows demonstrates the simplicity and usefulness of this technique. The analyses indicate that coastal time-series are significantly influenced by unusual flood events. Major river systems in regions with relatively low human impact did not exhibit significant trends. Most river systems with demonstrated human impact exhibited significant negative trends, with the noteworthy exception of the Pearl River in China, which has a positive trend.
NASA Astrophysics Data System (ADS)
Caraway, Nina Marie; McCreight, James Lucian; Rajagopalan, Balaji
2014-01-01
We offer a multisite stochastic weather generator which is an enhancement to the traditional K-nearest neighbor resampling approach. The proposed weather generator consists of three main components: (i) Clustering of spatial locations into homogeneous regions based on a selected attribute (precipitation), (ii) Markov transition probabilities (either on individual clusters or) over all eight wet/dry states of the three-cluster system to model the spatial precipitation occurrence, and (iii) the traditional K-NN weather generator applied to each cluster-averaged weather time series to generate weather sequences at all the desired locations. The weather generator is also adapted to conditional simulation based on seasonal forecasts involving modification of the third component. We demonstrate the utility of this approach by simulating daily weather sequences at 66 locations in the 25,000 sq. mile San Juan River watershed, a tributary of the Colorado River, USA. As the classic K-NN approach involves sampling from a domain-averaged feature vector, all daily weather is simulated across all locations simultaneously. While this preserves the joint statistics, it tends to be biased to the extremes on any given day. Our cluster-based approach offers the ability to account for regional persistence and spatial non-stationarities. In our comparison of the methods, the cluster-based approach demonstrates some improvement over the classic approach, particularly when modeling winter precipitation, reproducing spells, and in dry years. While this particular application shows only marginal improvement, we offer cluster-based resampling as a novel methodological contribution.
On the causes of major Baltic inflows —an analysis of long time series
NASA Astrophysics Data System (ADS)
Schinke, Holger; Matthäus, Wolfgang
1998-01-01
Conditions for life in the deep water of the Baltic Sea are strongly influenced by inflows of highly saline and oxygenated water from the North Sea. These events - termed major Baltic inflows (MBI) - have episodic character, and are the only mechanisms by which the central Baltic deep water is renewed. Although the cycle of water renewal is well documented, certain meteorological and oceanographic processes determining it are either not very well understood or even partly unknown. Based on the data set of major inflows during the present century, long time series of relevant variables from the Baltic Sea itself (salinity, sea level), its drainage area (river runoff, precipitation), the whole Baltic region (air temperature) and from the North Atlantic and Europe (sea level pressure) are analyzed using statistical methods. Characteristic variations in the relevant meteorological, hydrological and oceanographic variables before and during major events are calculated in order to identify conditions favouring or preventing such events. Major Baltic inflows are characterized by two phases: (1) high pressure over the Baltic region with easterly winds followed by (2) several weeks of strong zonal wind and pressure fields over the North Atlantic and Europe. Major events may occur when only one of these is well developed, the probability of strong events is high if both phases are well developed and closely spaced in time. Variations in river runoff to the Baltic obviously have a greater impact on the occurrence of major events then hitherto supposed. The decreasing frequency and intensity of major inflows since the mid-1970s and the complete absence of such events from February 1983 to the beginning of 1993 is explained by increased zonal circulation linked with intensified precipitation in the Baltic region and increased river runoff to the Baltic. Possible anthropogenic impacts on changes in occurrence of major inflows due to river runoff regulations are indicated. The
Changes in the Use of Broad-Spectrum Antibiotics after Cefepime Shortage: a Time Series Analysis
Pannatier, A.; Ruffieux, C.; Kronenberg, A.; Mühlemann, K.; Zanetti, G.
2012-01-01
The original cefepime product was withdrawn from the Swiss market in January 2007 and replaced by a generic 10 months later. The goals of the study were to assess the impact of this cefepime shortage on the use and costs of alternative broad-spectrum antibiotics, on antibiotic policy, and on resistance of Pseudomonas aeruginosa toward carbapenems, ceftazidime, and piperacillin-tazobactam. A generalized regression-based interrupted time series model assessed how much the shortage changed the monthly use and costs of cefepime and of selected alternative broad-spectrum antibiotics (ceftazidime, imipenem-cilastatin, meropenem, piperacillin-tazobactam) in 15 Swiss acute care hospitals from January 2005 to December 2008. Resistance of P. aeruginosa was compared before and after the cefepime shortage. There was a statistically significant increase in the consumption of piperacillin-tazobactam in hospitals with definitive interruption of cefepime supply and of meropenem in hospitals with transient interruption of cefepime supply. Consumption of each alternative antibiotic tended to increase during the cefepime shortage and to decrease when the cefepime generic was released. These shifts were associated with significantly higher overall costs. There was no significant change in hospitals with uninterrupted cefepime supply. The alternative antibiotics for which an increase in consumption showed the strongest association with a progression of resistance were the carbapenems. The use of alternative antibiotics after cefepime withdrawal was associated with a significant increase in piperacillin-tazobactam and meropenem use and in overall costs and with a decrease in susceptibility of P. aeruginosa in hospitals. This warrants caution with regard to shortages and withdrawals of antibiotics. PMID:22123703
Time Series Analysis of Meteorological Factors Influencing Malaria in South Eastern Iran
Ostovar, Afshin; Haghdoost, Ali Akbar; Rahimiforoushani, Abbas; Raeisi, Ahmad; Majdzadeh, Reza
2016-01-01
Background: The Malaria Early Warning System is defined as the use of prognostic variables for predicting the occurrence of malaria epidemics several months in advance. The principal objective of this study was to provide a malaria prediction model by using meteorological variables and historical malaria morbidity data for malaria-endemic areas in south eastern Iran. Methods: A total of 2002 locally transmitted microscopically confirmed malaria cases, which occurred in the Minab district of Hormozgan Province in Iran over a period of 6 years from March 2003 to March 2009, were analysed. Meteorological variables (the rainfall, temperature, and relative humidity in this district) were also assessed. Monthly and weekly autocorrelation functions, partial autocorrelation functions, and cross-correlation graphs were examined to explore the relationship between the historical morbidity data and meteorological variables and the number of cases of malaria. Having used univariate auto-regressive integrated moving average or transfer function models, significant predictors among the meteorological variables were selected to predict the number of monthly and weekly malaria cases. Ljung-Box statistics and stationary R-squared were used for model diagnosis and model fit, respectively. Results: The weekly model had a better fit (R2= 0.863) than the monthly model (R2= 0.424). However, the Ljung-Box statistic was significant for the weekly model. In addition to autocorrelations, meteorological variables were not significant, except for different orders of maximum and minimum temperatures in the monthly model. Conclusions: Time-series models can be used to predict malaria incidence with acceptable accuracy in a malaria early-warning system. The applicability of using routine meteorological data in statistical models is seriously limited. PMID:27308280
Time series analysis of recent (1 ky) sediments of the euxinic slope of the Black Sea
NASA Astrophysics Data System (ADS)
Duliu, O. G.; Oaie, G.; Preoteasa, F.
2012-04-01
To reconstruct the past history of the euxinic environment of the Black Sea, a 0.5 m core containing unconsolidated sediments was collected at a dept of 600 m on the slope of Continental Platform of the Black Sea. The vertical profiles of both Cs-137 and Pb-210, as radiometricaly measured, allowed us to calculate a sedimentation ratio of 0.49 ± 0.03 mm/y, thus giving to the entire stratigraphic column an age of about 1 ky. By means of a fourth generation Computer Tomograph we have obtained a high resolution tomographic image of a longitudinal section through entire core evidencing the presence of about 250 parallel laminae (1 to 2.5 mm thick) consisting of an alternation of coccolithic and argillaceous mud. After image digitization, we have obtained the corresponding 3550 equidistant points time series (TS). After detrending, TS was analyzed by means of the Blackman-Tukey correlogram and subsequently decomposed in wavelet functions. The resulted correlogram evidenced multiple maxima, the most important ones corresponding to 307, 125, 35, 18, 9 and 7 years (at p <0.001). At the same time, the Morlet wavelet evolutionary spectra showed the presence of a bundle of cycles whose age, estimated to be between A.D. 1600 and 1800, could indicate some significant changes of the European environment, the end of the Little Ice Age being one of the possible explanations. At the same time, the relative constancy of the laminae thickness along entire sedimentary column testifies to a long term stationarity of the euxinic environment during the last thousand years, in concordance with the experimental data regarding both Mo and U vertical profiles, two important proxies of the euxinic medium.
Time-series analysis of weather and mortality patterns in Nairobi's informal settlements
Egondi, Thaddaeus; Kyobutungi, Catherine; Kovats, Sari; Muindi, Kanyiva; Ettarh, Remare; Rocklöv, Joacim
2012-01-01
Background Many studies have established a link between weather (primarily temperature) and daily mortality in developed countries. However, little is known about this relationship in urban populations in sub-Saharan Africa. Objectives The objective of this study was to describe the relationship between daily weather and mortality in Nairobi, Kenya, and to evaluate this relationship with regard to cause of death, age, and sex. Methods We utilized mortality data from the Nairobi Urban Health and Demographic Surveillance System and applied time-series models to study the relationship between daily weather and mortality for a population of approximately 60,000 during the period 2003–2008. We used a distributed lag approach to model the delayed effect of weather on mortality, stratified by cause of death, age, and sex. Results Increasing temperatures (above 75th percentile) were significantly associated with mortality in children and non-communicable disease (NCD) deaths. We found all-cause mortality of shorter lag of same day and previous day to increase by 3.0% for a 1 degree decrease from the 25th percentile of 18°C (not statistically significant). Mortality among people aged 50+ and children aged below 5 years appeared most susceptible to cold compared to other age groups. Rainfall, in the lag period of 0–29 days, increased all-cause mortality in general, but was found strongest related to mortality among females. Low temperatures were associated with deaths due to acute infections, whereas rainfall was associated with all-cause pneumonia and NCD deaths. Conclusions Increases in mortality were associated with both hot and cold weather as well as rainfall in Nairobi, but the relationship differed with regard to age, sex, and cause of death. Our findings indicate that weather-related mortality is a public health concern for the population in the informal settlements of Nairobi, Kenya, especially if current trends in climate change continue. PMID:23195509
Multi-locus Analysis of Genomic Time Series Data from Experimental Evolution
Terhorst, Jonathan; Schlötterer, Christian; Song, Yun S.
2015-01-01
Genomic time series data generated by evolve-and-resequence (E&R) experiments offer a powerful window into the mechanisms that drive evolution. However, standard population genetic inference procedures do not account for sampling serially over time, and new methods are needed to make full use of modern experimental evolution data. To address this problem, we develop a Gaussian process approximation to the multi-locus Wright-Fisher process with selection over a time course of tens of generations. The mean and covariance structure of the Gaussian process are obtained by computing the corresponding moments in discrete-time Wright-Fisher models conditioned on the presence of a linked selected site. This enables our method to account for the effects of linkage and selection, both along the genome and across sampled time points, in an approximate but principled manner. We first use simulated data to demonstrate the power of our method to correctly detect, locate and estimate the fitness of a selected allele from among several linked sites. We study how this power changes for different values of selection strength, initial haplotypic diversity, population size, sampling frequency, experimental duration, number of replicates, and sequencing coverage depth. In addition to providing quantitative estimates of selection parameters from experimental evolution data, our model can be used by practitioners to design E&R experiments with requisite power. We also explore how our likelihood-based approach can be used to infer other model parameters, including effective population size and recombination rate. Then, we apply our method to analyze genome-wide data from a real E&R experiment designed to study the adaptation of D. melanogaster to a new laboratory environment with alternating cold and hot temperatures. PMID:25849855
Daily Mean Temperature Affects Urolithiasis Presentation in Seoul: a Time-series Analysis
2016-01-01
This study aimed to investigate the overall cumulative exposure-response and the lag response relationships between daily temperature and urolithiasis presentation in Seoul. Using a time-series design and distributing lag nonlinear methods, we estimated the relative risk (RR) of urolithiasis presentation associated with mean daily temperature, including the cumulative RR for a 20 days period, and RR for individual daily lag through 20 days. We analyzed data from 14,518 patients of 4 hospitals emergency department who sought medical evaluation or treatment of urolithiasis from 2005-2013 in Seoul. RR was estimated according to sex and age. Associations between mean daily temperature and urolithiasis presentation were not monotonic. Furthermore, there was variation in the exposure-response curve shapes and the strength of association at different temperatures, although in most cases RRs increased for temperatures above the 13°C reference value. The RRs for urolothiasis at 29°C vs. 13°C were 2.54 in all patients (95% confidence interval [CI]: 1.67-3.87), 2.59 in male (95% CI, 1.56-4.32), 2.42 in female (95% CI, 1.15-5.07), 3.83 in male less than 40 years old (95% CI, 1.78-8.26), and 2.47 in male between 40 and 60 years old (95% CI, 1.15-5.34). Consistent trends of increasing RR of urolithiasis presentation were observed within 5 days of high temperatures across all groups. Urolithiasis presentation increased with high temperature with higher daily mean temperatures, with the strongest associations estimated for lags of only a few days, in Seoul, a metropolitan city in Korea. PMID:27134497
Extreme value analysis in the Danube lower basin discharge time series in the twentieth century
NASA Astrophysics Data System (ADS)
Mares, C.; Mares, Ileana; Stanciu, Antoaneta
2009-03-01
The daily discharge time series in the lower Danube basin (Orsova) have been considered for the 1900-2005 period. The extreme value theory (EVT) is applied for the study of daily discharges incorporating some covariates. Two methods are applied for fitting the data to an extreme value distribution: block maxima and peaks over thresholds (POT). Using the block maxima approach associated with the use of the generalised extreme value (GEV) distribution, monthly and seasonal maxima of daily discharge for 1900-2005 have been analysed. Separately the monthly maxima of daily discharge for the 1958-2001 was analysed in order to be compatible with atmospheric circulation available from ERA-40. For performing parameter estimation, the maximum likelihood estimation (MLE) method was used. From the three possible types of GEV distribution, a Weibull distribution fits both the monthly and seasonal maxima of the daily discharges very well. The North Atlantic Oscillation (NAO) and the first ten principal components (PC) of the decomposition in multi-variate empirical orthogonal functions (MEOF) of three atmospheric fields (sea level pressure, 500 hPa and 500-1000 hPa thickness) over the Atlantic-European region (ERA-40), have been introduced as covariates. An improvement over the model without the covariate is found by incorporating NAO as the covariate in location parameter, especially for the spring maxima having the NAO as predictor during the winter. Related to atmospheric circulation influence, the most significant results are obtained by incorporating the first 10 PCs of the MEOF in the location parameter of GEV distribution within a month before the month of the discharge level. Regarding the POT approach associated with generalised Pareto distribution (GPD), different thresholds have been tested for daily discharges in the period 1900-2005, where the maxima were fitted by a bounded (or beta) distribution.
NASA Astrophysics Data System (ADS)
Hackl, M.; Malservisi, R.; Hugentobler, U.; Wonnacott, R.
2011-11-01
We present a method to derive velocity uncertainties from GPS position time series that are affected by time-correlated noise. This method is based on the Allan variance, which is widely used in the estimation of oscillator stability and requires neither spectral analysis nor maximum likelihood estimation (MLE). The Allan variance of the rate (AVR) is calculated in the time domain and hence is not too sensitive to gaps in the time series. We derived analytical expressions of the AVR for different kinds of noises like power law noise, white noise, flicker noise, and random walk and found an expression for the variance produced by an annual signal. These functional relations form the basis of error models that have to be fitted to the AVR in order to estimate the velocity uncertainty. Finally, we applied the method to the South Africa GPS network TrigNet. Most time series show noise characteristics that can be modeled by a power law noise plus an annual signal. The method is computationally very cheap, and the results are in good agreement with the ones obtained by methods based on MLE.
NASA Astrophysics Data System (ADS)
Feigin, A. M.; Mukhin, D.; Volodin, E. M.; Gavrilov, A.; Loskutov, E. M.
2013-12-01
The new method of decomposition of the Earth's climate system into well separated spatial-temporal patterns ('climatic modes') is discussed. The method is based on: (i) generalization of the MSSA (Multichannel Singular Spectral Analysis) [1] for expanding vector (space-distributed) time series in basis of spatial-temporal empirical orthogonal functions (STEOF), which makes allowance delayed correlations of the processes recorded in spatially separated points; (ii) expanding both real SST data, and longer by several times SST data generated numerically, in STEOF basis; (iii) use of the numerically produced STEOF basis for exclusion of 'too slow' (and thus not represented correctly) processes from real data. The application of the method allows by means of vector time series generated numerically by the INM RAS Coupled Climate Model [2] to separate from real SST anomalies data [3] two climatic modes possessing by noticeably different time scales: 3-5 and 9-11 years. Relations of separated modes to ENSO and PDO are investigated. Possible applications of spatial-temporal climatic patterns concept to prognosis of climate system evolution is discussed. 1. Ghil, M., R. M. Allen, M. D. Dettinger, K. Ide, D. Kondrashov, et al. (2002) "Advanced spectral methods for climatic time series", Rev. Geophys. 40(1), 3.1-3.41. 2. http://83.149.207.89/GCM_DATA_PLOTTING/GCM_INM_DATA_XY_en.htm 3. http://iridl.ldeo.columbia.edu/SOURCES/.KAPLAN/.EXTENDED/.v2/.ssta/
NASA Astrophysics Data System (ADS)
Samsonov, S. V.; d'Oreye, N.; Gonzalez, P. J.; Tiampo, K. F.
2013-12-01
Modern Synthetic Aperture Radar (SAR) satellites and satellite constellations are capable of acquiring data at high spatial resolution and increasing temporal resolution allowing detection of ground deformation signals with a minimal delay. Advanced interferometric SAR (InSAR) processing techniques, such as Small Baseline Subset (SBAS) and Multidimensional Small Baseline Subset (MSBAS) are capable of producing time series of ground deformation with a very high sub-centimeter precision. Additionally MSBAS allows combination of various InSAR data into a single set of vertical and horizontal deformation time series further improving their temporal resolution and precision. Developed methodologies are ready for operational monitoring of natural and anthropogenic hazards, including landslides, volcanoes, earthquakes and tectonic motion and ground subsidence caused by mining and groundwater extraction. Here we present various case studies where an InSAR time series analysis was able to map ground deformation with superior resolution and precision, including mining subsidence in the Greater Luxembourg region and southern Saskatchewan, groundwater extraction related subsidence in the Greater Vancouver Region, volcanic deformation in the Virunga Volcanic Province, and tectonic deformation and landslide in northern California. Often, InSAR is the best cost-efficient solution with no restrictions on spatial coverage, weather or lighting condition and timing. It is anticipated that the use of SAR data for mapping hazards will increase in the future as data access improves.
NASA Astrophysics Data System (ADS)
Forootan, Ehsan; Kusche, Jürgen
2016-04-01
Geodetic/geophysical observations, such as the time series of global terrestrial water storage change or sea level and temperature change, represent samples of physical processes and therefore contain information about complex physical interactionswith many inherent time scales. Extracting relevant information from these samples, for example quantifying the seasonality of a physical process or its variability due to large-scale ocean-atmosphere interactions, is not possible by rendering simple time series approaches. In the last decades, decomposition techniques have found increasing interest for extracting patterns from geophysical observations. Traditionally, principal component analysis (PCA) and more recently independent component analysis (ICA) are common techniques to extract statistical orthogonal (uncorrelated) and independent modes that represent the maximum variance of observations, respectively. PCA and ICA can be classified as stationary signal decomposition techniques since they are based on decomposing the auto-covariance matrix or diagonalizing higher (than two)-order statistical tensors from centered time series. However, the stationary assumption is obviously not justifiable for many geophysical and climate variables even after removing cyclic components e.g., the seasonal cycles. In this paper, we present a new decomposition method, the complex independent component analysis (CICA, Forootan, PhD-2014), which can be applied to extract to non-stationary (changing in space and time) patterns from geophysical time series. Here, CICA is derived as an extension of real-valued ICA (Forootan and Kusche, JoG-2012), where we (i) define a new complex data set using a Hilbert transformation. The complex time series contain the observed values in their real part, and the temporal rate of variability in their imaginary part. (ii) An ICA algorithm based on diagonalization of fourth-order cumulants is then applied to decompose the new complex data set in (i
Trends in Statin Use in Seniors 1999 to 2013: Time Series Analysis
Corkum, Amber; Sketris, Ingrid; Fisher, Judith; Zhang, Ying; Saleh, Ahmed
2016-01-01
Purpose To examine HMG-CoA reductase inhibitor (statin) drug dispensing patterns to Nova Scotia Seniors' Pharmacare program (NSSPP) beneficiaries over a 14-year period in response to: 1) rosuvastatin market entry in 2003, 2) JUPITER trial publication in 2008, and 3) generic atorvastatin availability in 2010. Methods All NSSPP beneficiaries who redeemed at least one prescription for a statin from April 1, 1999 to March 31, 2013 were included. Aggregated, anonymous monthly prescription counts were extracted by the Nova Scotia Department of Health and Wellness (Nova Scotia, Canada) and changes in dispensing patterns of statins were measured. Data were analyzed using descriptive analyses and interrupted time series methods. Results The percentage of NSSPP beneficiaries dispensed any statin increased from 5.3% in April 1999 to 20.7% in March 2013. In 1999, most NSSPP beneficiaries were dispensed either simvastatin (29.5%) or atorvastatin (28.7%). When rosuvastatin was added to the NSSPP Formulary in August 2003, prescriptions dispensed for simvastatin, lovastatin, pravastatin, and fluvastatin declined significantly (slope change, -0.0027; 95% confidence interval (CI), (-0.0046, -0.0009)). This significant decline continued following the publication of JUPITER (level change, -0.1974; 95% CI, (-0.2991, -0.0957)) and the availability of generic atorvastatin (level change, -0.2436; 95% CI, (-0.3314, -0.1558)). Atorvastatin was not significantly affected by any of the three interventions, although it maintained an overall decreasing trend. Only upon the availability of generic atorvastatin did the upward trend in rosuvastatin use decrease significantly (slope change, -0.0010, 95% CI, (-0.0015, -0.0005)). Conclusions The type and rate of statins dispensed to NSSPP beneficiaries changed from 1999 to 2013 in response to the availability of new agents and publication of the JUPITER trial. The overall proportion of NSSPP beneficiaries dispensed a statin increased approximately 4
Investigating the Creeping Segment of the San Andreas Fault using InSAR time series analysis
NASA Astrophysics Data System (ADS)
Rolandone, Frederique; Ryder, Isabelle; Agram, Piyush S.; Burgmann, Roland; Nadeau, Robert M.
2010-05-01
We exploit the advanced Interferometric Synthetic Aperture Radar (InSAR) technique referred to as the Small BAseline Subset (SBAS) algorithm to analyze the creeping section of the San Andreas Fault in Central California. Various geodetic creep rate measurements along the Central San Andreas Fault (CSAF) have been made since 1969 including creepmeters, alignment arrays, geodolite, and GPS. They show that horizontal surface displacements increase from a few mm/yr at either end to a maximum of up to ~34 mm/yr in the central portion. They also indicate some discrepancies in rate estimates, with the range being as high as 10 mm/yr at some places along the fault. This variation is thought to be a result of the different geodetic techniques used and of measurements being made at variable distances from the fault. An interferometric stack of 12 interferograms for the period 1992-2001 shows the spatial variation of creep that occurs within a narrow (<2 km) zone close to the fault trace. The creep rate varies spatially along the fault but also in time. Aseismic slip on the CSAF shows several kinds of time dependence. Shallow slip, as measured by surface measurements across the narrow creeping zone, occurs partly as ongoing steady creep, along with brief episodes with slip from mm to cm. Creep rates along the San Juan Bautista segment increased after the 1989 Loma Prieta earthquake and slow slip transients of varying duration and magnitude occurred in both transition segments The main focus of this work is to use the SBAS technique to identify spatial and temporal variations of creep on the CSAF. We will present time series of line-of-sight (LOS) displacements derived from SAR data acquired by the ASAR instrument, on board the ENVISAT satellite, between 2003 and 2009. For each coherent pixel of the radar images we compute time-dependent surface displacements as well as the average LOS deformation rate. We compare our results with characteristic repeating microearthquakes that
Analysis of time series of Cs-137 concentration in sewage sludge at Fukushima City
NASA Astrophysics Data System (ADS)
Fischer, Helmut W.; Mack, Majvor; Shikano, Yudai; Yokoo, Yoshiyuki
2015-04-01
Daily routine radioisotope measurements of sewage sludge at the sewage plant of Fukushima City starting in 2011 have provided a detailed data set for the isotopes Cs-137, Cs-134 and I-131. The long-term trend for the Cs isotopes is comparable to data sets from Central Europe caused by the Chernobyl emissions in 1986 - the average Cs-137 concentration decreases faster in the first year (T1/2 < 1 yr) and slower in later years (T1/2 > 1 yr). Absolute values at Fukushima City are comparably low (mostly below 1 kBq/kg dry mass), due to the existence of separate wastewater and rainwater sewer systems, with only a small portion of rainwater and erosion products reaching the purification plant. Cs-134 data decay faster due to the shorter radioactive half-life. I-131 appears even years after the NPP releases and is assumed to originate from the common medical usage of the isotope for thyroid treatment. Short-term Cs data show a clear dependence on rainfall: each significant rainfall event causes a concentration increase in sludge of up to a factor of ten. Therefore the time series exhibits high short-term variability. Here we attempt to numerically analyse the detailed Cs-137 data set, using two separate approaches: The first method tries to connect parameters like the local surface deposition density, surface types (sealed/unsealed), rainfall statistics, rainfall-induced erosion rate, leakage rate from rainwater to wastewater sewer, transport time in the sewer and residence time in the purification plant for a basically physical approach. As not all parameters are known, values have to be assumed or can be extracted in the course of the fitting process. The second approach is purely heuristic, based on a water surface runoff and transport model. Whilst there is no ad-hoc physical meaning in the extracted parameters, they can possibly be interpreted as such when compared with physical modeling results. The combination of both methods is expected to give a deeper insight
Training emergency services’ dispatchers to recognise stroke: an interrupted time-series analysis
2013-01-01
Background Stroke is a time-dependent medical emergency in which early presentation to specialist care reduces death and dependency. Up to 70% of all stroke patients obtain first medical contact from the Emergency Medical Services (EMS). Identifying ‘true stroke’ from an EMS call is challenging, with over 50% of strokes being misclassified. The aim of this study was to evaluate the impact of the training package on the recognition of stroke by Emergency Medical Dispatchers (EMDs). Methods This study took place in an ambulance service and a hospital in England using an interrupted time-series design. Suspected stroke patients were identified in one week blocks, every three weeks over an 18 month period, during which time the training was implemented. Patients were included if they had a diagnosis of stroke (EMS or hospital). The effect of the intervention on the accuracy of dispatch diagnosis was investigated using binomial (grouped) logistic regression. Results In the Pre-implementation period EMDs correctly identified 63% of stroke patients; this increased to 80% Post-implementation. This change was significant (p=0.003), reflecting an improvement in identifying stroke patients relative to the Pre-implementation period both the During-implementation (OR=4.10 [95% CI 1.58 to 10.66]) and Post-implementation (OR=2.30 [95% CI 1.07 to 4.92]) periods. For patients with a final diagnosis of stroke who had been dispatched as stroke there was a marginally non-significant 2.8 minutes (95% CI −0.2 to 5.9 minutes, p=0.068) reduction between Pre- and Post-implementation periods from call to arrival of the ambulance at scene. Conclusions This is the first study to develop, implement and evaluate the impact of a training package for EMDs with the aim of improving the recognition of stroke. Training led to a significant increase in the proportion of stroke patients dispatched as such by EMDs; a small reduction in time from call to arrival at scene by the ambulance also
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…
GPS Sensor Web Time Series Analysis Using SensorGrid Technology
NASA Astrophysics Data System (ADS)
Granat, R.; Pierce, M.; Aydin, G.; Qi, Z.
2006-12-01
We present a method for performing signal detection and classification on real-time streams of GPS sensor web data. Our approach has two parts. The first is a hidden Markov model fitting methodology that enables us to robustly describe the statistics of the data. The second is the SensorGrid technology which allows us to manage the data streams through a series of filters tied together with a publish/subscribe messaging system. In this framework, the HMM algorithm is viewed as a filter. The sensor web data we use in this work comes from the Southern California Integrated GPS Network (SCIGN), which produces a number of data products. In this work, we use the real-time (1Hz for most stations) three-dimensional position information. This data is collected from a system which is not only noisy but also poorly understood; driving forces on the system derive not only from the physical processes of the solid earth but also from external factors, including atmospheric effects and human activity. Fitting an HMM to time series allows us to describe the statistics of the data in a simple way that ascribes discrete modes of behavior to the system. By matching incoming data against the statistics of previously learned modes, we can perform classification according to the best match. In addition, we can perform signal detection across the entire sensor web by correlating mode changes in time; a significant number of mode changes across the network or within a certain sub-network is an indication of an event that is occurring over a wide geographical area. For most applications, reliable HMM fitting results are achieved by using a priori information to form constraints that reduce the number of free parameters. For GPS data, however, this information is not available as the underlying system is not well understood. As a result, we use the regularized deterministic annealing expectation-maximization (RDAEM) algorithm to perform the fit. This method provides high-quality, self
Analysis of MODIS snow cover time series over the alpine regions as input for hydrological modeling
NASA Astrophysics Data System (ADS)
Notarnicola, Claudia; Rastner, Philipp; Irsara, Luca; Moelg, Nico; Bertoldi, Giacomo; Dalla Chiesa, Stefano; Endrizzi, Stefano; Zebisch, Marc
2010-05-01
Snow extent and relative physical properties are key parameters in hydrology, weather forecast and hazard warning as well as in climatological models. Satellite sensors offer a unique advantage in monitoring snow cover due to their temporal and spatial synoptic view. The Moderate Resolution Imaging Spectrometer (MODIS) from NASA is especially useful for this purpose due to its high frequency. However, in order to evaluate the role of snow on the water cycle of a catchment such as runoff generation due to snowmelt, remote sensing data need to be assimilated in hydrological models. This study presents a comparison on a multi-temporal basis between snow cover data derived from (1) MODIS images, (2) LANDSAT images, and (3) predictions by the hydrological model GEOtop [1,3]. The test area is located in the catchment of the Matscher Valley (South Tyrol, Northern Italy). The snow cover maps derived from MODIS-images are obtained using a newly developed algorithm taking into account the specific requirements of mountain regions with a focus on the Alps [2]. This algorithm requires the standard MODIS-products MOD09 and MOD02 as input data and generates snow cover maps at a spatial resolution of 250 m. The final output is a combination of MODIS AQUA and MODIS TERRA snow cover maps, thus reducing the presence of cloudy pixels and no-data-values due to topography. By using these maps, daily time series starting from the winter season (November - May) 2002 till 2008/2009 have been created. Along with snow maps from MODIS images, also some snow cover maps derived from LANDSAT images have been used. Due to their high resolution (< 30 m) they have been considered as an evaluation tool. The snow cover maps are then compared with the hydrological GEOtop model outputs. The main objectives of this work are: 1. Evaluation of the MODIS snow cover algorithm using LANDSAT data 2. Investigation of snow cover, and snow cover duration for the area of interest for South Tyrol 3. Derivation
NASA Astrophysics Data System (ADS)
Hong, S.; Kim, J.; Lin, S.; yun, H.; Seo, H.; Choi, Y.
2013-12-01
Mt. Baekdu (also known as Changbai in Chinese) is a volcanic mountain located on the border between North Korea and China. It made one of the most destructive eruptions in the recorded history around 1000 A.D. This eruption was estimated to produce explosive Volcanic Explosivity Index (VEI) 7 eruption (Yin et al., 2012) which was comparable to Mt. Tambora's eruption. Since making minor eruption in 1702 A.D. as clearly stated in the Korean history, the Mt. Baekdu has been dormant. With continuous monitoring over Mt. Baekdu (Xu et al., 2012), it is evident that the frequencies of earthquakes and gas emission were increasing. The results showed important precursors of volcanic activation, including: (1) Strong seismic activities especially from 2002 to 2006; (2) Abnormal gas emissions in three hot springs around the summit from 2002 to 2006; (3) Strong vertical uplift during 2002 to 2005 and horizontal displacement away from Caldera Lake observed using GPS data; (4) A number of abnormal thermal activities in hot springs; (5) Surface deflation indicating new magma activities at the western and northern slopes from 2009. Therefore, it is realized that periodic magma activities are underway beneath Mt. Baekdu from the ground observations. In addition to such short-term campaigns applied on discrete observation stations, a comprehensive monitoring covering overall extent of Mt. Baekdu was further proposed. The Differential Interferometric Synthetic Aperture Radar (DInSAR) technique employing a series of remote sensed SAR phase angle difference was applied to address the difficulty for direct access to the border area due to political situation in this study. In order to deal with the harsh environmental conditions which might limit a successful D-InSAR processing over Mt. Baekdu, e.g. water vapor, vegetation canopy and steep slope around summit, StaMPS/MTI (Stanford Method for Persistent Scatterers/Multi-Temporal InSAR) approach for detecting time series deformation
NASA Astrophysics Data System (ADS)
Tarafdar, Sujata; Harper, David
2008-01-01
Lake Naivasha in Kenya is an important natural fresh water reserve, supporting surrounding wildlife as well as agriculture and industry. Uncontrolled use of the lake water for the past few decades is causing concern for environmentalists. In the present paper, fluctuations in the lake level for the last half century are analysed using standard tools for time-series analysis. The intervals 1951-1980 (period I) and 1981-2000 (period II) are treated separately, to look for any difference in their statistical patterns. From period II onwards, increased human consumption is believed to affect the level significantly. We analyse the data using three different approaches: (i) rescaled range analysis (R/S), (ii) roughness scaling analysis and (iii) a Lomb periodogram. R/S analysis shows no difference between the behavior in periods I and II, but the other methods reveal different fluctuation patterns for the two periods. The water level shows stronger fluctuations in period I compared to II. R/S analysis, however, shows an interesting anti-persistence with a Hurst exponent 0.44, which is not usually observed in natural time series.
NASA Astrophysics Data System (ADS)
Sohrabinia, M.; Rack, W.; Zawar-Reza, P.
2012-07-01
The objective of this analysis is to provide a quantitative estimate of the fluctuations of land surface temperature (LST) with varying near surface soil moisture (SM) on different land-cover (LC) types. The study area is located in the Canterbury Plains in the South Island of New Zealand. Time series of LST from the MODerate resolution Imaging Spectro-radiometer (MODIS) have been analysed statistically to study the relationship between the surface skin temperature and near-surface SM. In-situ measurements of the skin temperature and surface SM with a quasi-experimental design over multiple LC types are used for validation. Correlations between MODIS LST and in-situ SM, as well as in-situ surface temperature and SM are calculated. The in-situ measurements and MODIS data are collected from various LC types. Pearson's r correlation coefficient and linear regression are used to fit the MODIS LST and surface skin temperature with near-surface SM. There was no significant correlation between time-series of MODIS LST and near-surface SM from the initial analysis, however, careful analysis of the data showed significant correlation between the two parameters. Night-time series of the in-situ surface temperature and SM from a 12 hour period over Irrigated-Crop, Mixed-Grass, Forest, Barren and Open- Grass showed inverse correlations of -0.47, -0.68, -0.74, -0.88 and -0.93, respectively. These results indicated that the relationship between near-surface SM and LST in short-terms (12 to 24 hours) is strong, however, remotely sensed LST with higher temporal resolution is required to establish this relationship in such time-scales. This method can be used to study near-surface SM using more frequent LST observations from a geostationary satellite over the study area.
Bos, Elisabeth Henriette; Hoenders, Rogier; de Jonge, Peter
2012-01-01
Time-series analysis was used to study the associations between daily weather variables and symptomatology in a man suffering from recurrent anxiety. Outcome measures were the patient's main symptoms: anxiety and energy. Wind direction appeared to be related to the patient's energy levels; these were significantly lower when the wind blew from the southeast. This effect could not be explained by other weather parameters. Decreases in energy in turn predicted increases in anxiety. The reverse effect was observed as well, with increases in anxiety predicting decreases in energy, indicating a positive feedback loop. PMID:22684840
Wright, Nat M J; Roberts, Alison J; Allgar, Victoria L; Tompkins, Charlotte N E; Greenwood, Darren C; Laurence, Gillian
2004-01-01
In December 2000, the Committee for Safety of Medicines (CSM) advised that thioridazine may prolong QT intervals risking arrhythmias. We investigated the impact on general practitioner prescribing of thioridazine using a time series analysis. Numbers of items and costs of antipsychotics and benzodiazepines prescribed in Leeds from May 1999 until April 2002 were collated. Post-advice, thioridazine prescriptions dropped by 810 items per month (95% confidence interval = 420 to 1200, P<0.001) but others increased slightly in response. Costs mimicked these changes. Fresh criteria are proposed for appraising the quality of evidence needed to inform future urgent facsimile transmissions. PMID:15113522
NASA Astrophysics Data System (ADS)
Vincent, P.; Buckley, S. M.; Yang, D.; Carle, S. F.
2011-12-01
Anomalous uplift is observed at the Lop Nor, China nuclear test site using ERS satellite SAR data. Using an InSAR time-series analysis method, we show that an increase in absolute uplift with time is observed between 1997 and 1999. The signal is collocated with past underground nuclear tests. Due to the collocation in space with past underground tests we postulate a nuclear test-related hydrothermal source for the uplift signal. A possible mechanism is presented that can account for the observed transient uplift and is consistent with documented thermal regimes associated with underground nuclear tests conducted at the Nevada National Security Site (NNSS) (formerly the Nevada Test Site).
Sreeharan, Vaishnavee; Madden, Hugo; Lee, John Tayu; Millett, Christopher; Majeed, Azeem
2013-01-01
Background Antidepressant prescribing rates in England have been increasing since the 1970s. The impact of the Improving Access to Psychological Therapies (IAPT) initiative on antidepressant prescribing rates is unknown. Aim To investigate the impact of the establishment of IAPT services on antidepressant prescribing rates in primary care trusts (PCTs) in England. Design and setting A longitudinal time-series analysis, using PCT-level data from 2008 to 2011 set in England. Method A time-series analysis was conducted using PCT-level prescription data, dates of establishment of IAPT services, and covariate data for age, sex, and socioeconomic status. Statistical analysis was carried out using analysis of variance and a random-effect negative binomial model. Results Antidepressant prescribing rates in England increased by 10% per year during the study period (adjusted rate ratio = 1.10, 95% CI = 1.09 to 1.10). The implementation of IAPT services had no significant effect on antidepressant prescribing (adjusted rate ratio = 0.99, 95% CI = 0.99 to 1.00). Conclusion Introduction of a large-scale initiative to increase provision of psychological therapies has not curbed the long-term increased prescribing of antidepressants in England. PMID:23998846
NASA Astrophysics Data System (ADS)
Koeppen, W. C.; Wright, R.; Pilger, E.
2009-12-01
We developed and tested a new, automated algorithm, MODVOLC2, which analyzes thermal infrared satellite time series data to detect and quantify the excess energy radiated from thermal anomalies such as active volcanoes, fires, and gas flares. MODVOLC2 combines two previously developed algorithms, a simple point operation algorithm (MODVOLC) and a more complex time series analysis (Robust AVHRR Techniques, or RAT) to overcome the limitations of using each approach alone. MODVOLC2 has four main steps: (1) it uses the original MODVOLC algorithm to process the satellite data on a pixel-by-pixel basis and remove thermal outliers, (2) it uses the remaining data to calculate reference and variability images for each calendar month, (3) it compares the original satellite data and any newly acquired data to the reference images normalized by their variability, and it detects pixels that fall outside the envelope of normal thermal behavior, (4) it adds any pixels detected by MODVOLC to those detected in the time series analysis. Using test sites at Anatahan and Kilauea volcanoes, we show that MODVOLC2 was able to detect ~15% more thermal anomalies than using MODVOLC alone, with very few, if any, known false detections. Using gas flares from the Cantarell oil field in the Gulf of Mexico, we show that MODVOLC2 provided results that were unattainable using a time series-only approach. Some thermal anomalies (e.g., Cantarell oil field flares) are so persistent that an additional, semi-automated 12-µm correction must be applied in order to correctly estimate both the number of anomalies and the total excess radiance being emitted by them. Although all available data should be included to make the best possible reference and variability images necessary for the MODVOLC2, we estimate that at least 80 images per calendar month are required to generate relatively good statistics from which to run MODVOLC2, a condition now globally met by a decade of MODIS observations. We also found
Statistics for Time-Series Spatial Data: Applying Survival Analysis to Study Land-Use Change
ERIC Educational Resources Information Center
Wang, Ninghua Nathan
2013-01-01
Traditional spatial analysis and data mining methods fall short of extracting temporal information from data. This inability makes their use difficult to study changes and the associated mechanisms of many geographic phenomena of interest, for example, land-use. On the other hand, the growing availability of land-change data over multiple time…
The Use of Time Series Analysis and t Tests with Serially Correlated Data Tests.
ERIC Educational Resources Information Center
Nicolich, Mark J.; Weinstein, Carol S.
1981-01-01
Results of three methods of analysis applied to simulated autocorrelated data sets with an intervention point (varying in autocorrelation degree, variance of error term, and magnitude of intervention effect) are compared and presented. The three methods are: t tests; maximum likelihood Box-Jenkins (ARIMA); and Bayesian Box Jenkins. (Author/AEF)
PlanetPack: Radial-velocity time-series analysis tool
NASA Astrophysics Data System (ADS)
Baluev, Roman V.
2013-11-01
PlanetPack facilitates and standardizes the advanced analysis of radial velocity (RV) data for the goal of exoplanets detection, characterization, and basic dynamical N-body simulations. PlanetPack is a command-line interpreter that can run either in an interactive mode or in a batch mode of automatic script interpretation.
NASA Astrophysics Data System (ADS)
Park, S.
2015-04-01
The spatiotemporal influences of climatic factors and atmospheric aerosol on vegetative phenological cycles of the Korean Peninsula was analysed based on four major forest types. High temporal-resolution satellite data can overcome limitations of ground-based phenological studies with reasonable spatial resolution. Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index (VI) (MOD13Q1 and MYD13Q1) and aerosol (MOD04_D3) data were downloaded from the USGS Earth Observation and Science (EROS) Data Center and NASA Goddard Space Flight Center. Harmonic analysis was used to describe and compare the periodic phenomena of the vegetative phenology and atmospheric aerosol optical thickness (AOT). The method transforms complex timeseries to a sum of various sinusoidal functions, or harmonics. Each harmonic curve, or term (or Fourier series), from time-series data us defined by a unique amplitude and a phase, indicating the half of the height and the peak time of a curve. Therefore, the mean, phase, and amplitude of harmonic terms of the data provided the temporal relationships between AOT and VI time series. The phenological characteristics of evergreen forest, deciduous forest, and grassland were similar to each other, but the inter-annual VI amplitude of mixed forest was differentiated from the other forest types. Overall, forests with high VI amplitude reached their maximum greenness earlier, and the phase of VI, or the peak time of greenness, was significantly influenced by air temperature. AOT time-series showed strong seasonal and inter-annual variations. Generally, aerosol concentrations were peaked during late spring and early summer. However, inter-annual AOT variations did not have significant relationships with those of VI. Weak relationships between inter-annual AOT and VI variations indicate that the impacts of aerosols on vegetation growth may be limited for the temporal scale investigated in the region.
NASA Astrophysics Data System (ADS)
Barreyre, T.; Sohn, R. A.; Crone, T. J.
2014-12-01
Time-series records of mid-ocean ridge hydrothermal fluid properties and flow rates have the potential to help constrain the hydrogeology, subsurface circulation patterns, heat, mass, and chemical fluxes, and habitat conditions within young oceanic crust. This potential has motivated a concerted international effort to acquire such records from a variety of geologically distinct vent fields at numerous locations along the mid-ocean ridge system. However up until now, the global database has not been systematically explored. These records have only been analyzed in a piecemeal fashion, which is problematic because hydrothermal time-series records from individual sites typically exhibit enigmatic modes of episodic and periodic variability that are difficult to interpret in isolation. In this study, we conduct a systematic analysis of the extant set of hydrothermal time-series records from several mid-ocean ridge sites where observatory-style experiments have been conducted (including, LSHF, TAG, EPR 9°50'N and MEF). We show that most temperature records, regardless of location or geological setting, display systematic tide-related variability, with the strongest signal at the principal semidiurnal tidal periods (M2, S2, N2 and K2). Cross-spectral multi-taper methods applied to the temperature and bottom pressure records reveal robust phase relationships, particularly for the high-temperature, black-smoker records, as predicted by poroelastic theory. These results suggest that tidal pressures diffusely propagate through the formation, perturbing fluid velocities and temperatures, resulting in phase lags between the seafloor loading and the exit-fluid temperatures. Here, we use multi-layer analytical and numerical models to constrain the subseafloor permeability, skin depth, and Darcy velocities required to explain the phase lag observations.
NASA Astrophysics Data System (ADS)
van der Voort, Tessa Sophia; Hagedorn, Frank; Zell, Claudia; McIntyre, Cameron; Eglinton, Tim
2016-04-01
Understanding the interaction between soil organic matter (SOM) and climatic, geologic and ecological factors is essential for the understanding of potential susceptibility and vulnerability to climate and land use change. Radiocarbon constitutes a powerful tool for unraveling SOM dynamics and is increasingly used in studies of carbon turnover. The complex and inherently heterogeneous nature of SOM renders it challenging to assess the processes that govern SOM stability by solely looking at the bulk signature on a plot-scale level. This project combines bulk radiocarbon measurements on a regional-scale spanning wide climatic and geologic gradients with a more in-depth approach for a subset of locations. For this subset, time-series and carbon pool-specific radiocarbon data has been acquired for both topsoil and deeper soils. These well-studied sites are part of the Long-Term Forest Ecosystem Research (LWF) program of the Swiss Federal Institute for Forest, Snow and Landscape research (WSL). Statistical analysis was performed to examine relationships of radiocarbon signatures with variables such as temperature, precipitation and elevation. Bomb-curve modeling was applied determine carbon turnover using time-series data. Results indicate that (1) there is no significant correlation between Δ14C signature and environmental conditions except a weak positive correlation with mean annual temperature, (2) vertical gradients in Δ14C signatures in surface and deeper soils are highly similar despite covering disparate soil-types and climatic systems, and (3) radiocarbon signatures vary significantly between time-series samples and carbon pools. Overall, this study provides a uniquely comprehensive dataset that allows for a better understanding of links between carbon dynamics and environmental settings, as well as for pool-specific and long-term trends in carbon (de)stabilization.
Time-series analysis of chemical trends in a dated ice core from Antarctica
Keskin, S.S.; Olmez, I.; Langway, C.C. Jr.
1994-12-31
Polar ice sheets contain valuable information about past atmospheric conditions. Atmospherically produced or transported substances from natural and anthropogenic sources are preserved stratigraphically within the ice layers as a result of both wet and dry deposition mechanisms. Substances deposited include aerosols and gaseous compounds. The analysis of trace elements contained in dated annual snow layers provides a measure of the elemental chemistry content of the atmosphere during the same time interval. The aerosol content of the atmosphere and ice sheets is one of the most important parameters for cloud/radiation interaction processes. Ice cores were obtained from the Byrd Station, West Antarctica, in November, 1989. This study presents results obtained from instrumental neutron activation analysis and ion chromatography on 30 samples over a 20 year period.
A time-series analysis framework for the flood-wave method to estimate groundwater model parameters
NASA Astrophysics Data System (ADS)
Obergfell, Christophe; Bakker, Mark; Maas, Kees
2016-06-01
The flood-wave method is implemented within the framework of time-series analysis to estimate aquifer parameters for use in a groundwater model. The resulting extended flood-wave method is applicable to situations where groundwater fluctuations are affected significantly by time-varying precipitation and evaporation. Response functions for time-series analysis are generated with an analytic groundwater model describing stream-aquifer interaction. Analytical response functions play the same role as the well function in a pumping test, which is to translate observed head variations into groundwater model parameters by means of a parsimonious model equation. An important difference as compared to the traditional flood-wave method and pumping tests is that aquifer parameters are inferred from the combined effects of precipitation, evaporation, and stream stage fluctuations. Naturally occurring fluctuations are separated in contributions from different stresses. The proposed method is illustrated with data collected near a lowland river in the Netherlands. Special emphasis is put on the interpretation of the streambed resistance. The resistance of the streambed is the result of stream-line contraction instead of a semi-pervious streambed, which is concluded through comparison with the head loss calculated with an analytical two-dimensional cross-section model.
NASA Astrophysics Data System (ADS)
Scotch, C. G.; Murgulet, D.; Hay, R.
2013-12-01
Although surface-water and groundwater are often referred to as separate domains, they are intimately related as a change in one domain can ultimately affect the other domain. Since the two domains act as linked pathways for contaminant transport in the hydrologic cycle a comprehensive understanding of this relationship is essential for improved SW-GW management practices. The main objective of this study is to develop new statistical methods to better identify and characterize the advective component or water movement between SW-GW in a coastal area along the South Texas coast, adjacent to the Gulf of Mexico (GOM) margin, characterized by low gradients and low-conductivity stream beds. Identifying advection zones using temperature data in regions with low topographic relief and numerous small-scale flow paths is difficult. To overcome this challenge this study proposes the use of seasonal-trend decomposition (STL) of time series temperature data to analyze exchanges in this type of environment. Seasonal decomposition analysis was used to remove the daily and annual cyclic components leaving the random or non-cyclic component. It can be inferred that high variances of the random component indicate periods of advection. This statistically-derived advective component correlates well with advection periods identified from the conventional time-series temperature profile analysis. This correlation is a good validation of the statistical approach as means of identifying periods of advection and SW-GW interaction. Electrical resistivity imaging will be used for validation of the statistical model.
Greaves, Stephen P.
2015-01-01
Type 2 diabetes is known to be associated with environmental, behavioral, and lifestyle factors. However, the actual impacts of these factors on blood glucose (BG) variation throughout the day have remained relatively unexplored. Continuous blood glucose monitors combined with human activity tracking technologies afford new opportunities for exploration in a naturalistic setting. Data from a study of 40 patients with diabetes is utilized in this paper, including continuously monitored BG, food/medicine intake, and patient activity/location tracked using global positioning systems over a 4-day period. Standard linear regression and more disaggregated time-series analysis using autoregressive integrated moving average (ARIMA) are used to explore patient BG variation throughout the day and over space. The ARIMA models revealed a wide variety of BG correlating factors related to specific activity types, locations (especially those far from home), and travel modes, although the impacts were highly personal. Traditional variables related to food intake and medications were less often significant. Overall, the time-series analysis revealed considerable patient-by-patient variation in the effects of geographic and daily lifestyle factors. We would suggest that maps of BG spatial variation or an interactive messaging system could provide new tools to engage patients and highlight potential risk factors. PMID:25893201
Doherty, Sean T; Greaves, Stephen P
2015-01-01
Type 2 diabetes is known to be associated with environmental, behavioral, and lifestyle factors. However, the actual impacts of these factors on blood glucose (BG) variation throughout the day have remained relatively unexplored. Continuous blood glucose monitors combined with human activity tracking technologies afford new opportunities for exploration in a naturalistic setting. Data from a study of 40 patients with diabetes is utilized in this paper, including continuously monitored BG, food/medicine intake, and patient activity/location tracked using global positioning systems over a 4-day period. Standard linear regression and more disaggregated time-series analysis using autoregressive integrated moving average (ARIMA) are used to explore patient BG variation throughout the day and over space. The ARIMA models revealed a wide variety of BG correlating factors related to specific activity types, locations (especially those far from home), and travel modes, although the impacts were highly personal. Traditional variables related to food intake and medications were less often significant. Overall, the time-series analysis revealed considerable patient-by-patient variation in the effects of geographic and daily lifestyle factors. We would suggest that maps of BG spatial variation or an interactive messaging system could provide new tools to engage patients and highlight potential risk factors. PMID:25893201
The GPS Analysis Package for Exploration and Understanding of Geodetic Sensor Web Time Series Data
NASA Astrophysics Data System (ADS)
Granat, R. A.; Moghaddam, B.; Donnellan, A.
2012-12-01
We introduce the GPS Analysis Package (GAP), a Matlab toolbox for GPS data exploration and understanding. The toolbox is designed to support scientists and engineers studying the motion of the solid Earth both in an academic environment and in the course of NASA missions such as UAVSAR and future InSAR satellite missions. It includes an ensemble of low-level routines to perform basic signal processing operations, such as removal of secular motion, de-noising, and removal of seasonal signals. It also includes a suite of more sophisticated statistical pattern recognition techniques, including hidden Markov models and Bayes nets, to detect changes, identify transient signals, understand regional motion, and uncover relationships between geographically removed nodes in the GPS network. Finally, it provides an assortment of methods for estimating missing observations in the network. We provide usage examples of the package applied to particular scenarios, including the 2010 El Mayor-Cucapah earthquake, the 2011 Tohoku-Oki earthquake, and ongoing slow slip events in the Cascadia region. We also demonstrate the utility of the package within a web portal and web services environment by showcasing its use in the QuakeSim web portal. The QuakeSim portal allows easy access to GPS data sources provided by multiple institutions as well as a map and plotting interface to quickly assess analysis results. Finally, we show the extensibility of the package to other problem domains and sensor network data sources, demonstrating the analysis tools as applied to seismic network data, autonomous robotic navigation, and fault detection in engineering data streams from the International Space Station.
EMCS and time-series energy data analysis in a large government office building
Piette, Mary Ann; Kinney, Satkartar; Friedman, Hannah
2001-04-01
Energy Management Control System (EMCS) data are an underutilized source of information on the performance of commercial buildings. Newer EMCS's have the ability and storage capacity to trend large amounts of data and perform preliminary analyses; however, these features often receive little or no use, as operators are generally not trained in data management, visualization, and analysis. Whole-building hourly electric-utility data are another readily available and underutilized source of information. This paper outlines the use of EMCS and utility data to evaluate the performance of the Ronald V. Dellums Federal Building in Oakland, California, a large office building operated by the Federal General Services Administration (GSA). The project began as an exploratory effort at Lawrence Berkeley National Laboratory (LBNL) to examine the procedures operators were using to obtain information and operate their buildings. Trending capabilities were available, but in limited use by the operators. LBNL worked with the building operators to use EMCS to trend one-minute data for over one-hundred points. Hourly electricity-use data were also used to understand usage patterns and peak demand. The paper describes LBNL's key findings in the following areas: Characterization of cooling plant operations; Characterization of economizer performance; Analysis of annual energy use and peak demand operations; Techniques, strengths, and shortcomings of EMCS data analysis; Future plans at the building for web-based remote monitoring and diagnostics. These findings have helped GSA develop strategies for peak demand reduction in this and other GSA buildings. Such activities are of great interest in California and elsewhere, where electricity reliability and demand are currently problematic. Overall, though the building's energy use is fairly low, significant energy savings are available by improving the existing EMCS control strategies.
NASA Astrophysics Data System (ADS)
Olofsson, Pontus; Holden, Christopher E.; Bullock, Eric L.; Woodcock, Curtis E.
2016-06-01
Land cover and land change were monitored continuously between 1985 and 2011 at 30 m resolution across New England in the Northeastern United States in support of modeling the terrestrial carbon budget. It was found that the forest area has been decreasing throughout the study period in each state of the region since the 1980s. A total of 386 657 ± 98 137 ha (95% confidence interval) of forest has been converted to other land covers since 1985. Mainly driven by low density residential development, the deforestation accelerated in the mid-1990s until 2007 when it plateaued as a result of declining new residential construction and in turn, the financial crisis of 2007–08. The area of forest harvest, estimated at 226 519 ± 66 682 ha, was mapped separately and excluded from the deforestation estimate, while the area of forest expansion on non-forested lands was found to not be significantly different from zero. New England is often held as a principal example of a forest transition with historical widespread deforestation followed by recovery of forestlands as farming activities diminished, but the results of this study support the notion of a reversal of the forest transition as the region again is experiencing widespread deforestation. All available Landsat imagery acquired after 1985 for the study area were collected and used in the analysis. Areas of land cover and land change were estimated from a random sample of reference observations stratified by a twelve-class land change map encompassing the entire study area and period. The statistical analysis revealed that the net change in forest area and the associated modeled impact on the terrestrial carbon balance would have been considerably different if the results of the map were used without inferring the area of forest change by analysis of a reference sample.
Time-series network analysis of civil aviation in Japan (1985-2005)
NASA Astrophysics Data System (ADS)
Michishita, Ryo; Xu, Bing; Yamada, Ikuho
2008-10-01
Due to the airline deregulation in 1985, a series of new airport developments in the 1990s and 2000s, and the reorganization of airline companies in the 2000s, Japan's air passenger transportation has been dramatically altered in the last two decades in many ways. In this paper, the authors examine how the network and flow structures of domestic air passenger transportation in Japan have geographically changed since 1985. For this purpose, passenger flow data in 1985, 1995, and 2005 were extracted from the Air Transportation Statistical Survey conducted by the Ministry of Land, Infrastructure and Transport, Japan. First, national and regional hub airports are identified via dominant flow and hub function analysis. Then the roles of the hub airports and individual connections over the network are examined with respect to their spatial and network autocorrelations. Spatial and network autocorrelations were evaluated both globally and locally using Moran's I and LISA statistics. The passenger flow data were first examined as a whole and then divided into 3 airline-based categories. Dominant flow and hub function enabled us to detect the hub airports. Structural processes of the hub-and-spoke network were confirmed in each airline through spatial autocorrelation analysis. Network autocorrelation analysis showed that all airlines ingeniously optimized their networks by connecting their routes with large numbers of passengers to other routes with large numbers of passengers, and routes with small numbers of passengers to other routes with small numbers of passengers. The effects of political events and the changes in the strategies of each airline on the whole networks were strongly reflected in the results of this study.
Hutchinson, J M S; Jacquin, A; Hutchinson, S L; Verbesselt, J
2015-03-01
Given the significant land holdings of the U.S. Department of Defense, and the importance of those lands to support a variety of inherently damaging activities, application of sound natural resource conservation principles and proactive monitoring practices are necessary to manage military training lands in a sustainable manner. This study explores a method for, and the utility of, analyzing vegetation condition and trends as sustainability indicators for use by military commanders and land managers, at both the national and local levels, in identifying when and where vegetation-related environmental impacts might exist. The BFAST time series decomposition method was applied to a ten-year MODIS NDVI time series dataset for the Fort Riley military installation and Konza Prairie Biological Station (KPBS) in northeastern Kansas. Imagery selected for time-series analysis were 16-day MODIS NDVI (MOD13Q1 Collection 5) composites capable of characterizing vegetation change induced by human activities and climate variability. Three indicators related to gradual interannual or abrupt intraannual vegetation change for each pixel were calculated from the trend component resulting from the BFAST decomposition. Assessment of gradual interannual NDVI trends showed the majority of Fort Riley experienced browning between 2001 and 2010. This result is supported by validation using high spatial resolution imagery. The observed versus expected frequency of linear trends detected at Fort Riley and KPBS were significantly different and suggest a causal link between military training activities and/or land management practices. While both sites were similar with regards to overall disturbance frequency and the relative spatial extents of monotonic or interrupted trends, vegetation trajectories after disturbance were significantly different. This suggests that the type and magnitude of disturbances characteristic of each location result in distinct post-disturbance vegetation responses
Time-series metagenomic analysis reveals robustness of soil microbiome against chemical disturbance
Kato, Hiromi; Mori, Hiroshi; Maruyama, Fumito; Toyoda, Atsushi; Oshima, Kenshiro; Endo, Ryo; Fuchu, Genki; Miyakoshi, Masatoshi; Dozono, Ayumi; Ohtsubo, Yoshiyuki; Nagata, Yuji; Hattori, Masahira; Fujiyama, Asao; Kurokawa, Ken; Tsuda, Masataka
2015-01-01
Soil microbial communities have great potential for bioremediation of recalcitrant aromatic compounds. However, it is unclear which taxa and genes in the communities, and how they contribute to the bioremediation in the polluted soils. To get clues about this fundamental question here, time-course (up to 24 weeks) metagenomic analysis of microbial community in a closed soil microcosm artificially polluted with four aromatic compounds, including phenanthrene, was conducted to investigate the changes in the community structures and gene pools. The pollution led to drastic changes in the community structures and the gene sets for pollutant degradation. Complete degradation of phenanthrene was strongly suggested to occur by the syntrophic metabolism by Mycobacterium and the most proliferating genus, Burkholderia. The community structure at Week 24 (∼12 weeks after disappearance of the pollutants) returned to the structure similar to that before pollution. Our time-course metagenomic analysis of phage genes strongly suggested the involvement of the ‘kill-the-winner’ phenomenon (i.e. phage predation of Burkholderia cells) for the returning of the microbial community structure. The pollution resulted in a decrease in taxonomic diversity and a drastic increase in diversity of gene pools in the communities, showing the functional redundancy and robustness of the communities against chemical disturbance. PMID:26428854
Time Series Analysis of Monte Carlo Fission Sources - I: Dominance Ratio Computation
Ueki, Taro; Brown, Forrest B.; Parsons, D. Kent; Warsa, James S.
2004-11-15
In the nuclear engineering community, the error propagation of the Monte Carlo fission source distribution through cycles is known to be a linear Markov process when the number of histories per cycle is sufficiently large. In the statistics community, linear Markov processes with linear observation functions are known to have an autoregressive moving average (ARMA) representation of orders p and p - 1. Therefore, one can perform ARMA fitting of the binned Monte Carlo fission source in order to compute physical and statistical quantities relevant to nuclear criticality analysis. In this work, the ARMA fitting of a binary Monte Carlo fission source has been successfully developed as a method to compute the dominance ratio, i.e., the ratio of the second-largest to the largest eigenvalues. The method is free of binning mesh refinement and does not require the alteration of the basic source iteration cycle algorithm. Numerical results are presented for problems with one-group isotropic, two-group linearly anisotropic, and continuous-energy cross sections. Also, a strategy for the analysis of eigenmodes higher than the second-largest eigenvalue is demonstrated numerically.
Time-series metagenomic analysis reveals robustness of soil microbiome against chemical disturbance.
Kato, Hiromi; Mori, Hiroshi; Maruyama, Fumito; Toyoda, Atsushi; Oshima, Kenshiro; Endo, Ryo; Fuchu, Genki; Miyakoshi, Masatoshi; Dozono, Ayumi; Ohtsubo, Yoshiyuki; Nagata, Yuji; Hattori, Masahira; Fujiyama, Asao; Kurokawa, Ken; Tsuda, Masataka
2015-12-01
Soil microbial communities have great potential for bioremediation of recalcitrant aromatic compounds. However, it is unclear which taxa and genes in the communities, and how they contribute to the bioremediation in the polluted soils. To get clues about this fundamental question here, time-course (up to 24 weeks) metagenomic analysis of microbial community in a closed soil microcosm artificially polluted with four aromatic compounds, including phenanthrene, was conducted to investigate the changes in the community structures and gene pools. The pollution led to drastic changes in the community structures and the gene sets for pollutant degradation. Complete degradation of phenanthrene was strongly suggested to occur by the syntrophic metabolism by Mycobacterium and the most proliferating genus, Burkholderia. The community structure at Week 24 (∼12 weeks after disappearance of the pollutants) returned to the structure similar to that before pollution. Our time-course metagenomic analysis of phage genes strongly suggested the involvement of the 'kill-the-winner' phenomenon (i.e. phage predation of Burkholderia cells) for the returning of the microbial community structure. The pollution resulted in a decrease in taxonomic diversity and a drastic increase in diversity of gene pools in the communities, showing the functional redundancy and robustness of the communities against chemical disturbance. PMID:26428854
Oomens, Wouter; Maes, Joseph H. R.; Hasselman, Fred; Egger, Jos I. M.
2015-01-01
The concept of executive functions plays a prominent role in contemporary experimental and clinical studies on cognition. One paradigm used in this framework is the random number generation (RNG) task, the execution of which demands aspects of executive functioning, specifically inhibition and working memory. Data from the RNG task are best seen as a series of successive events. However, traditional RNG measures that are used to quantify executive functioning are mostly summary statistics referring to deviations from mathematical randomness. In the current study, we explore the utility of recurrence quantification analysis (RQA), a non-linear method that keeps the entire sequence intact, as a better way to describe executive functioning compared to traditional measures. To this aim, 242 first- and second-year students completed a non-paced RNG task. Principal component analysis of their data showed that traditional and RQA measures convey more or less the same information. However, RQA measures do so more parsimoniously and have a better interpretation. PMID:26097449
Oomens, Wouter; Maes, Joseph H R; Hasselman, Fred; Egger, Jos I M
2015-01-01
The concept of executive functions plays a prominent role in contemporary experimental and clinical studies on cognition. One paradigm used in this framework is the random number generation (RNG) task, the execution of which demands aspects of executive functioning, specifically inhibition and working memory. Data from the RNG task are best seen as a series of successive events. However, traditional RNG measures that are used to quantify executive functioning are mostly summary statistics referring to deviations from mathematical randomness. In the current study, we explore the utility of recurrence quantification analysis (RQA), a non-linear method that keeps the entire sequence intact, as a better way to describe executive functioning compared to traditional measures. To this aim, 242 first- and second-year students completed a non-paced RNG task. Principal component analysis of their data showed that traditional and RQA measures convey more or less the same information. However, RQA measures do so more parsimoniously and have a better interpretation. PMID:26097449
Enhancing dominant modes in nonstationary time series by means of the symbolic resonance analysis.
beim Graben, Peter; Drenhaus, Heiner; Brehm, Eva; Rhode, Bela; Saddy, Douglas; Frisch, Stefan
2007-12-01
We present the symbolic resonance analysis (SRA) as a viable method for addressing the problem of enhancing a weakly dominant mode in a mixture of impulse responses obtained from a nonlinear dynamical system. We demonstrate this using results from a numerical simulation with Duffing oscillators in different domains of their parameter space, and by analyzing event-related brain potentials (ERPs) from a language processing experiment in German as a representative application. In this paradigm, the averaged ERPs exhibit an N400 followed by a sentence final negativity. Contemporary sentence processing models predict a late positivity (P600) as well. We show that the SRA is able to unveil the P600 evoked by the critical stimuli as a weakly dominant mode from the covering sentence final negativity. PMID:18163770
Program for the analysis of time series. [by means of fast Fourier transform algorithm
NASA Technical Reports Server (NTRS)
Brown, T. J.; Brown, C. G.; Hardin, J. C.
1974-01-01
A digital computer program for the Fourier analysis of discrete time data is described. The program was designed to handle multiple channels of digitized data on general purpose computer systems. It is written, primarily, in a version of FORTRAN 2 currently in use on CDC 6000 series computers. Some small portions are written in CDC COMPASS, an assembler level code. However, functional descriptions of these portions are provided so that the program may be adapted for use on any facility possessing a FORTRAN compiler and random-access capability. Properly formatted digital data are windowed and analyzed by means of a fast Fourier transform algorithm to generate the following functions: (1) auto and/or cross power spectra, (2) autocorrelations and/or cross correlations, (3) Fourier coefficients, (4) coherence functions, (5) transfer functions, and (6) histograms.
Trend analysis of rainfall time series for Sindh river basin in India
NASA Astrophysics Data System (ADS)
Gajbhiye, Sarita; Meshram, Chandrashekhar; Mirabbasi, Rasoul; Sharma, S. K.
2015-06-01
The study of precipitation trends is critically important for a country like India whose food security and economy are dependent on the timely availability of water such as 83 % water used for agriculture sector, 12 % for industry sector and only 5 % for domestic sector. In this study, the historical rainfall data for the periods 1901-2002 and 1942-2002 of the Sindh river basin, India, were analysed for monthly, seasonal and annual trends. The conventional Mann-Kendall test (MK) and Mann-Kendall test (MMK), after the removal of the effect of all significant autocorrelation coefficients, and Sen's slope estimator were used to identify the trends. Kriging technique was used for interpolating the spatial pattern using Arc GIS 9.3. The analysis suggested significant increase in the trend of rainfall for seasonal and annual series in the Sindh basin during 1901-2002.
Trend analysis of rainfall time series for Sindh river basin in India
NASA Astrophysics Data System (ADS)
Gajbhiye, Sarita; Meshram, Chandrashekhar; Mirabbasi, Rasoul; Sharma, S. K.
2016-08-01
The study of precipitation trends is critically important for a country like India whose food security and economy are dependent on the timely availability of water such as 83 % water used for agriculture sector, 12 % for industry sector and only 5 % for domestic sector. In this study, the historical rainfall data for the periods 1901-2002 and 1942-2002 of the Sindh river basin, India, were analysed for monthly, seasonal and annual trends. The conventional Mann-Kendall test (MK) and Mann-Kendall test (MMK), after the removal of the effect of all significant autocorrelation coefficients, and Sen's slope estimator were used to identify the trends. Kriging technique was used for interpolating the spatial pattern using Arc GIS 9.3. The analysis suggested significant increase in the trend of rainfall for seasonal and annual series in the Sindh basin during 1901-2002.
Singular Spectrum Analysis for Astronomical Time Series: Constructing a Parsimonious Hypothesis Test
NASA Astrophysics Data System (ADS)
Greco, G.; Kondrashov, D.; Kobayashi, S.; Ghil, M.; Branchesi, M.; Guidorzi, C.; Stratta, G.; Ciszak, M.; Marino, F.; Ortolan, A.
We present a data-adaptive spectral method - Monte Carlo Singular Spectrum Analysis (MC-SSA) - and its modification to tackle astrophysical problems. Through numerical simulations we show the ability of the MC-SSA in dealing with 1/f β power-law noise affected by photon counting statistics. Such noise process is simulated by a first-order autoregressive, AR(1) process corrupted by intrinsic Poisson noise. In doing so, we statistically estimate a basic stochastic variation of the source and the corresponding fluctuations due to the quantum nature of light. In addition, MC-SSA test retains its effectiveness even when a significant percentage of the signal falls below a certain level of detection, e.g., caused by the instrument sensitivity. The parsimonious approach presented here may be broadly applied, from the search for extrasolar planets to the extraction of low-intensity coherent phenomena probably hidden in high energy transients.
Velocity field from twenty-two years of combined GPS daily coordinate time series analysis
NASA Astrophysics Data System (ADS)
Susilo, Meilano, Irwan; Abidin, Hasanuddin Z.; Sapiie, Benyamin; Efendi, Joni; Wijanarto, Antonius B.
2016-05-01
We uses survey mode (sGPS) and continuous GPS (cGPS) from Geospatial Information Agency, National Land Agency and Sumatran GPS Array (SUGAR) from 1993 - 2014 to derive new GPS velocities field in Indonesia region. In this study we quantify the velocities field from our GPS sites during this period. We reprocess and analyses our GPS data using the GAMIT/GLOBK 10.5 software suite. Our analysis reveals that the velocities field in Indonesia region has a distinctly pattern with the different direction and magnitude which represents the plates or blocks tectonic motion in Indonesia region. This information is useful for determines Indonesian deformation model to support the new Indonesian datum which called Sistem Referensi Geospasial Indonesia 2013 (Indonesian Geospatial Reference System 2013).
Detecting network modules in fMRI time series: a weighted network analysis approach.
Mumford, Jeanette A; Horvath, Steve; Oldham, Michael C; Langfelder, Peter; Geschwind, Daniel H; Poldrack, Russell A
2010-10-01
Many network analyses of fMRI data begin by defining a set of regions, extracting the mean signal from each region and then analyzing the correlations between regions. One essential question that has not been addressed in the literature is how to best define the network neighborhoods over which a signal is combined for network analyses. Here we present a novel unsupervised method for the identification of tightly interconnected voxels, or modules, from fMRI data. This approach, weighted voxel coactivation network analysis (WVCNA), is based on a method that was originally developed to find modules of genes in gene networks. This approach differs from many of the standard network approaches in fMRI in that connections between voxels are described by a continuous measure, whereas typically voxels are considered to be either connected or not connected depending on whether the correlation between the two voxels survives a hard threshold value. Additionally, instead of simply using pairwise correlations to describe the connection between two voxels, WVCNA relies on a measure of topological overlap, which not only compares how correlated two voxels are but also the degree to which the pair of voxels is highly correlated with the same other voxels. We demonstrate the use of WVCNA to parcellate the brain into a set of modules that are reliably detected across data within the same subject and across subjects. In addition we compare WVCNA to ICA and show that the WVCNA modules have some of the same structure as the ICA components, but tend to be more spatially focused. We also demonstrate the use of some of the WVCNA network metrics for assessing a voxel's membership to a module and also how that voxel relates to other modules. Last, we illustrate how WVCNA modules can be used in a network analysis to find connections between regions of the brain and show that it produces reasonable results. PMID:20553896
Detecting network modules in fMRI time series: A weighted network analysis approach
Mumford, Jeanette A; Horvath, Steve; Oldham, Michael C.; Langfelder, Peter; Geschwind, Daniel H.; Poldrack, Russell A
2010-01-01
Many network analyses of fMRI data begin by defining a set of regions, extracting the mean signal from each region and then analyzing the correlations between regions. One essential question that has not been addressed in the literature is how to best define the network neighborhoods over which a signal is combined for network analyses. Here we present a novel unsupervised method for the identification of tightly interconnected voxels, or modules, from fMRI data. This approach, weighted voxel coactivation network analysis (WVCNA) is based on a method that was originally developed to find modules of genes in gene networks. This approach differs from many of the standard network approaches in fMRI in that connections between voxels are described by a continuous measure, whereas typically voxels are considered to be either connected or not connected depending on whether the correlation between the two voxels survives a hard threshold value. Additionally, instead of simply using pairwise correlations to describe the connection between two voxels, WVCNA relies on a measure of topological overlap, which not only compares how correlated two voxels are, but also the degree to which the pair of voxels is highly correlated with the same other voxels. We demonstrate the use of WVCNA to parcellate the brain into a set of modules that are reliably detected across data within the same subject and across subjects. In addition we compare WVCNA to ICA and show that the WVCNA modules have some of the same structure as the ICA components, but tend to be more spatially focused. We also demonstrate the use of some of the WVCNA network metrics for assessing a voxel’s membership to a module and also how that voxel relates to other modules. Last, we illustrate how WVCNA modules can be used in a network analysis to find connections between regions of the brain and show that it produces reasonable results. PMID:20553896
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
NASA Astrophysics Data System (ADS)
Yin, Yi; Shang, Pengjian
2015-04-01
In this paper, we propose multiscale detrended cross-correlation analysis (MSDCCA) to detect the long-range power-law cross-correlation of considered signals in the presence of nonstationarity. For improving the performance and getting better robustness, we further introduce the empirical mode decomposition (EMD) to eliminate the noise effects and propose MSDCCA method combined with EMD, which is called MS-EDXA method, then systematically investigate the multiscale cross-correlation structure of the real traffic signals. We apply the MSDCCA and MS-EDXA methods to study the cross-correlations in three situations: velocity and volume on one lane, velocities on the present and the next moment and velocities on the adjacent lanes, and further compare their spectrums respectively. When the difference between the spectrums of MSDCCA and MS-EDXA becomes unobvious, there is a crossover which denotes the turning point of difference. The crossover results from the competition between the noise effects in the original signals and the intrinsic fluctuation of traffic signals and divides the plot of spectrums into two regions. In all the three case, MS-EDXA method makes the average of local scaling exponents increased and the standard deviation decreased and provides a relative stable persistent scaling cross-correlated behavior which gets the analysis more precise and more robust and improves the performance after noises being removed. Applying MS-EDXA method avoids the inaccurate characteristics of multiscale cross-correlation structure at the short scale including the spectrum minimum, the range for the spectrum fluctuation and general trend, which are caused by the noise in the original signals. We get the conclusions that the traffic velocity and volume are long-range cross-correlated, which is accordant to their actual evolution, while velocities on the present and the next moment and velocities on adjacent lanes reflect the strong cross-correlations both in temporal and
The Tracking Meteogram, an AWIPS II Tool for Time-Series Analysis
NASA Technical Reports Server (NTRS)
Burks, Jason Eric; Sperow, Ken
2015-01-01
A new tool has been developed for the National Weather Service (NWS) Advanced Weather Interactive Processing System (AWIPS) II through collaboration between NASA's Short-term Prediction Research and Transition (SPoRT) and the NWS Meteorological Development Laboratory (MDL). Referred to as the "Tracking Meteogram", the tool aids NWS forecasters in assessing meteorological parameters associated with moving phenomena. The tool aids forecasters in severe weather situations by providing valuable satellite and radar derived trends such as cloud top cooling rates, radial velocity couplets, reflectivity, and information from ground-based lightning networks. The Tracking Meteogram tool also aids in synoptic and mesoscale analysis by tracking parameters such as the deepening of surface low pressure systems, changes in surface or upper air temperature, and other properties. The tool provides a valuable new functionality and demonstrates the flexibility and extensibility of the NWS AWIPS II architecture. In 2014, the operational impact of the tool was formally evaluated through participation in the NOAA/NWS Operations Proving Ground (OPG), a risk reduction activity to assess performance and operational impact of new forecasting concepts, tools, and applications. Performance of the Tracking Meteogram Tool during the OPG assessment confirmed that it will be a valuable asset to the operational forecasters. This presentation reviews development of the Tracking Meteogram tool, performance and feedback acquired during the OPG activity, and future goals for continued support and extension to other application areas.
Analysis of the mass balance time series of glaciers in the Italian Alps
NASA Astrophysics Data System (ADS)
Carturan, L.; Baroni, C.; Brunetti, M.; Carton, A.; Dalla Fontana, G.; Salvatore, M. C.; Zanoner, T.; Zuecco, G.
2015-10-01
This work presents an analysis of the mass balance series of nine Italian glaciers, which were selected based on the length, continuity and reliability of observations. All glaciers experienced mass loss in the observation period, which is variable for the different glaciers and ranges between 10 and 47 years. The longest series display increasing mass loss rates, that were mainly due to increased ablation during longer and warmer ablation seasons. The mean annual mass balance (Ba) in the decade from 2004 to 2013 ranged from -1788 mm to -763 mm w.e. yr-1. Low-altitude glaciers with low elevation ranges are more out of balance than the higher, larger and steeper glaciers, which maintain residual accumulation areas in their upper reaches. The response of glaciers is mainly controlled by the combination of October-May precipitation and June-September temperature, but rapid geometric adjustments and atmospheric changes lead to modifications in their response to climatic variations. In particular, a decreasing correlation of Ba with the June-September temperature and an increasing correlation with October-May precipitation are observed for some glaciers. In addition, the October-May temperature tends to become significantly correlated with Ba, possibly indicating a decrease in the fraction of solid precipitation, and/or increased ablation, during the accumulation season. Because most of the monitored glaciers have no more accumulation area, their observations series are at risk due to their impending extinction, thus requiring a soon replacement.
Humeau, Anne; Mahé, Guillaume; Chapeau-Blondeau, François; Rousseau, David; Abraham, Pierre
2011-10-01
Processes regulating the cardiovascular system (CVS) are numerous. Each possesses several temporal scales. Their interactions lead to interdependences across multiple scales. For the CVS analysis, different multiscale studies have been proposed, mostly performed on heart rate variability signals (HRV) reflecting the central CVS; only few were dedicated to data from the peripheral CVS, such as laser Doppler flowmetry (LDF) signals. Very recently, a study implemented the first computation of multiscale entropy for LDF signals. A nonmonotonic evolution of multiscale entropy with two distinctive scales was reported, leading to a markedly different behavior from the one of HRV. Our goal herein is to confirm these results and to go forward in the investigations on origins of this behavior. For this purpose, 12 LDF signals recorded simultaneously on the two forearms of six healthy subjects are processed. This is performed before and after application of physiological scales-based filters aiming at isolating previously found frequency bands linked to physiological activities. The results obtained with signals recorded simultaneously on two different sites of each subject show a probable central origin for the nonmonotonic behavior. The filtering results lead to the suggestion that origins of the distinctive scales could be dominated by the cardiac activity. PMID:21712149
Analysis of the mass balance time series of glaciers in the Italian Alps
NASA Astrophysics Data System (ADS)
Carturan, Luca; Baroni, Carlo; Brunetti, Michele; Carton, Alberto; Dalla Fontana, Giancarlo; Salvatore, Maria Cristina; Zanoner, Thomas; Zuecco, Giulia
2016-03-01
This work presents an analysis of the mass balance series of nine Italian glaciers, which were selected based on the length, continuity and reliability of observations. All glaciers experienced mass loss in the observation period, which is variable for the different glaciers and ranges between 10 and 47 years. The longest series display increasing mass loss rates, which were mainly due to increased ablation during longer and warmer ablation seasons. The mean annual mass balance (Ba) in the decade from 2004 to 2013 ranged from -1788 to -763 mm w.e. yr-1. Low-altitude glaciers with low range of elevation are more out of balance than the higher, larger and steeper glaciers, which maintain residual accumulation areas in their upper reaches. The response of glaciers is mainly controlled by the combination of October-May precipitations and June-September temperatures, but rapid geometric adjustments and atmospheric changes lead to modifications in their response to climatic variations. In particular, a decreasing correlation of Ba with the June-September temperatures and an increasing correlation with October-May precipitations are observed for some glaciers. In addition, the October-May temperatures tend to become significantly correlated with Ba, possibly indicating a decrease in the fraction of solid precipitation, and/or increased ablation, during the accumulation season. Because most of the monitored glaciers have no more accumulation area, their observations series are at risk due to their impending extinction, thus requiring a replacement soon.
Time-series analysis of air pollution and cause-specific mortality.
Zmirou, D; Schwartz, J; Saez, M; Zanobetti, A; Wojtyniak, B; Touloumi, G; Spix, C; Ponce de León, A; Le Moullec, Y; Bacharova, L; Schouten, J; Pönkä, A; Katsouyanni, K
1998-09-01
Ten large European cities provided data on daily air pollution as well as mortality from respiratory and cardiovascular mortality. We used Poisson autoregressive models that controlled for trend, season, influenza epidemics, and meteorologic influences to assess the short-term effects of air pollution at each city. We then compared and pooled the city-specific results in a meta-analysis. The pooled relative risks of daily deaths from cardiovascular conditions were 1.02 [95% confidence interval (CI) = 1.01-1.04] for a 50 microg/m3 increment in the concentration of black smoke and 1.04 (95% CI = 1.01-1.06) for an increase in sulfur dioxide levels in western European cities. For respiratory diseases, these figures were 1.04 (95% CI = 1.02-1.07) and 1.05 (95% CI = 1.03-1.07), respectively. These associations were not found in the five central European cities. Eight-hour averages of ozone were also moderately associated with daily mortality in western European cities (relative risk = 1.02; 95% CI = 1.00-1.03 for cardiovascular conditions and relative risk = 1.06; 95% CI = 1.02-1.10 for respiratory conditions). Nitrogen dioxide did not show consistent relations with daily mortality. These results are similar to previously published data and add credence to the causal interpretation of these associations at levels of air pollution close to or lower than current European standards. PMID:9730027
Trend analysis of time-series phenology of North America derived from satellite data
Reed, B.C.
2006-01-01
Remote sensing information has been used in studies of the seasonal dynamics (phenology) of the land surface since the 1980s. While our understanding of remote sensing phenology is still in development, it is regarded as a key to understanding land-surface processes over large areas. Phenologic metrics, including start of season, end of season, duration of season, and seasonally integrated greenness, were derived from 8 km advanced very high resolution radiometer (AVHRR) data over North America spanning the years 1982-2003. Trend analysis was performed on annual summaries of the metrics to determine areas with increasing or decreasing growing season trends for the time period under study. Results show a trend toward earlier starts of season in limited areas of the mixed boreal forest, and a trend toward later end of season in well-defined areas of New England and southeastern Canada. Results in Saskatchewan, Canada, include a trend toward longer duration of season over a well-defined area, principally as a result of regional changes in land use practices. Changing seasonality appears to be an integrated response to a complex of factors, including climate change, but also, in many places, changes in land use practices. Copyright ?? 2006 by V. H. Winston & Son, Inc. All rights reserved.
Time series analysis of V 1794 Cygni long-term photometry
NASA Astrophysics Data System (ADS)
Jetsu, L.; Pelt, J.; Tuominen, I.
1999-11-01
Standard Johnson UBVRI photometry of V 1794 Cyg (HD199178) between 1975 and 1995 is analysed. Instead of the traditional constant period ephemeris, we determine the seasonal periodicities (Pphot) and the primary and secondary minima epochs (t_{min,1}, t_{min,2}) of the normalized UBVRI magnitudes using the three stage period analysis (TSPA) and complementary methods. Our t_{min,1} and t_{min,2} estimates with variable Pphot can adapt easily to both differential rotation and longitudinal activity migration. The seasonal Pphot are utilized in modelling the mean (M) and total amplitude (A) of contemporary light curves in UBVRI. TSPA reveals that the long-term M and A changes of V 1794 Cyg are unpredictable. We search for active longitudes from the t_{min,1} and t_{min,2} series of time points with nonparametric methods. The critical level of 0.0029 for the best 3.d3175 period detected with the Kuiper method is high, but exceeds the 0.001 significance for rejecting the hypothesis that the phases of t_{min,1} and t_{min,2} are randomly distributed. The activity centres in V 1794 Cyg are rarely disrupted, and most probably undergo continuous longitudinal migration, because only one abrupt disruption is observed during 20 years. As for differential rotation, the irregular changes of seasonal Pphot are 7.5%. The surprisingly regular 3.3% changes of yearly Pphot may provide a stellar analogy of the solar ``butterfly'' diagram.
Yu, Hwa-Lung; Lin, Yuan-Chien; Kuo, Yi-Ming
2015-09-01
Understanding the temporal dynamics and interactions of particulate matter (PM) concentration and composition is important for air quality control. This paper applied a dynamic factor analysis method (DFA) to reveal the underlying mechanisms of nonstationary variations in twelve ambient concentrations of aerosols and gaseous pollutants, and the associations with meteorological factors. This approach can consider the uncertainties and temporal dependences of time series data. The common trends of the yearlong and three selected diurnal variations were obtained to characterize the dominant processes occurring in general and specific scenarios in Taipei during 2009 (i.e., during Asian dust storm (ADS) events, rainfall, and under normal conditions). The results revealed the two distinct yearlong NOx transformation processes, and demonstrated that traffic emissions and photochemical reactions both critically influence diurnal variation, depending upon meteorological conditions. During an ADS event, transboundary transport and distinct weather conditions both influenced the temporal pattern of identified common trends. This study shows the DFA method can effectively extract meaningful latent processes of time series data and provide insights of the dominant associations and interactions in the complex air pollution processes. PMID:25600321
NASA Astrophysics Data System (ADS)
Jeannet, P.; Stübi, R.; Levrat, G.; Viatte, P.; Staehelin, J.
2007-06-01
This study documents the history of the Payerne (Switzerland) ozone series obtained with the Brewer-Mast sonde from the end of 1966 until the change to the electrochemical concentration cell (ECC) sonde in autumn 2002, as well as the reevaluation of the original data. Several corrections were made in order to improve the homogeneity and the quality of the time series. We furthermore derived long-term trends for the reevaluated time series using atmospheric variables in a stepwise regression model. In the stratosphere, trends over the 1970-2002 period remain nearly the same as over periods ending a few years earlier. For tropospheric ozone trends, a hockey stick model allowing for a change in trend in 1990 was used and a sensitivity analysis with different data sets was carried out. Besides the standard World Meteorological Organization (WMO) data evaluation procedure, we used alternative data sets (1) accounting for the preflight laboratory calibrations, or (2) ignoring the total ozone normalization, (3) as well as correcting for chemical interference with SO2. With all data sets, tropospheric trends were strongly positive in all seasons over the 1967-1989 period. In the 1990-2002 period, winter trends remained positive over the whole troposphere with all data sets, whereas in the other seasons, trends were generally negative near the ground and shifted to zero or positive values with increasing altitude in the troposphere. The alternative evaluation procedures strongly affect the derived tropospheric trends in the 1990-2002 period and their uncertainties.
Häfner, Hans-Martin; Bräuer, Kurt; Radke, Carolin; Eichner, Martin; Strölin, Anke
2009-01-01
We use continuous wavelet analysis (WA) of Laser Doppler Flux (LDF) time series measured in basal cell carcinomas (BCC) and plaque psoriasis (PP) in order to investigate the rhythmical behavior of blood flow in tumor or inflammatory associated neoangiogenesis.A total of 68 patients with primary BCCs and 40 patients with PP were included in the study. LDF time series were separated in four scaling levels corresponding to the influences of sympathetic activity (SL1), myogenic activity in the vessel wall (SL2), respiration (SL3) and heart beat (SL4).In BCC, SL1 decreased compared to healthy skin. In all other scaling levels, we found a statistically significant increase of the SLs compared to healthy skin. These increases were not found in PP.Rhythmical behavior of blood flow in malignant tumors is totally different from that in regions with inflammation. In BCCs, thermoregulatory processes, ascribed to sympathetic activity, decrease statistically significant. In contrast, inflammatory processes in PP do not substantially change sympathetic activity. WA of tumor perfusion could open a new noninvasive monitor system for controlling tumor therapy. PMID:19847053
Asymmetric multiscale detrended cross-correlation analysis of financial time series.
Yin, Yi; Shang, Pengjian
2014-09-01
We propose the asymmetric multiscale detrended cross-correlation analysis (MS-ADCCA) method and apply MS-ADCCA method to explore the existence of asymmetric cross-correlation for daily price returns in US and Chinese stock markets and to assess the properties of these asymmetric cross-correlations. The results all show the existences of asymmetric cross-correlations, while small asymmetries at small scales and larger asymmetries at larger scales are also displayed. There is a strong similarity between S&P500 and DJI, and we reveal that the asymmetries depend more on the cross-correlations of S&P500 vs. DJI, S&P500 vs. NQCI, DJI vs. NQCI, and ShangZheng vs. ShenCheng when the market is falling than rising, respectively. By comparing the spectra of S&P500 vs. NQCI and DJI vs. NQCI with uptrends and downtrends, we detect some new characteristics which lead to some new conclusions. Likewise, some new conclusions also can be drawn by the new characteristics displayed through the comparison between the spectra of ShangZheng vs. HSI and ShenCheng vs. HSI. Obviously, we conclude that although the overall spectra are similar and one market has the same effect when it is rising and falling in the study of asymmetric cross-correlations between it and different markets, the cross-correlations and asymmetries on the trends of the different markets are all different. MS-ADCCA method can detect the differences on the asymmetric cross-correlations by different trends of markets. Moreover, the uniqueness of cross-correlation between NQCI and HSI can be detected in the study of the asymmetric cross-correlations, which confirms that HSI is unique in the Chinese stock markets and NQCI is unique in the US stock markets further. PMID:25273179
Monitoring the Urban Growth on Vitosha Northeast Slope by Time Series Analysis