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.
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.
Time series analysis of injuries.
Martinez-Schnell, B; Zaidi, A
1989-12-01
We used time series models in the exploratory and confirmatory analysis of selected fatal injuries in the United States from 1972 to 1983. We built autoregressive integrated moving average (ARIMA) models for monthly, weekly, and daily series of deaths and used these models to generate hypotheses. These deaths resulted from six causes of injuries: motor vehicles, suicides, homicides, falls, drownings, and residential fires. For each cause of injury, we estimated calendar effects on the monthly death counts. We confirmed the significant effect of vehicle miles travelled on motor vehicle fatalities with a transfer function model. Finally, we applied intervention analysis to deaths due to motor vehicles.
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.
Cho, Jee-Ye; Lee, Jin-Hee; Kim, Gon; Kim, Min-Chan
2008-01-01
This study was conducted to develop the NutriSonic Web Expert System for Meal Management and Nutrition Counseling with Analysis of User's Nutritive Changes of selected days and food exchange information with easy data transition. This program manipulates a food, menu and meal and search database that has been developed. Also, the system provides a function to check the user's nutritive change of selected days. Users can select a recommended general and therapeutic menu using this system. NutriSonic can analyze nutrients and e-food exchange ("e" means the food exchange data base calculated by a computer program) in menus and meals. The expert can insert and store a meal database and generate the synthetic information of age, sex and therapeutic purpose of disease. With investigation and analysis of the user's needs, the meal planning program on the internet has been continuously developed. Users are able to follow up their nutritive changes with nutrient information and ratio of 3 major energy nutrients. Also, users can download another data format like Excel files (.xls) for analysis and verify their nutrient time-series analysis. The results of analysis are presented quickly and accurately. Therefore it can be used by not only usual people, but also by dietitians and nutritionists who take charge of making a menu and experts in the field of food and nutrition. It is expected that the NutriSonic Web Expert System can be useful for nutrition education, nutrition counseling and expert meal management. PMID:20126376
NASA Astrophysics Data System (ADS)
Aguilera, R.; Marcé, R.; Sabater, S.
2014-12-01
The scientific community concurs that the rate and the intensity of the current environmental changes have been accelerated by anthropogenic activities. Mediterranean freshwater systems are particularly vulnerable to such changes due to their inherent climate-dependent hydrological variability. We aimed to detect common patterns in monthly nutrient concentration time-series (1980-2011) from 50 sampling stations across a Mediterranean river basin, and to attribute their spatiotemporal variability to environmental factors at the basin and regional scales. Dynamic Factor Analysis (DFA) provided the methodological framework to extract underlying common patterns in nutrient time-series with missing observations, a commonly encountered problem in environmental databases. Most importantly, DFA guaranteed the explicit consideration of the inextricable link between temporal and spatial patterns of change necessary to investigate the drivers and processes that shape them. Using complementary methods such as frequency and trend analyses, we sought to further characterize the extracted patterns and identify the drivers behind their variability across time and space. The extracted nitrate concentration patterns described a large proportion of the observed variability at the basin scale. Cycles of 2.5 and 3.5 years identified in nitrate concentration patterns were linked to climatic oscillations. The seasonality of nitrate patterns was either driven by hydrological or phenological processes, depending on the geographical location of the monitoring point. Land uses linked to fertilizer application further modulated the increasing and decreasing nitrate trends scattered across the basin. Conversely, phosphate concentration patterns did not fully describe the behavior of all monitoring points included in the analysis. Nevertheless, decreasing phosphate trends observed across space and time coincided with changes in land-use management practices in the study basin.
Spectral analysis of multiple time series
NASA Technical Reports Server (NTRS)
Dubman, M. R.
1972-01-01
Application of spectral analysis for mathematically determining relationship of random vibrations in structures and concurrent events in electric circuits, physiology, economics, and seismograms is discussed. Computer program for performing spectral analysis of multiple time series is described.
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
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.
Circulant Matrices and Time-Series Analysis
ERIC Educational Resources Information Center
Pollock, D. S. G.
2002-01-01
This paper sets forth some salient results in the algebra of circulant matrices which can be used in time-series analysis. It provides easy derivations of some results that are central to the analysis of statistical periodograms and empirical spectral density functions. A statistical test for the stationarity or homogeneity of empirical processes…
Circulant Matrices and Time-Series Analysis
ERIC Educational Resources Information Center
Pollock, D. S. G.
2002-01-01
This paper sets forth some salient results in the algebra of circulant matrices which can be used in time-series analysis. It provides easy derivations of some results that are central to the analysis of statistical periodograms and empirical spectral density functions. A statistical test for the stationarity or homogeneity of empirical processes…
Complex network analysis of time series
NASA Astrophysics Data System (ADS)
Gao, Zhong-Ke; Small, Michael; Kurths, Jürgen
2016-12-01
Revealing complicated behaviors from time series constitutes a fundamental problem of continuing interest and it has attracted a great deal of attention from a wide variety of fields on account of its significant importance. The past decade has witnessed a rapid development of complex network studies, which allow to characterize many types of systems in nature and technology that contain a large number of components interacting with each other in a complicated manner. Recently, the complex network theory has been incorporated into the analysis of time series and fruitful achievements have been obtained. Complex network analysis of time series opens up new venues to address interdisciplinary challenges in climate dynamics, multiphase flow, brain functions, ECG dynamics, economics and traffic systems.
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.
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.
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 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.
Time Series Analysis Without Model Identification.
ERIC Educational Resources Information Center
Velicer, Wayne F.; McDonald, Roderick P.
1984-01-01
A new approach to time series analysis was developed. It employs a generalized transformation of the observed data to meet the assumptions of the general linear model, thus eliminating the need to identify a specific model. This approach permits alternative computational procedures, based on a generalized least squares algorithm. (Author/BW)
Interrupted time series analysis in clinical research.
Matowe, Lloyd K; Leister, Cathie A; Crivera, Concetta; Korth-Bradley, Joan M
2003-01-01
To demonstrate the usefulness of interrupted time series analysis in clinical trial design. A safety data set of electrocardiographic (ECG) information was simulated from actual data that had been collected in a Phase I study. Simulated data on 18 healthy volunteers based on a study performed in a contract research facility were collected based on single doses of an experimental medication that may affect ECG parameters. Serial ECGs were collected before and during treatment with the experimental medication. Data from 7 real subjects receiving placebo were used to simulate the pretreatment phase of time series; data from 18 real subjects receiving active treatment were used to simulate the treatment phase of the time series. Visual inspection of data was performed, followed by tests for trend, seasonality, and autocorrelation by use of SAS. There was no evidence of trend, seasonality, or autocorrelation. In 11 of 18 simulated individuals, statistically significant changes in QTc intervals were observed following treatment with the experimental medication. A significant time of day and treatment interaction was observed in 4 simulated patients. Interrupted time series analysis techniques offer an additional tool for the study of clinical situations in which patients must act as their own controls and where serial data can be collected at evenly distributed intervals.
Delay differential analysis of time series.
Lainscsek, Claudia; Sejnowski, Terrence J
2015-03-01
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
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.
Highly comparative time-series analysis: the empirical structure of time series and their methods
Fulcher, Ben D.; Little, Max A.; Jones, Nick S.
2013-01-01
The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines. PMID:23554344
Highly comparative time-series analysis: the empirical structure of time series and their methods.
Fulcher, Ben D; Little, Max A; Jones, Nick S
2013-06-06
The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines.
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.
Radar Interferometry Time Series Analysis and Tools
NASA Astrophysics Data System (ADS)
Buckley, S. M.
2006-12-01
We consider the use of several multi-interferogram analysis techniques for identifying transient ground motions. Our approaches range from specialized InSAR processing for persistent scatterer and small baseline subset methods to the post-processing of geocoded displacement maps using a linear inversion-singular value decomposition solution procedure. To better understand these approaches, we have simulated sets of interferograms spanning several deformation phenomena, including localized subsidence bowls with constant velocity and seasonal deformation fluctuations. We will present results and insights from the application of these time series analysis techniques to several land subsidence study sites with varying deformation and environmental conditions, e.g., arid Phoenix and coastal Houston-Galveston metropolitan areas and rural Texas sink holes. We consistently find that the time invested in implementing, applying and comparing multiple InSAR time series approaches for a given study site is rewarded with a deeper understanding of the techniques and deformation phenomena. To this end, and with support from NSF, we are preparing a first-version of an InSAR post-processing toolkit to be released to the InSAR science community. These studies form a baseline of results to compare against the higher spatial and temporal sampling anticipated from TerraSAR-X as well as the trade-off between spatial coverage and resolution when relying on ScanSAR interferometry.
Tremor classification and tremor time series analysis
NASA Astrophysics Data System (ADS)
Deuschl, Günther; Lauk, Michael; Timmer, Jens
1995-03-01
The separation between physiologic tremor (PT) in normal subjects and the pathological tremors of essential tremor (ET) or Parkinson's disease (PD) was investigated on the basis of monoaxial accelerometric recordings of 35 s hand tremor epochs. Frequency and amplitude were insufficient to separate between these conditions, except for the trivial distinction between normal and pathologic tremors that is already defined on the basis of amplitude. We found that waveform analysis revealed highly significant differences between normal and pathologic tremors, and, more importantly, among different forms of pathologic tremors. We found in our group of 25 patients with PT and 15 with ET a reasonable distinction with the third momentum and the time reversal invariance. A nearly complete distinction between these two conditions on the basis of the asymmetric decay of the autocorrelation function. We conclude that time series analysis can probably be developed into a powerful tool for the objective analysis of tremors.
Dimensionless embedding for nonlinear time series analysis
NASA Astrophysics Data System (ADS)
Hirata, Yoshito; Aihara, Kazuyuki
2017-09-01
Recently, infinite-dimensional delay coordinates (InDDeCs) have been proposed for predicting high-dimensional dynamics instead of conventional delay coordinates. Although InDDeCs can realize faster computation and more accurate short-term prediction, it is still not well-known whether InDDeCs can be used in other applications of nonlinear time series analysis in which reconstruction is needed for the underlying dynamics from a scalar time series generated from a dynamical system. Here, we give theoretical support for justifying the use of InDDeCs and provide numerical examples to show that InDDeCs can be used for various applications for obtaining the recurrence plots, correlation dimensions, and maximal Lyapunov exponents, as well as testing directional couplings and extracting slow-driving forces. We demonstrate performance of the InDDeCs using the weather data. Thus, InDDeCs can eventually realize "dimensionless embedding" while we enjoy faster and more reliable computations.
TSAN: a package for time series analysis.
Wang, D C; Vagnucci, A H
1980-04-01
Many biomedical data are in the form of time series. Analyses of these data include: (1) search for any biorhythm; (2) test of homogeneity of several time series; (3) assessment of stationarity; (4) test of normality of the time series histogram; (5) evaluation of dependence between data points. In this paper we present a subroutine package called TSAN. It is developed to accomplish these tasks. Computational methods, as well as flowcharts, for these subroutines are described. Two sample runs are demonstrated.
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.
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
Rotavirus and adenovirus gastroenteritis: time series analysis.
Celik, Cem; Gozel, Mustafa Gokhan; Turkay, Hakan; Bakici, Mustafa Zahir; Güven, Ahmet Sami; Elaldi, Nazif
2015-08-01
This study investigated the effects of changes in weather conditions (monthly average temperature, monthly minimum temperature, monthly average humidity) on rotavirus and adenovirus gastroenteritis frequency and whether there was a seasonal correlation. Between 2006 and 2012, 4702 fecal samples were taken from patients ≤ 5 years of age with acute gastroenteritis; these samples were analyzed in terms of rotavirus group A and adenovirus serotype 40-41 antigens using time-series and negative binomial regression analysis. Rotavirus antigens were found in 797 samples (17.0%), adenovirus antigens in 113 samples (2.4%), and rotavirus and adenovirus antigens together in 16 samples (0.3%). There was a seasonal change in rotavirus gastroenteritis (P < 0.001), and a 1°C decrease in average temperature increased the ratio of rotavirus cases in those with diarrhea by 0.523%. In addition, compared with data from other years, the number of patients was lower in the first month of 2008 and in the second month of 2012, when the temperature was below -20°C (monthly minimum temperature). There was no statistically significant relationship between adenovirus infection and change in weather conditions. Various factors such as change in weather conditions, as well as the population's sensitivity and associated changes in activity, play a role in the spread of rotavirus infection. © 2015 Japan Pediatric Society.
Three Analysis Examples for Time Series Data
USDA-ARS?s Scientific Manuscript database
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...
Comparative Analysis on Time Series with Included Structural Break
NASA Astrophysics Data System (ADS)
Andreeski, Cvetko J.; Vasant, Pandian
2009-08-01
The time series analysis (ARIMA models) is a good approach for identification of time series. But, if we have structural break in the time series, we cannot create only one model of time series. Further more, if we don't have enough data between two structural breaks, it's impossible to create valid time series models for identification of the time series. This paper explores the possibility of identification of the inflation process dynamics via of the system-theoretic, by means of both Box-Jenkins ARIMA methodologies and artificial neural networks.
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.
Time Series Analysis of Mother-Infant Interaction.
ERIC Educational Resources Information Center
Rosenfeld, Howard M.
A method of studying attachment behavior in infants was devised using time series and time sequence analyses. Time series analysis refers to relationships between events coded over adjacent fixed-time units. Time sequence analysis refers to the distribution of exact times at which particular events happen. Using these techniques, multivariate…
Nonlinear independent component analysis and multivariate time series analysis
NASA Astrophysics Data System (ADS)
Storck, Jan; Deco, Gustavo
1997-02-01
We derive an information-theory-based unsupervised learning paradigm for nonlinear independent component analysis (NICA) with neural networks. We demonstrate that under the constraint of bounded and invertible output transfer functions the two main goals of unsupervised learning, redundancy reduction and maximization of the transmitted information between input and output (Infomax-principle), are equivalent. No assumptions are made concerning the kind of input and output distributions, i.e. the kind of nonlinearity of correlations. An adapted version of the general NICA network is used for the modeling of multivariate time series by unsupervised learning. Given time series of various observables of a dynamical system, our net learns their evolution in time by extracting statistical dependencies between past and present elements of the time series. Multivariate modeling is obtained by making present value of each time series statistically independent not only from their own past but also from the past of the other series. Therefore, in contrast to univariate methods, the information lying in the couplings between the observables is also used and a detection of higher-order cross correlations is possible. We apply our method to time series of the two-dimensional Hénon map and to experimental time series obtained from the measurements of axial velocities in different locations in weakly turbulent Taylor-Couette flow.
Analysis of Nonstationary Time Series for Biological Rhythms Research.
Leise, Tanya L
2017-06-01
This article is part of a Journal of Biological Rhythms series exploring analysis and statistics topics relevant to researchers in biological rhythms and sleep research. The goal is to provide an overview of the most common issues that arise in the analysis and interpretation of data in these fields. In this article on time series analysis for biological rhythms, we describe some methods for assessing the rhythmic properties of time series, including tests of whether a time series is indeed rhythmic. Because biological rhythms can exhibit significant fluctuations in their period, phase, and amplitude, their analysis may require methods appropriate for nonstationary time series, such as wavelet transforms, which can measure how these rhythmic parameters change over time. We illustrate these methods using simulated and real time series.
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.
Short time-series microarray analysis: Methods and challenges
Wang, Xuewei; Wu, Ming; Li, Zheng; Chan, Christina
2008-01-01
The detection and analysis of steady-state gene expression has become routine. Time-series microarrays are of growing interest to systems biologists for deciphering the dynamic nature and complex regulation of biosystems. Most temporal microarray data only contain a limited number of time points, giving rise to short-time-series data, which imposes challenges for traditional methods of extracting meaningful information. To obtain useful information from the wealth of short-time series data requires addressing the problems that arise due to limited sampling. Current efforts have shown promise in improving the analysis of short time-series microarray data, although challenges remain. This commentary addresses recent advances in methods for short-time series analysis including simplification-based approaches and the integration of multi-source information. Nevertheless, further studies and development of computational methods are needed to provide practical solutions to fully exploit the potential of this data. PMID:18605994
Detrended fluctuation analysis of multivariate time series
NASA Astrophysics Data System (ADS)
Xiong, Hui; Shang, P.
2017-01-01
In this work, we generalize the detrended fluctuation analysis (DFA) to the multivariate case, named multivariate DFA (MVDFA). The validity of the proposed MVDFA is illustrated by numerical simulations on synthetic multivariate processes, where the cases that initial data are generated independently from the same system and from different systems as well as the correlated variate from one system are considered. Moreover, the proposed MVDFA works well when applied to the multi-scale analysis of the returns of stock indices in Chinese and US stock markets. Generally, connections between the multivariate system and the individual variate are uncovered, showing the solid performances of MVDFA and the multi-scale MVDFA.
Time series analysis of compressor noise.
NASA Technical Reports Server (NTRS)
Hardin, J. C.; Brown, T. J.
1972-01-01
Description of a method to determine compressor directivity at discrete frequencies by separation of the tone power from the noise background. The technique developed effected the separation by Fourier analysis of the sound pressure autocorrelation at large lags. This produced experimental directivity results which compared reasonably well with the theoretical patterns except near the compressor axis.
Topic Time Series Analysis of Microblogs
2014-10-01
is generated by Instagram. Topic 80, Distance: 143.2101 Top words: 1. rawr 2. ˆ0ˆ 3. kill 4. jurassic 5. dinosaur Analysis: This topic is quite...center in Commerce, CA (a subdivision of Los Angeles). Topic 80, Distance: 6.6391 Top words: 1. rawr 2. ˆ0ˆ 3. kill 4. jurassic 5. dinosaur Analysis...8.65 0.90 0.040 ‘cold’ ‘af’ ‘outside’ 7.88 0.60 0.059 ‘chico’ ‘fluff’ ‘ice’ 9.10 0.19 0.002 ‘rawr’ ‘ dinosaur ’ ‘jurassic’ ‘seen’ 0.55 0.36 4.15 6.2
The scaling of time series size towards detrended fluctuation analysis
NASA Astrophysics Data System (ADS)
Gao, Xiaolei; Ren, Liwei; Shang, Pengjian; Feng, Guochen
2016-06-01
In this paper, we introduce a modification of detrended fluctuation analysis (DFA), called multivariate DFA (MNDFA) method, based on the scaling of time series size N. In traditional DFA method, we obtained the influence of the sequence segmentation interval s, and it inspires us to propose a new model MNDFA to discuss the scaling of time series size towards DFA. The effectiveness of the procedure is verified by numerical experiments with both artificial and stock returns series. Results show that the proposed MNDFA method contains more significant information of series compared to traditional DFA method. The scaling of time series size has an influence on the auto-correlation (AC) in time series. For certain series, we obtain an exponential relationship, and also calculate the slope through the fitting function. Our analysis and finite-size effect test demonstrate that an appropriate choice of the time series size can avoid unnecessary influences, and also make the testing results more accurate.
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.
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.
Improved singular spectrum analysis for time series with missing data
NASA Astrophysics Data System (ADS)
Shen, Y.; Peng, F.; Li, B.
2015-07-01
Singular spectrum analysis (SSA) is a powerful technique for time series analysis. Based on the property that the original time series can be reproduced from its principal components, this contribution develops an improved SSA (ISSA) for processing the incomplete time series and the modified SSA (SSAM) of Schoellhamer (2001) is its special case. The approach is evaluated with the synthetic and real incomplete time series data of suspended-sediment concentration from San Francisco Bay. The result from the synthetic time series with missing data shows that the relative errors of the principal components reconstructed by ISSA are much smaller than those reconstructed by SSAM. Moreover, when the percentage of the missing data over the whole time series reaches 60 %, the improvements of relative errors are up to 19.64, 41.34, 23.27 and 50.30 % for the first four principal components, respectively. Both the mean absolute error and mean root mean squared error of the reconstructed time series by ISSA are also smaller than those by SSAM. The respective improvements are 34.45 and 33.91 % when the missing data accounts for 60 %. The results from real incomplete time series also show that the standard deviation (SD) derived by ISSA is 12.27 mg L-1, smaller than the 13.48 mg L-1 derived by SSAM.
Improved singular spectrum analysis for time series with missing data
NASA Astrophysics Data System (ADS)
Shen, Y.; Peng, F.; Li, B.
2014-12-01
Singular spectrum analysis (SSA) is a powerful technique for time series analysis. Based on the property that the original time series can be reproduced from its principal components, this contribution will develop an improved SSA (ISSA) for processing the incomplete time series and the modified SSA (SSAM) of Schoellhamer (2001) is its special case. The approach was evaluated with the synthetic and real incomplete time series data of suspended-sediment concentration from San Francisco Bay. The result from the synthetic time series with missing data shows that the relative errors of the principal components reconstructed by ISSA are much smaller than those reconstructed by SSAM. Moreover, when the percentage of the missing data over the whole time series reaches 60%, the improvements of relative errors are up to 19.64, 41.34, 23.27 and 50.30% for the first four principal components, respectively. Besides, both the mean absolute errors and mean root mean squared errors of the reconstructed time series by ISSA are also much smaller than those by SSAM. The respective improvements are 34.45 and 33.91% when the missing data accounts for 60%. The results from real incomplete time series also show that the SD derived by ISSA is 12.27 mg L-1, smaller than 13.48 mg L-1 derived by SSAM.
Transition Icons for Time Series Visualization and Exploratory Analysis.
Nickerson, Paul; Baharloo, Raheleh; Wanigatunga, Amal A; Manini, Todd D; Tighe, Patrick J; Rashidi, Parisa
2017-05-16
The modern healthcare landscape has seen the rapid emergence of techniques and devices which temporally monitor and record physiological signals. The prevalence of time series data within the healthcare field necessitates the development of methods which can analyze the data in order to draw meaningful conclusions. Time series behavior is notoriously difficult to intuitively understand due to its intrinsic high-dimensionality, which is compounded in the case of analyzing groups of time series collected from different patients. Our framework, which we call Transition Icons, renders common patterns in a visual format useful for understanding the shared behavior within groups of time series. Transition Icons are adept at detecting and displaying subtle differences and similarities e.g. between measurements taken from patients receiving different treatment strategies or stratified by demographics. We introduce various methods which collectively allow for exploratory analysis of groups of time series, while being free of distribution assumptions and including simple heuristics for parameter determination. Our technique extracts discrete transition patterns from Symbolic Aggregate approXimation (SAX) representations, and compiles transition frequencies into a Bag of Patterns (BoP) constructed for each group. These transition frequencies are normalized and aligned in icon form to intuitively display the underlying patterns. We demonstrate the Transition Icon technique for two time series data sets - postoperative pain scores, and hip-worn accelerometer activity counts. We believe Transition Icons can be an important tool for researchers approaching time series data, as they give rich and intuitive information about collective time series behaviors.
A Dimensionality Reduction Technique for Efficient Time Series Similarity Analysis
Wang, Qiang; Megalooikonomou, Vasileios
2008-01-01
We propose a dimensionality reduction technique for time series analysis that significantly improves the efficiency and accuracy of similarity searches. In contrast to piecewise constant approximation (PCA) techniques that approximate each time series with constant value segments, the proposed method--Piecewise Vector Quantized Approximation--uses the closest (based on a distance measure) codeword from a codebook of key-sequences to represent each segment. The new representation is symbolic and it allows for the application of text-based retrieval techniques into time series similarity analysis. Experiments on real and simulated datasets show that the proposed technique generally outperforms PCA techniques in clustering and similarity searches. PMID:18496587
Time averaging, ageing and delay analysis of financial time series
NASA Astrophysics Data System (ADS)
Cherstvy, Andrey G.; Vinod, Deepak; Aghion, Erez; Chechkin, Aleksei V.; Metzler, Ralf
2017-06-01
We introduce three strategies for the analysis of financial time series based on time averaged observables. These comprise the time averaged mean squared displacement (MSD) as well as the ageing and delay time methods for varying fractions of the financial time series. We explore these concepts via statistical analysis of historic time series for several Dow Jones Industrial indices for the period from the 1960s to 2015. Remarkably, we discover a simple universal law for the delay time averaged MSD. The observed features of the financial time series dynamics agree well with our analytical results for the time averaged measurables for geometric Brownian motion, underlying the famed Black-Scholes-Merton model. The concepts we promote here are shown to be useful for financial data analysis and enable one to unveil new universal features of stock market dynamics.
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.
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.
Digital time series analysis for flutter test data
NASA Technical Reports Server (NTRS)
Batill, S. M.; Carey, D. M.; Kehoe, M. W.
1992-01-01
An application of digital time series analysis to flutter test data processing was conducted. A numerical investigation was used to evaluate the method, as well as its sensitivity to noise and parameter variations. These parameters included those involved with data acquisition, as well as system response characteristics. This digital time series method was then used to predict flutter speed from subcritical response wind tunnel tests. Flutter speeds predicted from forced response, subcritical wind tunnel tests were compared to the experimental flutter speeds.
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.
Multiresolution analysis of Bursa Malaysia KLCI time series
NASA Astrophysics Data System (ADS)
Ismail, Mohd Tahir; Dghais, Amel Abdoullah Ahmed
2017-05-01
In general, a time series is simply a sequence of numbers collected at regular intervals over a period. Financial time series data processing is concerned with the theory and practice of processing asset price over time, such as currency, commodity data, and stock market data. The primary aim of this study is to understand the fundamental characteristics of selected financial time series by using the time as well as the frequency domain analysis. After that prediction can be executed for the desired system for in sample forecasting. In this study, multiresolution analysis which the assist of discrete wavelet transforms (DWT) and maximal overlap discrete wavelet transform (MODWT) will be used to pinpoint special characteristics of Bursa Malaysia KLCI (Kuala Lumpur Composite Index) daily closing prices and return values. In addition, further case study discussions include the modeling of Bursa Malaysia KLCI using linear ARIMA with wavelets to address how multiresolution approach improves fitting and forecasting results.
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
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.
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.
Time series analysis of monthly pulpwood use in the Northeast
James T. Bones
1980-01-01
Time series analysis was used to develop a model that depicts pulpwood use in the Northeast. The model is useful in forecasting future pulpwood requirements (short term) or monitoring pulpwood-use activity in relation to past use patterns. The model predicted a downturn in use during 1980.
A Time-Series Analysis of Student and Teacher Interaction.
ERIC Educational Resources Information Center
Schempp, Paul G.
The stability of teaching behavior was examined by observing student/teacher interaction over one academic year. One teacher was studied using a time-series analysis. He had 14 years experience and taught physical education in grades K-6 in a single school. Data were collected over one academic year using the Cheffers Adaptation of Flanders…
Application of time series analysis for assessing reservoir trophic status
Paris Honglay Chen; Ka-Chu Leung
2000-01-01
This study is to develop and apply a practical procedure for the time series analysis of reservoir eutrophication conditions. A multiplicative decomposition method is used to determine the trophic variations including seasonal, circular, long-term and irregular changes. The results indicate that (1) there is a long high peak for seven months from April to October...
Time Series Analysis Based on Running Mann Whitney Z Statistics
USDA-ARS?s Scientific Manuscript database
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-...
Application of modern time series analysis to high stability oscillators
NASA Technical Reports Server (NTRS)
Farrell, B. F.; Mattison, W. M.; Vessot, R. F. C.
1980-01-01
Techniques of modern time series analysis useful for investigating the characteristics of high-stability oscillators and identifying systematic perturbations are discussed with reference to an experiment in which the frequencies of superconducting cavity-stabilized oscillators and hydrogen masers were compared. The techniques examined include transformation to stationarity, autocorrelation and cross-correlation, superresolution, and transfer function determination.
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.
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
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.
Rodgers, Joseph Lee; Beasley, William Howard; Schuelke, Matthew
2014-01-01
Many data structures, particularly time series data, are naturally seasonal, cyclical, or otherwise circular. Past graphical methods for time series have focused on linear plots. In this article, we move graphical analysis onto the circle. We focus on 2 particular methods, one old and one new. Rose diagrams are circular histograms and can be produced in several different forms using the RRose software system. In addition, we propose, develop, illustrate, and provide software support for a new circular graphical method, called Wrap-Around Time Series Plots (WATS Plots), which is a graphical method useful to support time series analyses in general but in particular in relation to interrupted time series designs. We illustrate the use of WATS Plots with an interrupted time series design evaluating the effect of the Oklahoma City bombing on birthrates in Oklahoma County during the 10 years surrounding the bombing of the Murrah Building in Oklahoma City. We compare WATS Plots with linear time series representations and overlay them with smoothing and error bands. Each method is shown to have advantages in relation to the other; in our example, the WATS Plots more clearly show the existence and effect size of the fertility differential.
Scale-space analysis of time series in circulatory research.
Mortensen, Kim Erlend; Godtliebsen, Fred; Revhaug, Arthur
2006-12-01
Statistical analysis of time series is still inadequate within circulation research. With the advent of increasing computational power and real-time recordings from hemodynamic studies, one is increasingly dealing with vast amounts of data in time series. This paper aims to illustrate how statistical analysis using the significant nonstationarities (SiNoS) method may complement traditional repeated-measures ANOVA and linear mixed models. We applied these methods on a dataset of local hepatic and systemic circulatory changes induced by aortoportal shunting and graded liver resection. We found SiNoS analysis more comprehensive when compared with traditional statistical analysis in the following four ways: 1) the method allows better signal-to-noise detection; 2) including all data points from real time recordings in a statistical analysis permits better detection of significant features in the data; 3) analysis with multiple scales of resolution facilitates a more differentiated observation of the material; and 4) the method affords excellent visual presentation by combining group differences, time trends, and multiscale statistical analysis allowing the observer to quickly view and evaluate the material. It is our opinion that SiNoS analysis of time series is a very powerful statistical tool that may be used to complement conventional statistical methods.
Learning dynamics from nonstationary time series: Analysis of electroencephalograms
NASA Astrophysics Data System (ADS)
Gribkov, Dmitrii; Gribkova, Valentina
2000-06-01
We propose an empirical modeling technique for a nonstationary time series analysis. Proposed methods include a high-dimensional (N>3) dynamical model construction in the form of delay differential equations, a nonparametric method of respective time delay calculation, the detection of quasistationary regions of the process by reccurence analysis in the space of model coefficients, and final fitting of the model to quasistationary segments of observed time series. We also demonstrate the effectiveness of our approach for nonstationary signal classification in the space of model coefficients. Applying the empirical modeling technique to electroencephalogram (EEG) records analysis, we find evidence of high-dimensional nonlinear dynamics in quasistationary EEG segments. Reccurence analysis of model parameters reveals long-term correlations in nonstationary EEG records. Using the dynamical model as a nonlinear filter, we find that different emotional states of subjects can be clearly distinguished in the space of model coefficients.
An entropic approach to the analysis of time series
NASA Astrophysics Data System (ADS)
Scafetta, Nicola
Statistical analysis of time series. With compelling arguments we show that the Diffusion Entropy Analysis (DEA) is the only method of the literature of the Science of Complexity that correctly determines the scaling hidden within a time series reflecting a Complex Process. The time series is thought of as a source of fluctuations, and the DEA is based on the Shannon entropy of the diffusion process generated by these fluctuations. All traditional methods of scaling analysis, instead, are based on the variance of this diffusion process. The variance methods detect the real scaling only if the Gaussian assumption holds true. We call H the scaling exponent detected by the variance methods and delta the real scaling exponent. If the time series is characterized by Fractional Brownian Motion, we have H = delta and the scaling can be safely determined, in this case, by using the variance methods. If, on the contrary, the time series is characterized, for example, by Levy statistics, H ≠ delta and the variance methods cannot be used to detect the true scaling. Levy walk yields the relation delta = 1/(3 - 2H). In the case of Levy flights, the variance diverges and the exponent H cannot be determined, whereas the scaling delta exists and can be established by using the DEA. Therefore, only the joint use of two different scaling analysis methods, the variance scaling analysis and the DEA, can assess the real nature, Gauss or Levy or something else, of a time series. Moreover, the DEA determines the information content, under the form of Shannon entropy, or of any other convenient entropic indicator, at each time step of the process that, given a sufficiently large number of data, is expected to become diffusion with scaling. This makes it possible to study the regime of transition from dynamics to thermodynamics, non-stationary regimes, and the saturation regime as well. First of all, the efficiency of the DEA is proved with theoretical arguments and with numerical work
Time series analysis of nuclear instrumentation in EBR-II
Imel, G.R.
1996-05-01
Results of a time series analysis of the scaler count data from the 3 wide range nuclear detectors in the Experimental Breeder Reactor-II are presented. One of the channels was replaced, and it was desired to determine if there was any statistically significant change (ie, improvement) in the channel`s response after the replacement. Data were collected from all 3 channels for 16-day periods before and after detector replacement. Time series analysis and statistical tests showed that there was no significant change after the detector replacement. Also, there were no statistically significant differences among the 3 channels, either before or after the replacement. Finally, it was determined that errors in the reactivity change inferred from subcritical count monitoring during fuel handling would be on the other of 20-30 cents for single count intervals.
A Radial Basis Function Approach to Financial Time Series Analysis
1993-12-01
Massachusetts Institute of Technology Artificial Intelligence Laboratory AI-TR 1457 545 Technology Square Cambridge, Massachusetts 02139 9. SPONSORING...Function Approach to Financial Time Series Analysis Ihy James M. Hutchinson Master of Science in EE(S. Massachusetts Institute of Technology (1986...Philosophy 0e r . at tilt NTiS CRA&IDTIC TAB Massachusetts Institute of Technology Unannoun•ea February. I9-1 Justific.igo,, . @1991 Massachusetts Institut
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.
Nonlinear time series analysis of solar and stellar data
NASA Astrophysics Data System (ADS)
Jevtic, Nada
2003-06-01
Nonlinear time series analysis was developed to study chaotic systems. Its utility was investigated for the study of solar and stellar data time series. Sunspot data are the longest astronomical time series, and it reflects the long-term variation of the solar magnetic field. Due to periods of low solar activity, such as the Maunder minimum, and the solar cycle's quasiperiodicity, it has been postulated that the solar dynamo is a chaotic system. We show that, due to the definition of sunspot number, using nonlinear time series methods, it is not possible to test this postulate. To complement the sunspot data analysis, theoretically generated data for the α-Ω solar dynamo with meridional circulation were analyzed. Effects of stochastic fluctuations on the energy of an α-Ω dynamo with meridional circulation were investigated. This proved extremely useful in generating a clearer understanding of the effect of dynamical noise on the unperturbed system. This was useful in the study of the light intensity curve of white dwarf PG 1351+489. Dynamical resetting was identified for PG 1351+489, using phase space methods, and then, using nonlinear noise reduction methods, the white noise tail of the power spectrum was lowered by a factor of 40. This allowed the identification of 10 new lines in the power spectrum. Finally, using Poincare section return times, a periodicity in the light curve of cataclysmic variable SS Cygni was identified. We initially expected that time delay methods would be useful as a qualitative comparison tool. However, they were capable, under the proper set of constraints on the data sets, of providing quantitative information about the signal source.
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.
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).
The QuakeSim System for GPS Time Series Analysis
NASA Astrophysics Data System (ADS)
Granat, R. A.; Gao, X.; Pierce, M.; Wang, J.
2010-12-01
We present a system for analysis of GPS time series data available to geosciences users through a web services / web portal interface. The system provides two time series analysis methods, one based on hidden Markov model (HMM) segmentation, the other based on covariance descriptor analysis (CDA). In addition, it provides data pre-processing routines that perform spike noise removal, linear de-trending, sum-of-sines removal, and common mode removal using probabilistic principle components analysis (PPCA). These components can be composed by the user into the desired series of processing steps for analysis through an intuitive graphical interface. The system is accessed through a web portal that allows both micro-scale (individual station) and macro-scale (whole network) exploration of data sets and analysis results via Google Maps. Users can focus in on or scroll through particular spatial or temporal time windows, or observe dynamic behavior by created movies that display the system state. Analysis results can be exported to KML format for easy combination with other sources of data, such as fault databases and InSAR interferograms. GPS solutions for California member stations of the plate boundary observatory from both the SOPAC and JPL gipsy context groups are automatically imported into the system as that data becomes available. We show the results of the methods as applied to these data sets for an assortment of case studies, and show how the system can be used to analyze both seismic and aseismic signals.
FROG: Time Series Analysis for the Web Service Era
NASA Astrophysics Data System (ADS)
Allan, A.
2005-12-01
The FROG application is part of the next generation Starlink{http://www.starlink.ac.uk} software work (Draper et al. 2005) and released under the GNU Public License{http://www.gnu.org/copyleft/gpl.html} (GPL). Written in Java, it has been designed for the Web and Grid Service era as an extensible, pluggable, tool for time series analysis and display. With an integrated SOAP server the packages functionality is exposed to the user for use in their own code, and to be used remotely over the Grid, as part of the Virtual Observatory (VO).
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.
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.
Conditional adaptive Bayesian spectral analysis of nonstationary biomedical time series.
Bruce, Scott A; Hall, Martica H; Buysse, Daniel J; Krafty, Robert T
2017-05-08
Many studies of biomedical time series signals aim to measure the association between frequency-domain properties of time series and clinical and behavioral covariates. However, the time-varying dynamics of these associations are largely ignored due to a lack of methods that can assess the changing nature of the relationship through time. This article introduces a method for the simultaneous and automatic analysis of the association between the time-varying power spectrum and covariates, which we refer to as conditional adaptive Bayesian spectrum analysis (CABS). The procedure adaptively partitions the grid of time and covariate values into an unknown number of approximately stationary blocks and nonparametrically estimates local spectra within blocks through penalized splines. CABS is formulated in a fully Bayesian framework, in which the number and locations of partition points are random, and fit using reversible jump Markov chain Monte Carlo techniques. Estimation and inference averaged over the distribution of partitions allows for the accurate analysis of spectra with both smooth and abrupt changes. The proposed methodology is used to analyze the association between the time-varying spectrum of heart rate variability and self-reported sleep quality in a study of older adults serving as the primary caregiver for their ill spouse. © 2017, The International Biometric Society.
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.
Monthly hail time series analysis related to agricultural insurance
NASA Astrophysics Data System (ADS)
Tarquis, Ana M.; Saa, Antonio; Gascó, Gabriel; Díaz, M. C.; Garcia Moreno, M. R.; Burgaz, F.
2010-05-01
Hail is one of the mos important crop insurance in Spain being more than the 50% of the total insurance in cereal crops. The purpose of the present study is to carry out a study about the hail in cereals. Four provinces have been chosen, those with the values of production are higher: Burgos and Zaragoza for the wheat and Cuenca and Valladolid for the barley. The data that we had available for the study of the evolution and intensity of the damages for hail includes an analysis of the correlation between the ratios of agricultural insurances provided by ENESA and the number of days of annual hail (from 1981 to 2007). At the same time, several weather station per province were selected by the longest more complete data recorded (from 1963 to 2007) to perform an analysis of monthly time series of the number of hail days (HD). The results of the study show us that relation between the ratio of the agricultural insurances and the number of hail days is not clear. Several observations are discussed to explain these results as well as if it is possible to determinte a change in tendency in the HD time series.
Time series analysis of gold production in Malaysia
NASA Astrophysics Data System (ADS)
Muda, Nora; Hoon, Lee Yuen
2012-05-01
Gold is a soft, malleable, bright yellow metallic element and unaffected by air or most reagents. It is highly valued as an asset or investment commodity and is extensively used in jewellery, industrial application, dentistry and medical applications. In Malaysia, gold mining is limited in several areas such as Pahang, Kelantan, Terengganu, Johor and Sarawak. The main purpose of this case study is to obtain a suitable model for the production of gold in Malaysia. The model can also be used to predict the data of Malaysia's gold production in the future. Box-Jenkins time series method was used to perform time series analysis with the following steps: identification, estimation, diagnostic checking and forecasting. In addition, the accuracy of prediction is tested using mean absolute percentage error (MAPE). From the analysis, the ARIMA (3,1,1) model was found to be the best fitted model with MAPE equals to 3.704%, indicating the prediction is very accurate. Hence, this model can be used for forecasting. This study is expected to help the private and public sectors to understand the gold production scenario and later plan the gold mining activities in Malaysia.
Spectral analysis of GPS precise point positioning time series
NASA Astrophysics Data System (ADS)
Selle, C.; Desai, S.; Garcia Fernandez, M.; Sibois, A.
2014-12-01
This paper presents the results from performing spectral analysis on GPS positioning time series obtained from precise point positioning (PPP). The goal of this work was to evaluate the impact of different choices of processing strategies and models on GPS-based PPP. We studied the spectra of station positions, examined overall noise levels and identified the presence of spurious periodic signals. Testing various processing options allowed us to assess their effect on station position estimates. With the Jet Propulsion Laboratory's contribution to the second reprocessing campaign of the International GNSS Service (IGS) as our reference source for input orbits and clocks, we also considered the effects of using different orbit and clock products. This included products from the previous reprocessing campaign, which were fixed in the IGS05 reference frame, while recent products use the IGS08 frame. Of particular importance are our results from assessing the impact on the station position time series from the single-receiver ambiguity resolution capability offered by JPL's reprocessing campaigns. Furthermore, our tests raise the possibility of distinguishing between PPP processing settings, input orbits and clocks, and station data and location-dependent effects as causes of these features.
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.
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.
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
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.
A Simple Pile-up Model for Time Series Analysis
NASA Astrophysics Data System (ADS)
Sevilla, Diego J. R.
2017-07-01
In this paper, a simple pile-up model is presented. This model calculates the probability P(n| N) of having n counts if N particles collide with a sensor during an exposure time. Through some approximations, an analytic expression depending on only one parameter is obtained. This parameter characterizes the pile-up magnitude, and depends on features of the instrument and the source. The statistical model obtained permits the determination of probability distributions of measured counts from the probability distributions of incoming particles, which is valuable for time series analysis. Applicability limits are discussed, and an example of the improvement that can be achieved in the statistical analysis considering the proposed pile-up model is shown by analyzing real data.
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.
SAGE: A tool for time-series analysis of Greenland
NASA Astrophysics Data System (ADS)
Duerr, R. E.; Gallaher, D. W.; Khalsa, S. S.; Lewis, S.
2011-12-01
The National Snow and Ice Data Center (NSIDC) has developed an operational tool for analysis. This production tool is known as "Services for the Analysis of the Greenland Environment" (SAGE). Using an integrated workspace approach, a researcher has the ability to find relevant data and perform various analysis functions on the data, as well as retrieve the data and analysis results. While there continues to be compelling observational evidence for increased surface melting and rapid thinning along the margins of the Greenland ice sheet, there are still uncertainties with respect to estimates of mass balance of Greenland's ice sheet as a whole. To better understand the dynamics of these issues, it is important for scientists to have access to a variety of datasets from multiple sources, and to be able to integrate and analyze the data. SAGE provides data from various sources, such as AMSR-E and AVHRR datasets, which can be analyzed individually through various time-series plots and aggregation functions; or they can be analyzed together with scatterplots or overlaid time-series plots to provide quick and useful results to support various research products. The application is available at http://nsidc.org/data/sage/. SAGE was built on top of NSIDC's existing Searchlight engine. The SAGE interface gives users access to much of NSIDC's relevant Greenland raster data holdings, as well as data from outside sources. Additionally, various web services provide access for other clients to utilize the functionality that the SAGE interface provides. Combined, these methods of accessing the tool allow scientists the ability to devote more of their time to their research, and less on trying to find and retrieve the data they need.
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
Centrality measures in temporal networks with time series analysis
NASA Astrophysics Data System (ADS)
Huang, Qiangjuan; Zhao, Chengli; Zhang, Xue; Wang, Xiaojie; Yi, Dongyun
2017-05-01
The study of identifying important nodes in networks has a wide application in different fields. However, the current researches are mostly based on static or aggregated networks. Recently, the increasing attention to networks with time-varying structure promotes the study of node centrality in temporal networks. In this paper, we define a supra-evolution matrix to depict the temporal network structure. With using of the time series analysis, the relationships between different time layers can be learned automatically. Based on the special form of the supra-evolution matrix, the eigenvector centrality calculating problem is turned into the calculation of eigenvectors of several low-dimensional matrices through iteration, which effectively reduces the computational complexity. Experiments are carried out on two real-world temporal networks, Enron email communication network and DBLP co-authorship network, the results of which show that our method is more efficient at discovering the important nodes than the common aggregating method.
Visual analytics for model selection in time series analysis.
Bögl, Markus; Aigner, Wolfgang; Filzmoser, Peter; Lammarsch, Tim; Miksch, Silvia; Rind, Alexander
2013-12-01
Model selection in time series analysis is a challenging task for domain experts in many application areas such as epidemiology, economy, or environmental sciences. The methodology used for this task demands a close combination of human judgement and automated computation. However, statistical software tools do not adequately support this combination through interactive visual interfaces. We propose a Visual Analytics process to guide domain experts in this task. For this purpose, we developed the TiMoVA prototype that implements this process based on user stories and iterative expert feedback on user experience. The prototype was evaluated by usage scenarios with an example dataset from epidemiology and interviews with two external domain experts in statistics. The insights from the experts' feedback and the usage scenarios show that TiMoVA is able to support domain experts in model selection tasks through interactive visual interfaces with short feedback cycles.
Time series Analysis of Integrateds Building System Variables
NASA Astrophysics Data System (ADS)
Georgiev, Tz.; Jonkov, T.; Yonchev, E.
2010-10-01
This article deals with time series analysis of indoor and outdoor variables of the integrated building system. The kernel of these systems is heating, ventilation and air conditioning (HVAC) problems. Important outdoor and indoor variables are: air temperature, global and diffuse radiations, wind speed and direction, temperature, relative humidity, mean radiant temperature, and so on. The aim of this article is TO select the structure and investigation of a linear auto—regressive (AR) and auto—regressive with external inputs (ARX) models. The investigation of obtained models is based on real—live data. All researches are derived in MATLAB environment. The further research will focus on synthesis of robust energy saving control algorithms.
Time series analysis for minority game simulations of financial markets
NASA Astrophysics Data System (ADS)
Ferreira, Fernando F.; Francisco, Gerson; Machado, Birajara S.; Muruganandam, Paulsamy
2003-04-01
The minority game (MG) model introduced recently provides promising insights into the understanding of the evolution of prices, indices and rates in the financial markets. In this paper we perform a time series analysis of the model employing tools from statistics, dynamical systems theory and stochastic processes. Using benchmark systems and a financial index for comparison, several conclusions are obtained about the generating mechanism for this kind of evolution. The motion is deterministic, driven by occasional random external perturbation. When the interval between two successive perturbations is sufficiently large, one can find low dimensional chaos in this regime. However, the full motion of the MG model is found to be similar to that of the first differences of the SP500 index: stochastic, nonlinear and (unit root) stationary.
The Prediction of Teacher Turnover Employing Time Series Analysis.
ERIC Educational Resources Information Center
Costa, Crist H.
The purpose of this study was to combine knowledge of teacher demographic data with time-series forecasting methods to predict teacher turnover. Moving averages and exponential smoothing were used to forecast discrete time series. The study used data collected from the 22 largest school districts in Iowa, designated as FACT schools. Predictions…
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
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.
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.
Interrupted time-series analysis: studying trends in neurosurgery.
Wong, Ricky H; Smieliauskas, Fabrice; Pan, I-Wen; Lam, Sandi K
2015-12-01
OBJECT Neurosurgery studies traditionally have evaluated the effects of interventions on health care outcomes by studying overall changes in measured outcomes over time. Yet, this type of linear analysis is limited due to lack of consideration of the trend's effects both pre- and postintervention and the potential for confounding influences. The aim of this study was to illustrate interrupted time-series analysis (ITSA) as applied to an example in the neurosurgical literature and highlight ITSA's potential for future applications. METHODS The methods used in previous neurosurgical studies were analyzed and then compared with the methodology of ITSA. RESULTS The ITSA method was identified in the neurosurgical literature as an important technique for isolating the effect of an intervention (such as a policy change or a quality and safety initiative) on a health outcome independent of other factors driving trends in the outcome. The authors determined that ITSA allows for analysis of the intervention's immediate impact on outcome level and on subsequent trends and enables a more careful measure of the causal effects of interventions on health care outcomes. CONCLUSIONS ITSA represents a significant improvement over traditional observational study designs in quantifying the impact of an intervention. ITSA is a useful statistical procedure to understand, consider, and implement as the field of neurosurgery evolves in sophistication in big-data analytics, economics, and health services research.
Nonlinear Time Series Analysis in Earth Sciences - Potentials and Pitfalls
NASA Astrophysics Data System (ADS)
Kurths, Jürgen; Donges, Jonathan F.; Donner, Reik V.; Marwan, Norbert; Zou, Yong
2010-05-01
The application of methods of nonlinear time series analysis has a rich tradition in Earth sciences and has enabled substantially new insights into various complex processes there. However, some approaches and findings have been controversially discussed over the last decades. One reason is that they are often bases on strong restrictions and their violation may lead to pitfalls and misinterpretations. Here, we discuss three general concepts of nonlinear dynamics and statistical physics, synchronization, recurrence and complex networks and explain how to use them for data analysis. We show that the corresponding methods can be applied even to rather short and non-stationary data which are typical in Earth sciences. References Marwan, N., Romano, M., Thiel, M., Kurths, J.: Recurrence plots for the analysis of complex systems, Physics Reports 438, 237-329 (2007) Arenas, A., Diaz-Guilera, A., Kurths, J., Moreno, Y., Zhou, C.: Synchronization in complex networks, Physics Reports 469, 93-153 (2008) Marwan, N., Donges, J.F., Zou, Y., Donner, R. and Kurths, J., Phys. Lett. A 373, 4246 (2009) Donges, J.F., Zou, Y., Marwan, N. and Kurths, J. Europhys. Lett. 87, 48007 (2009) Donner, R., Zou, Y., Donges, J.F., Marwan, N. and Kurths, J., Phys. Rev. E 81, 015101(R) (2010)
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.
Advanced tools for astronomical time series and image analysis
NASA Astrophysics Data System (ADS)
Scargle, Jeffrey D.
The algorithms described here, which I have developed for applications in X-ray and γ-ray astronomy, will hopefully be of use in other ways, perhaps aiding in the exploration of modern astronomy's data cornucopia. The goal is to describe principled approaches to some ubiquitous problems, such as detection and characterization of periodic and aperiodic signals, estimation of time delays between multiple time series, and source detection in noisy images with noisy backgrounds. The latter problem is related to detection of clusters in data spaces of various dimensions. A goal of this work is to achieve a unifying view of several related topics: signal detection and characterization, cluster identification, classification, density estimation, and multivariate regression. In addition to being useful for analysis of data from space-based and ground-based missions, these algorithms may be a basis for a future automatic science discovery facility, and in turn provide analysis tools for the Virtual Observatory. This chapter has ties to those by Larry Bretthorst, Tom Loredo, Alanna Connors, Fionn Murtagh, Jim Berger, David van Dyk, Vicent Martinez & Enn Saar.
Time Series Analysis of the Quasar PKS 1749+096
NASA Astrophysics Data System (ADS)
Lam, Michael T.; Balonek, T. J.
2011-01-01
Multiple timescales of variability are observed in quasars at a variety of wavelengths, the nature of which is not fully understood. In 2007 and 2008, the quasar 1749+096 underwent two unprecedented optical outbursts, reaching a brightness never before seen in our twenty years of monitoring. Much lower level activity had been seen prior to these two outbursts. We present an analysis of the timescales of variability over the two regimes using a variety of statistical techniques. An IDL software package developed at Colgate University over the summer of 2010, the Quasar User Interface (QUI), provides effective computation of four time series functions for analyzing underlying trends present in generic, discretely sampled data sets. Using the Autocorrelation Function, Structure Function, and Power Spectrum, we are able to quickly identify possible variability timescales. QUI is also capable of computing the Cross-Correlation Function for comparing variability at different wavelengths. We apply these algorithms to 1749+096 and present our analysis of the timescales for this object. Funding for this project was received from Colgate University, the Justus and Jayne Schlichting Student Research Fund, and the NASA / New York Space Grant.
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.
Multi-Granular Trend Detection for Time-Series Analysis.
Arthur Van, Goethem; Staals, Frank; Loffler, Maarten; Dykes, Jason; Speckmann, Bettina
2017-01-01
Time series (such as stock prices) and ensembles (such as model runs for weather forecasts) are two important types of one-dimensional time-varying data. Such data is readily available in large quantities but visual analysis of the raw data quickly becomes infeasible, even for moderately sized data sets. Trend detection is an effective way to simplify time-varying data and to summarize salient information for visual display and interactive analysis. We propose a geometric model for trend-detection in one-dimensional time-varying data, inspired by topological grouping structures for moving objects in two- or higher-dimensional space. Our model gives provable guarantees on the trends detected and uses three natural parameters: granularity, support-size, and duration. These parameters can be changed on-demand. Our system also supports a variety of selection brushes and a time-sweep to facilitate refined searches and interactive visualization of (sub-)trends. We explore different visual styles and interactions through which trends, their persistence, and evolution can be explored.
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
Seasonal dynamics of bacterial meningitis: a time-series analysis.
Paireau, Juliette; Chen, Angelica; Broutin, Helene; Grenfell, Bryan; Basta, Nicole E
2016-06-01
Bacterial meningitis, which is caused mainly by Neisseria meningitidis, Haemophilus influenzae, and Streptococcus pneumoniae, inflicts a substantial burden of disease worldwide. Yet, the temporal dynamics of this disease are poorly characterised and many questions remain about the ecology of the disease. We aimed to comprehensively assess seasonal trends in bacterial meningitis on a global scale. We developed the first bacterial meningitis global database by compiling monthly incidence data as reported by country-level surveillance systems. Using country-level wavelet analysis, we identified whether a 12 month periodic component (annual seasonality) was detected in time-series that had at least 5 years of data with at least 40 cases reported per year. We estimated the mean timing of disease activity by computing the centre of gravity of the distribution of cases and investigated whether synchrony exists between the three pathogens responsible for most cases of bacterial meningitis. We used country-level data from 66 countries, including from 47 countries outside the meningitis belt in sub-Saharan Africa. A persistent seasonality was detected in 49 (96%) of the 51 time-series from 38 countries eligible for inclusion in the wavelet analyses. The mean timing of disease activity had a latitudinal trend, with bacterial meningitis seasons peaking during the winter months in countries in both the northern and southern hemispheres. The three pathogens shared similar seasonality, but time-shifts differed slightly by country. Our findings provide key insight into the seasonal dynamics of bacterial meningitis and add to knowledge about the global epidemiology of meningitis and the host, environment, and pathogen characteristics driving these patterns. Comprehensive understanding of global seasonal trends in meningitis could be used to design more effective prevention and control strategies. Princeton University Health Grand Challenge, US National Institutes of Health (NIH
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.
Stochastic time series analysis of fetal heart-rate variability
NASA Astrophysics Data System (ADS)
Shariati, M. A.; Dripps, J. H.
1990-06-01
Fetal Heart Rate(FHR) is one of the important features of fetal biophysical activity and its long term monitoring is used for the antepartum(period of pregnancy before labour) assessment of fetal well being. But as yet no successful method has been proposed to quantitatively represent variety of random non-white patterns seen in FHR. Objective of this paper is to address this issue. In this study the Box-Jenkins method of model identification and diagnostic checking was used on phonocardiographic derived FHR(averaged) time series. Models remained exclusively autoregressive(AR). Kalman filtering in conjunction with maximum likelihood estimation technique forms the parametric estimator. Diagnosrics perfonned on the residuals indicated that a second order model may be adequate in capturing type of variability observed in 1 up to 2 mm data windows of FHR. The scheme may be viewed as a means of data reduction of a highly redundant information source. This allows a much more efficient transmission of FHR information from remote locations to places with facilities and expertise for doser analysis. The extracted parameters is aimed to reflect numerically the important FHR features. These are normally picked up visually by experts for their assessments. As a result long term FHR recorded during antepartum period could then be screened quantitatively for detection of patterns considered normal or abnonnal. 1.
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.
Learning Bayesian networks for clinical time series analysis.
van der Heijden, Maarten; Velikova, Marina; Lucas, Peter J F
2014-04-01
Autonomous chronic disease management requires models that are able to interpret time series data from patients. However, construction of such models by means of machine learning requires the availability of costly health-care data, often resulting in small samples. We analysed data from chronic obstructive pulmonary disease (COPD) patients with the goal of constructing a model to predict the occurrence of exacerbation events, i.e., episodes of decreased pulmonary health status. Data from 10 COPD patients, gathered with our home monitoring system, were used for temporal Bayesian network learning, combined with bootstrapping methods for data analysis of small data samples. For comparison a temporal variant of augmented naive Bayes models and a temporal nodes Bayesian network (TNBN) were constructed. The performances of the methods were first tested with synthetic data. Subsequently, different COPD models were compared to each other using an external validation data set. The model learning methods are capable of finding good predictive models for our COPD data. Model averaging over models based on bootstrap replications is able to find a good balance between true and false positive rates on predicting COPD exacerbation events. Temporal naive Bayes offers an alternative that trades some performance for a reduction in computation time and easier interpretation. Copyright © 2013 Elsevier Inc. All rights reserved.
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.
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.
Causal analysis of time series from hydrological systems
NASA Astrophysics Data System (ADS)
Selle, Benny; Aufgebauer, Britta; Knorr, Klaus-Holger
2017-04-01
It is often difficult to infer cause and effect in hydrological systems for which time series of system inputs, outputs and state variables are observed. A recently published technique called Convergent Cross Mapping could be a promising tool to detect causality between time series. A response variable Y may be causally related to a forcing variable X, if the so called cross mapping of X using Y improves with the amount of data included. The idea is that a response variable contains information on the history of its driving variable whereas the reverse may not be true. We propose an alternative approach based on similar ideas using neural networks. Our approach is firstly compared to Convergent Cross Mapping using a synthetic time series of precipitation and streamflow generated by a rainfall runoff model. Secondly, measured concentrations of dissolved organic carbon and dissolved iron from a mountainous stream in Germany, that were previously hypothesised to be casually linked, are tested.
Appropriate Algorithms for Nonlinear Time Series Analysis in Psychology
NASA Astrophysics Data System (ADS)
Scheier, Christian; Tschacher, Wolfgang
Chaos theory has a strong appeal for psychology because it allows for the investigation of the dynamics and nonlinearity of psychological systems. Consequently, chaos-theoretic concepts and methods have recently gained increasing attention among psychologists and positive claims for chaos have been published in nearly every field of psychology. Less attention, however, has been paid to the appropriateness of chaos-theoretic algorithms for psychological time series. An appropriate algorithm can deal with short, noisy data sets and yields `objective' results. In the present paper it is argued that most of the classical nonlinear techniques don't satisfy these constraints and thus are not appropriate for psychological data. A methodological approach is introduced that is based on nonlinear forecasting and the method of surrogate data. In artificial data sets and empirical time series we can show that this methodology reliably assesses nonlinearity and chaos in time series even if they are short and contaminated by noise.
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.
Time series analysis of collective motions in proteins
NASA Astrophysics Data System (ADS)
Alakent, Burak; Doruker, Pemra; ćamurdan, Mehmet C.
2004-01-01
The dynamics of α-amylase inhibitor tendamistat around its native state is investigated using time series analysis of the principal components of the Cα atomic displacements obtained from molecular dynamics trajectories. Collective motion along a principal component is modeled as a homogeneous nonstationary process, which is the result of the damped oscillations in local minima superimposed on a random walk. The motion in local minima is described by a stationary autoregressive moving average model, consisting of the frequency, damping factor, moving average parameters and random shock terms. Frequencies for the first 50 principal components are found to be in the 3-25 cm-1 range, which are well correlated with the principal component indices and also with atomistic normal mode analysis results. Damping factors, though their correlation is less pronounced, decrease as principal component indices increase, indicating that low frequency motions are less affected by friction. The existence of a positive moving average parameter indicates that the stochastic force term is likely to disturb the mode in opposite directions for two successive sampling times, showing the modes tendency to stay close to minimum. All these four parameters affect the mean square fluctuations of a principal mode within a single minimum. The inter-minima transitions are described by a random walk model, which is driven by a random shock term considerably smaller than that for the intra-minimum motion. The principal modes are classified into three subspaces based on their dynamics: essential, semiconstrained, and constrained, at least in partial consistency with previous studies. The Gaussian-type distributions of the intermediate modes, called "semiconstrained" modes, are explained by asserting that this random walk behavior is not completely free but between energy barriers.
Analytical framework for recurrence network analysis of time series.
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.
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.
Geostatistical analysis as applied to two environmental radiometric time series.
Dowdall, Mark; Lind, Bjørn; Gerland, Sebastian; Rudjord, Anne Liv
2003-03-01
This article details the results of an investigation into the application of geostatistical data analysis to two environmental radiometric time series. The data series employed consist of 99Tc values for seaweed (Fucus vesiculosus) and seawater samples taken as part of a marine monitoring program conducted on the coast of northern Norway by the Norwegian Radiation Protection Authority. Geostatistical methods were selected in order to provide information on values of the variables at unsampled times and to investigate the temporal correlation exhibited by the data sets. This information is of use in the optimisation of future sampling schemes and for providing information on the temporal behaviour of the variables in question that may not be obtained during a cursory analysis. The results indicate a high degree of temporal correlation within the data sets, the correlation for the seawater and seaweed data being modelled with an exponential and linear function, respectively. The semi-variogram for the seawater data indicates a temporal range of correlation of approximately 395 days with no apparent random component to the overall variance structure and was described best by an exponential function. The temporal structure of the seaweed data was best modelled by a linear function with a small nugget component. Evidence of drift was present in both semi-variograms. Interpolation of the data sets using the fitted models and a simple kriging procedure were compared, using a cross-validation procedure, with simple linear interpolation. Results of this exercise indicate that, for the seawater data, the kriging procedure outperformed the simple interpolation with respect to error distribution and correlation of estimates with actual values. Using the unbounded linear model with the seaweed data produced estimates that were only marginally better than those produced by the simple interpolation.
An introduction to chaotic and random time series analysis
NASA Technical Reports Server (NTRS)
Scargle, Jeffrey D.
1989-01-01
The origin of chaotic behavior and the relation of chaos to randomness are explained. Two mathematical results are described: (1) a representation theorem guarantees the existence of a specific time-domain model for chaos and addresses the relation between chaotic, random, and strictly deterministic processes; (2) a theorem assures that information on the behavior of a physical system in its complete state space can be extracted from time-series data on a single observable. Focus is placed on an important connection between the dynamical state space and an observable time series. These two results lead to a practical deconvolution technique combining standard random process modeling methods with new embedded techniques.
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.
An introduction to chaotic and random time series analysis
NASA Technical Reports Server (NTRS)
Scargle, Jeffrey D.
1989-01-01
The origin of chaotic behavior and the relation of chaos to randomness are explained. Two mathematical results are described: (1) a representation theorem guarantees the existence of a specific time-domain model for chaos and addresses the relation between chaotic, random, and strictly deterministic processes; (2) a theorem assures that information on the behavior of a physical system in its complete state space can be extracted from time-series data on a single observable. Focus is placed on an important connection between the dynamical state space and an observable time series. These two results lead to a practical deconvolution technique combining standard random process modeling methods with new embedded techniques.
Time-series intervention analysis of pedestrian countdown timer effects.
Huitema, Bradley E; Van Houten, Ron; Manal, Hana
2014-11-01
Pedestrians account for 40-50% of traffic fatalities in large cities. Several previous studies based on relatively small samples have concluded that Pedestrian Countdown Timers (PCT) may reduce pedestrian crashes at signalized intersections, but other studies report no reduction. The purposes of the present article are to (1) describe a new methodology to evaluate the effectiveness of introducing PCT signals and (2) to present results of applying this methodology to pedestrian crash data collected in a large study carried out in Detroit, Michigan. The study design incorporated within-unit as well as between-unit components. The main focus was on dynamic effects that occurred within the PCT unit of 362 treated sites during the 120 months of the study. An interrupted time-series analysis was developed to evaluate whether change in crash frequency depended upon of the degree to which the countdown timers penetrated the treatment unit. The between-unit component involved comparisons between the treatment unit and a control unit. The overall conclusion is that the introduction of PCT signals in Detroit reduced pedestrian crashes to approximately one-third of the preintervention level. The evidence for this reductionis strong and the change over time was shown to be a function of the extent to which the timers were introduced during the intervention period. There was no general drop-off in crash frequency throughout the baseline interval of over five years; only when the PCT signals were introduced in large numbers was consistent and convincing crash reduction observed. Correspondingly, there was little evidence of change in the control unit.
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)
TSNet--a distributed architecture for time series analysis.
Hunter, Jim
2008-01-01
This paper describes an infrastructure (TSNet) which can be used by geographically separated research groups to develop algorithms for the abstraction of complex time series data. The framework was specifically designed for the kinds of abstractions required for the application of clinical guidelines within intensive care.
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)
Model Identification in Time-Series Analysis: Some Empirical Results.
ERIC Educational Resources Information Center
Padia, William L.
Model identification of time-series data is essential to valid statistical tests of intervention effects. Model identification is, at best, inexact in the social and behavioral sciences where one is often confronted with small numbers of observations. These problems are discussed, and the results of independent identifications of 130 social 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.
Noise analysis of GPS time series in Taiwan
NASA Astrophysics Data System (ADS)
Lee, You-Chia; Chang, Wu-Lung
2017-04-01
Global positioning system (GPS) usually used for researches of plate tectonics and crustal deformation. In most studies, GPS time series considered only time-independent noises (white noise), but time-dependent noises (flicker noise, random walk noise) which were found by nearly twenty years are also important to the precision of data. The rate uncertainties of stations will be underestimated if the GPS time series are assumed only time-independent noise. Therefore studying the noise properties of GPS time series is necessary in order to realize the precision and reliability of velocity estimates. The lengths of our GPS time series are from over 500 stations around Taiwan with time spans longer than 2.5 years up to 20 years. The GPS stations include different monument types such as deep drill braced, roof, metal tripod, and concrete pier, and the most common type in Taiwan is the metal tripod. We investigated the noise properties of continuous GPS time series by using the spectral index and amplitude of the power law noise. During the process we first remove the data outliers, and then estimate linear trend, size of offsets, and seasonal signals, and finally the amplitudes of the power-law and white noise are estimated simultaneously. Our preliminary results show that the noise amplitudes of the north component are smaller than that of the other two components, and the largest amplitudes are in the vertical. We also find that the amplitudes of white noise and power-law noises are positively correlated in three components. Comparisons of noise amplitudes of different monument types in Taiwan reveal that the deep drill braced monuments have smaller data uncertainties and therefore are more stable than other monuments.
A 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
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.
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.
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.
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.
Surrogate-assisted network analysis of nonlinear time series
NASA Astrophysics Data System (ADS)
Laut, Ingo; Räth, Christoph
2016-10-01
The performance of recurrence networks and symbolic networks to detect weak nonlinearities in time series is compared to the nonlinear prediction error. For the synthetic data of the Lorenz system, the network measures show a comparable performance. In the case of relatively short and noisy real-world data from active galactic nuclei, the nonlinear prediction error yields more robust results than the network measures. The tests are based on surrogate data sets. The correlations in the Fourier phases of data sets from some surrogate generating algorithms are also examined. The phase correlations are shown to have an impact on the performance of the tests for nonlinearity.
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).
Cluster analysis of long time-series medical datasets
NASA Astrophysics Data System (ADS)
Hirano, Shoji; Tsumoto, Shusaku
2004-04-01
This paper presents a comparative study about the characteristics of clustering methods for inhomogeneous time-series medical datasets. Using various combinations of comparison methods and grouping methods, we performed clustering experiments of the hepatitis data set and evaluated validity of the results. The results suggested that (1) complete-linkage (CL) criterion in agglomerative hierarchical clustering (AHC) outperformed average-linkage (AL) criterion in terms of the interpretability of a dendrogram and clustering results, (2) combination of dynamic time warping (DTW) and CL-AHC constantly produced interpretable results, (3) combination of DTW and rough clustering (RC) would be used to find the core sequences of the clusters, (4) multiscale matching may suffer from the treatment of 'no-match' pairs, however, the problem may be eluded by using RC as a subsequent grouping method.
Multiscale multifractal multiproperty analysis of financial time series based on Rényi entropy
NASA Astrophysics Data System (ADS)
Yujun, Yang; Jianping, Li; Yimei, Yang
This paper introduces a multiscale multifractal multiproperty analysis based on Rényi entropy (3MPAR) method to analyze short-range and long-range characteristics of financial time series, and then applies this method to the five time series of five properties in four stock indices. Combining the two analysis techniques of Rényi entropy and multifractal detrended fluctuation analysis (MFDFA), the 3MPAR method focuses on the curves of Rényi entropy and generalized Hurst exponent of five properties of four stock time series, which allows us to study more universal and subtle fluctuation characteristics of financial time series. By analyzing the curves of the Rényi entropy and the profiles of the logarithm distribution of MFDFA of five properties of four stock indices, the 3MPAR method shows some fluctuation characteristics of the financial time series and the stock markets. Then, it also shows a richer information of the financial time series by comparing the profile of five properties of four stock indices. In this paper, we not only focus on the multifractality of time series but also the fluctuation characteristics of the financial time series and subtle differences in the time series of different properties. We find that financial time series is far more complex than reported in some research works using one property of time series.
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.
On the use of bipolar montages for time-series analysis of intracranial electroencephalograms.
Zaveri, Hitten P; Duckrow, Robert B; Spencer, Susan S
2006-09-01
Bipolar montages are routinely employed for the interpretation of scalp and intracranial EEGs (icEEGs). In this manuscript we consider the assumptions that support the use of a bipolar montage and question the universal appropriateness of bipolar representation of icEEGs for the time-series analysis of these signals. Bipolar montages introduce an element of spatial processing into the observed time-series. In the case of icEEGs, we argue ambiguity may be introduced in some settings through this operation because of a lack of certifiability of local differentiability and continuity of the spatial structure of icEEGs, and their suboptimal spatial sampling. Example icEEGs were collected from three patients being studied for possible resective epilepsy surgery. Referential and bipolar representations of these signals were subjected to different visual and time-series analysis. The time-series measures calculated were the power spectral density and magnitude squared coherence. Visual analysis and time-series measures revealed that the icEEG time-series was altered by the use of a bipolar montage. The changes resulted from either the introduction of unrelated information from the two referential time-series into the bipolar time-series, or from the removal or alteration of information common to the two referential time-series in the bipolar time-series. The changes could not be predicted without prior knowledge of the relationship between measurement sites that form the bipolar montage. In certain settings, bipolar montages alter icEEGs and can confound the time-series analysis of these signals. In such settings, bipolar montages should be used with caution in the time-series analysis of icEEGs. This manuscript addresses the representation of the intracranial EEG for time-series analysis. There may be contexts where the assumptions underpinning correct application of the bipolar montage to the intracranial EEG are not satisfied.
A unified nonlinear stochastic time series analysis for climate science.
Moon, Woosok; Wettlaufer, John S
2017-03-13
Earth's orbit and axial tilt imprint a strong seasonal cycle on climatological data. Climate variability is typically viewed in terms of fluctuations in the seasonal cycle induced by higher frequency processes. We can interpret this as a competition between the orbitally enforced monthly stability and the fluctuations/noise induced by weather. Here we introduce a new time-series method that determines these contributions from monthly-averaged data. We find that the spatio-temporal distribution of the monthly stability and the magnitude of the noise reveal key fingerprints of several important climate phenomena, including the evolution of the Arctic sea ice cover, the El Nio Southern Oscillation (ENSO), the Atlantic Nio and the Indian Dipole Mode. In analogy with the classical destabilising influence of the ice-albedo feedback on summertime sea ice, we find that during some time interval of the season a destabilising process operates in all of these climate phenomena. The interaction between the destabilisation and the accumulation of noise, which we term the memory effect, underlies phase locking to the seasonal cycle and the statistical nature of seasonal predictability.
Financial time series analysis based on effective phase transfer entropy
NASA Astrophysics Data System (ADS)
Yang, Pengbo; Shang, Pengjian; Lin, Aijing
2017-02-01
Transfer entropy is a powerful technique which is able to quantify the impact of one dynamic system on another system. In this paper, we propose the effective phase transfer entropy method based on the transfer entropy method. We use simulated data to test the performance of this method, and the experimental results confirm that the proposed approach is capable of detecting the information transfer between the systems. We also explore the relationship between effective phase transfer entropy and some variables, such as data size, coupling strength and noise. The effective phase transfer entropy is positively correlated with the data size and the coupling strength. Even in the presence of a large amount of noise, it can detect the information transfer between systems, and it is very robust to noise. Moreover, this measure is indeed able to accurately estimate the information flow between systems compared with phase transfer entropy. In order to reflect the application of this method in practice, we apply this method to financial time series and gain new insight into the interactions between systems. It is demonstrated that the effective phase transfer entropy can be used to detect some economic fluctuations in the financial market. To summarize, the effective phase transfer entropy method is a very efficient tool to estimate the information flow between systems.
A unified nonlinear stochastic time series analysis for climate science
Moon, Woosok; Wettlaufer, John S.
2017-01-01
Earth’s orbit and axial tilt imprint a strong seasonal cycle on climatological data. Climate variability is typically viewed in terms of fluctuations in the seasonal cycle induced by higher frequency processes. We can interpret this as a competition between the orbitally enforced monthly stability and the fluctuations/noise induced by weather. Here we introduce a new time-series method that determines these contributions from monthly-averaged data. We find that the spatio-temporal distribution of the monthly stability and the magnitude of the noise reveal key fingerprints of several important climate phenomena, including the evolution of the Arctic sea ice cover, the El Nio Southern Oscillation (ENSO), the Atlantic Nio and the Indian Dipole Mode. In analogy with the classical destabilising influence of the ice-albedo feedback on summertime sea ice, we find that during some time interval of the season a destabilising process operates in all of these climate phenomena. The interaction between the destabilisation and the accumulation of noise, which we term the memory effect, underlies phase locking to the seasonal cycle and the statistical nature of seasonal predictability. PMID:28287128
A unified nonlinear stochastic time series analysis for climate science
NASA Astrophysics Data System (ADS)
Moon, Woosok; Wettlaufer, John S.
2017-03-01
Earth’s orbit and axial tilt imprint a strong seasonal cycle on climatological data. Climate variability is typically viewed in terms of fluctuations in the seasonal cycle induced by higher frequency processes. We can interpret this as a competition between the orbitally enforced monthly stability and the fluctuations/noise induced by weather. Here we introduce a new time-series method that determines these contributions from monthly-averaged data. We find that the spatio-temporal distribution of the monthly stability and the magnitude of the noise reveal key fingerprints of several important climate phenomena, including the evolution of the Arctic sea ice cover, the El Nio Southern Oscillation (ENSO), the Atlantic Nio and the Indian Dipole Mode. In analogy with the classical destabilising influence of the ice-albedo feedback on summertime sea ice, we find that during some time interval of the season a destabilising process operates in all of these climate phenomena. The interaction between the destabilisation and the accumulation of noise, which we term the memory effect, underlies phase locking to the seasonal cycle and the statistical nature of seasonal predictability.
A unified nonlinear stochastic time series analysis for climate science
NASA Astrophysics Data System (ADS)
Moon, Woosok; Wettlaufer, John
2017-04-01
Earth's orbit and axial tilt imprint a strong seasonal cycle on climatological data. Climate variability is typically viewed in terms of fluctuations in the seasonal cycle induced by higher frequency processes. We can interpret this as a competition between the orbitally enforced monthly stability and the fluctuations/noise induced by weather. Here we introduce a new time-series method that determines these contributions from monthly-averaged data. We find that the spatio-temporal distribution of the monthly stability and the magnitude of the noise reveal key fingerprints of several important climate phenomena, including the evolution of the Arctic sea ice cover, the El Niño Southern Oscillation (ENSO), the Atlantic Niño and the Indian Dipole Mode. In analogy with the classical destabilising influence of the ice-albedo feedback on summertime sea ice, we find that during some period of the season a destabilising process operates in all of these climate phenomena. The interaction between the destabilisation and the accumulation of noise, which we term the memory effect, underlies phase locking to the seasonal cycle and the statistical nature of seasonal predictability.
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.
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.
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.
Gan, M; Zuo, H; Yang, Z; Jiang, Y
2000-02-01
In this paper, we discuss the applications of time series modeling method in the analysis of lubricating oil of mechanical equipment. We obtained satisfactory results by applying AR model to perform time series modeling and forecasting analysis to the collected spectral analysis data of the air engine. So we have built a practical method for state monitoring and trouble forecasting of mechanical equipment.
Time-dependent spectral analysis of epidemiological time-series with wavelets.
Cazelles, Bernard; Chavez, Mario; Magny, Guillaume Constantin de; Guégan, Jean-Francois; Hales, Simon
2007-08-22
In the current context of global infectious disease risks, a better understanding of the dynamics of major epidemics is urgently needed. Time-series analysis has appeared as an interesting approach to explore the dynamics of numerous diseases. Classical time-series methods can only be used for stationary time-series (in which the statistical properties do not vary with time). However, epidemiological time-series are typically noisy, complex and strongly non-stationary. Given this specific nature, wavelet analysis appears particularly attractive because it is well suited to the analysis of non-stationary signals. Here, we review the basic properties of the wavelet approach as an appropriate and elegant method for time-series analysis in epidemiological studies. The wavelet decomposition offers several advantages that are discussed in this paper based on epidemiological examples. In particular, the wavelet approach permits analysis of transient relationships between two signals and is especially suitable for gradual change in force by exogenous variables.
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.
Statistical assessment of a unique time series analysis technique
NASA Technical Reports Server (NTRS)
Benignus, V. A.
1972-01-01
Empirical tests of a particular least squares multiple prediction program were studied, as well as the relationship of this particular program to general least squares multiple prediction theory. Empirical evaluation and test of the program involved: (1) conversion of the program to run on an IBM 360/44 with replication of test results obtained on a Univac 1108; and (2) generation and analysis of Monte Carlo simulated data with the objective of comparison against results theoretically obtainable from spectrum analysis routines.
Interrupted Time-Series Analysis with Brief Single-Subject Data.
ERIC Educational Resources Information Center
Crosbie, John
1993-01-01
Describes problems of assessing change with short time-series data: unreliability of visual inference and fact that current statistical procedures cannot control Type I error because they underestimate positive autocorrelation. Shows how problems can be solved with new interrupted time-series analysis procedure (ITSACORR) that uses more accurate…
Multiscale multifractal diffusion entropy analysis of financial time series
NASA Astrophysics Data System (ADS)
Huang, Jingjing; Shang, Pengjian
2015-02-01
This paper introduces a multiscale multifractal diffusion entropy analysis (MMDEA) method to analyze long-range correlation then applies this method to stock index series. The method combines the techniques of diffusion process and Rényi entropy to focus on the scaling behaviors of stock index series using a multiscale, which allows us to extend the description of stock index variability to include the dependence on the magnitude of the variability and time scale. Compared to multifractal diffusion entropy analysis, the MMDEA can show more details of scale properties and provide a reliable analysis. In this paper, we concentrate not only on the fact that the stock index series has multifractal properties but also that these properties depend on the time scale in which the multifractality is measured. This time scale is related to the frequency band of the signal. We find that stock index variability appears to be far more complex than reported in the studies using a fixed time scale.
Determinants of Male School Enrollments: A Time-Series Analysis.
ERIC Educational Resources Information Center
Mattila, J. Peter
1982-01-01
Regression analysis of 1956-79 data on high school and college enrollment among males aged 16-21, minimum wage levels, military draft requirements, unemployment rates, family income, high school graduation, and job opportunities indicates that male enrollment rates respond strongly to changes in the expected rate of financial return to schooling.…
Beyond linear methods of data analysis: time series analysis and its applications in renal research.
Gupta, Ashwani K; Udrea, Andreea
2013-01-01
Analysis of temporal trends in medicine is needed to understand normal physiology and to study the evolution of disease processes. It is also useful for monitoring response to drugs and interventions, and for accountability and tracking of health care resources. In this review, we discuss what makes time series analysis unique for the purposes of renal research and its limitations. We also introduce nonlinear time series analysis methods and provide examples where these have advantages over linear methods. We review areas where these computational methods have found applications in nephrology ranging from basic physiology to health services research. Some examples include noninvasive assessment of autonomic function in patients with chronic kidney disease, dialysis-dependent renal failure and renal transplantation. Time series models and analysis methods have been utilized in the characterization of mechanisms of renal autoregulation and to identify the interaction between different rhythms of nephron pressure flow regulation. They have also been used in the study of trends in health care delivery. Time series are everywhere in nephrology and analyzing them can lead to valuable knowledge discovery. The study of time trends of vital signs, laboratory parameters and the health status of patients is inherent to our everyday clinical practice, yet formal models and methods for time series analysis are not fully utilized. With this review, we hope to familiarize the reader with these techniques in order to assist in their proper use where appropriate.
A novel water quality data analysis framework based on time-series data mining.
Deng, Weihui; Wang, Guoyin
2017-07-01
The rapid development of time-series data mining provides an emerging method for water resource management research. In this paper, based on the time-series data mining methodology, we propose a novel and general analysis framework for water quality time-series data. It consists of two parts: implementation components and common tasks of time-series data mining in water quality data. In the first part, we propose to granulate the time series into several two-dimensional normal clouds and calculate the similarities in the granulated level. On the basis of the similarity matrix, the similarity search, anomaly detection, and pattern discovery tasks in the water quality time-series instance dataset can be easily implemented in the second part. We present a case study of this analysis framework on weekly Dissolve Oxygen time-series data collected from five monitoring stations on the upper reaches of Yangtze River, China. It discovered the relationship of water quality in the mainstream and tributary as well as the main changing patterns of DO. The experimental results show that the proposed analysis framework is a feasible and efficient method to mine the hidden and valuable knowledge from water quality historical time-series data. Copyright © 2017 Elsevier Ltd. All rights reserved.
Analysis of the temporal properties in car accident time series
NASA Astrophysics Data System (ADS)
Telesca, Luciano; Lovallo, Michele
2008-05-01
In this paper we study the time-clustering behavior of sequences of car accidents, using data from a freely available database in the internet. The Allan Factor analysis, which is a well-suited method to investigate time-dynamical behaviors in point processes, has revealed that the car accident sequences are characterized by a general time-scaling behavior, with the presence of cyclic components. These results indicate that the time dynamics of the events are not Poissonian but long range correlated with periodicities ranging from 12 h to 1 year.
Time Series Analysis of Symbiotic Stars and Cataclysmic Variables
NASA Astrophysics Data System (ADS)
Ren, Jiaying; MacLachlan, G.; Panchmal, A.; Dhuga, K.; Morris, D.
2010-01-01
Symbiotic stars (SSs) and Cataclysmic Variables (CVs) are two families of binary systems which occasionally vary in brightness because of accretion from the secondary star. High frequency oscillations, also known as flickering, are thought to occur because of turbulence in the accretion disk especially in and near the vicinity of the boundary layer between the surface of the compact object and the inner edge of the disk. Lower frequency oscillations are also observed but these are typically associated with the orbital and spin motions of the binary system and may be modulated by the presence of a magnetic field. By studying these variations, we probe the emission regions in these compact systems and gain a better understanding of the accretion process. Time-ordered series of apparent magnitudes for several SSs and CVs, obtained from the American Association of Variable Star Observers (AAVSO), have been analyzed. The analysis techniques include Power Spectral Densities, Rescaled R/S Analysis, and Discrete Wavelet Transforms. The results are used to estimate a Hurst exponent which is a measure of long-range memory dependence and self-similarity.
Spectral Analysis of the Daily Maximum Precipitation Time Series over Iran
NASA Astrophysics Data System (ADS)
Taghavi, F.
2009-04-01
A method of estimating the spectral density of random components of precipitation time series is presented. Precipitation time series consist of periodic components (such as normal annual variation) and random or nondeterministic components which are the deviations from the normal. In this paper, Fourier analysis technique is used to study the daily maximum precipitation data to identify the 24- hour extreme precipitation frequency and to evaluate the risk of flooding over Iran. Time series data form 40 stations are used in the period 1960-2007. As in most analyses, in time series analysis, it is assumed that the data consist of a systematic pattern and random noise which usually makes the pattern difficult to identify.This paper discuses the problem of estimating the spectral density of random components in order to obtain an estimate of predicting future values of the precipitation time series.
Engine Control Improvement through Application of Chaotic Time Series Analysis
Green, J.B., Jr.; Daw, C.S.
2003-07-15
The objective of this program was to investigate cyclic variations in spark-ignition (SI) engines under lean fueling conditions and to develop options to reduce emissions of nitrogen oxides (NOx) and particulate matter (PM) in compression-ignition direct-injection (CIDI) engines at high exhaust gas recirculation (EGR) rates. The CIDI activity builds upon an earlier collaboration between ORNL and Ford examining combustion instabilities in SI engines. Under the original CRADA, the principal objective was to understand the fundamental causes of combustion instability in spark-ignition engines operating with lean fueling. The results of this earlier activity demonstrated that such combustion instabilities are dominated by the effects of residual gas remaining in each cylinder from one cycle to the next. A very simple, low-order model was developed that explained the observed combustion instability as a noisy nonlinear dynamical process. The model concept lead to development of a real-time control strategy that could be employed to significantly reduce cyclic variations in real engines using existing sensors and engine control systems. This collaboration led to the issuance of a joint patent for spark-ignition engine control. After a few years, the CRADA was modified to focus more on EGR and CIDI engines. The modified CRADA examined relationships between EGR, combustion, and emissions in CIDI engines. Information from CIDI engine experiments, data analysis, and modeling were employed to identify and characterize new combustion regimes where it is possible to simultaneously achieve significant reductions in NOx and PM emissions. These results were also used to develop an on-line combustion diagnostic (virtual sensor) to make cycle-resolved combustion quality assessments for active feedback control. Extensive experiments on engines at Ford and ORNL led to the development of the virtual sensor concept that may be able to detect simultaneous reductions in NOx and PM
Chaos in Electronic Circuits: Nonlinear Time Series Analysis
Wheat, Jr., Robert M.
2003-07-01
Chaos in electronic circuits is a phenomenon that has been largely ignored by engineers, manufacturers, and researchers until the early 1990’s and the work of Chua, Matsumoto, and others. As the world becomes more dependent on electronic devices, the detrimental effects of non-normal operation of these devices becomes more significant. Developing a better understanding of the mechanisms involved in the chaotic behavior of electronic circuits is a logical step toward the prediction and prevention of any potentially catastrophic occurrence of this phenomenon. Also, a better understanding of chaotic behavior, in a general sense, could potentially lead to better accuracy in the prediction of natural events such as weather, volcanic activity, and earthquakes. As a first step in this improvement of understanding, and as part of the research being reported here, methods of computer modeling, identifying and analyzing, and producing chaotic behavior in simple electronic circuits have been developed. The computer models were developed using both the Alternative Transient Program (ATP) and Spice, the analysis techniques have been implemented using the C and C++ programming languages, and the chaotically behaving circuits developed using “off the shelf” electronic components.
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.
Hilbert-Huang Transform Analysis of Hydrological and Environmental Time Series
NASA Astrophysics Data System (ADS)
Burr, Tom
2009-06-01
Climatic and hydrologic time series often display periodicities, and thus Fourier spectral analysis sometimes is appropriate. However, time series that are nonstationary, and also perhaps nonlinear, are not well handled by standard Fourier spectral analysis. Methods to handle nonstationarity, such as moving-time-window Fourier spectral analysis, assume linearity and have known limitations regarding the combined frequency and time resolution. For example, if the time series is stationary, then it is well known that better frequency resolution can be achieved by observing a longer time series (more time points). However, if the time series is nonstationary, then shorter time windows are required to estimate the “local in time” spectrum, analogous to using short-memory moving averages that use only the recent past few values to forecast the next value (P. Bloomfield, Fourier Analysis of Time Series: An Introduction, 2nd ed., John Wiley, 2000) because the mean value is changing over time. Therefore, in nonstationary time series analysis, there is a tension between the competing goals of time and frequency resolution. This tension is the reason that N. Huang et al. (Proc. R. Soc. A, 454, 903-995, 1998) introduced the Hilbert-Huang Transform (HHT) as an alternative to moving-time-window Fourier spectral analysis (Bloomfield, 2000).
NASA Astrophysics Data System (ADS)
Peng, Wei; Dai, Wujiao; Santerre, Rock; Cai, Changsheng; Kuang, Cuilin
2017-02-01
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.
Wang, Jin; Sun, Xiangping; Nahavandi, Saeid; Kouzani, Abbas; Wu, Yuchuan; She, Mary
2014-11-01
Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Detrended fluctuation analysis of time series of a firing fusimotor neuron
NASA Astrophysics Data System (ADS)
Blesić, S.; Milošević, S.; Stratimirović, Dj.; Ljubisavljević, M.
We study the interspike intervals (ISI) time series of the spontaneous fusimotor neuron activity by applying the detrended fluctuation analysis that is a modification of the random walk model analysis. Thus, we have found evidence for the white noise characteristics of the ISI time series, which means that the fusimotor activity does not possess temporal correlations. We conclude that such an activity represents the requisite noisy component for occurrence of the stochastic resonance mechanism in the neural coordination of muscle spindles.
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.
Time series analysis of InSAR data: Methods and trends
NASA Astrophysics Data System (ADS)
Osmanoğlu, Batuhan; Sunar, Filiz; Wdowinski, Shimon; Cabral-Cano, Enrique
2016-05-01
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.
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
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
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.
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
Goto, Kensuke; Kumarendran, Balachandran; Mettananda, Sachith; Gunasekara, Deepa; Fujii, Yoshito; Kaneko, Satoshi
2013-01-01
In tropical and subtropical regions of eastern and South-eastern Asia, dengue fever (DF) and dengue hemorrhagic fever (DHF) outbreaks occur frequently. Previous studies indicate an association between meteorological variables and dengue incidence using time series analyses. The impacts of meteorological changes can affect dengue outbreak. However, difficulties in collecting detailed time series data in developing countries have led to common use of monthly data in most previous studies. In addition, time series analyses are often limited to one area because of the difficulty in collecting meteorological and dengue incidence data in multiple areas. To gain better understanding, we examined the effects of meteorological factors on dengue incidence in three geographically distinct areas (Ratnapura, Colombo, and Anuradhapura) of Sri Lanka by time series analysis of weekly data. The weekly average maximum temperature and total rainfall and the total number of dengue cases from 2005 to 2011 (7 years) were used as time series data in this study. Subsequently, time series analyses were performed on the basis of ordinary least squares regression analysis followed by the vector autoregressive model (VAR). In conclusion, weekly average maximum temperatures and the weekly total rainfall did not significantly affect dengue incidence in three geographically different areas of Sri Lanka. However, the weekly total rainfall slightly influenced dengue incidence in the cities of Colombo and Anuradhapura.
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.
Chaos Analysis of Precipitation Time Series in the upper Magdalena River Basin
NASA Astrophysics Data System (ADS)
Santiago Duarte Prieto, Freddy; Hernández Murcia, Oscar Eduardo; Corzo Perez, Gerald Augusto; Santos Granados, Germán Ricardo
2017-04-01
An analysis of chaos realized in the upper Magdalena River Basin (UMRB) for two precipitation time series is presented. The first time series was collected from 129 ground rain gauges stations (period 1970 to 2011, diary) located along the UMRB. The second modeled time series were derived from a Global Climate Model (GCM: MPI-ESM-MR), (1850 to 2089, diary) with a resolution 1.875°x1.875°. The time series were utilized to reconstruct the phase space by applying the Time-Delay Method, which finds an appropriate time-delay (Autocorrelation and Mutual Information) and embedding dimensions (Correlation Dimension, False Nearest Neighbors and Caós method) to unfold the attractor. This information was then utilized to calculate the Lyapunov exponents (-0.01 a 0.60). The Lyapunov exponents shows that 97% of ground rain gauges presents deterministic chaos for an interval of 5 days. The same pattern was found in the GCM time series for a rainfall accumulation interval of 15 days. In addition, both time series becomes completely deterministic for a rainfall accumulation of 30 days or more. These results show that the precipitation data set has deterministic chaos, which have the potential to improve rainfall forecasting.
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.
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.
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.
2017-02-01
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.
The application of complex network time series analysis in turbulent heated jets.
Charakopoulos, A Κ; 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.
A Markovian Entropy Measure for the Analysis of Calcium Activity Time Series.
Marken, John P; Halleran, Andrew D; Rahman, Atiqur; Odorizzi, Laura; LeFew, Michael C; Golino, Caroline A; Kemper, Peter; Saha, Margaret S
2016-01-01
Methods to analyze the dynamics of calcium activity often rely on visually distinguishable features in time series data such as spikes, waves, or oscillations. However, systems such as the developing nervous system display a complex, irregular type of calcium activity which makes the use of such methods less appropriate. Instead, for such systems there exists a class of methods (including information theoretic, power spectral, and fractal analysis approaches) which use more fundamental properties of the time series to analyze the observed calcium dynamics. We present a new analysis method in this class, the Markovian Entropy measure, which is an easily implementable calcium time series analysis method which represents the observed calcium activity as a realization of a Markov Process and describes its dynamics in terms of the level of predictability underlying the transitions between the states of the process. We applied our and other commonly used calcium analysis methods on a dataset from Xenopus laevis neural progenitors which displays irregular calcium activity and a dataset from murine synaptic neurons which displays activity time series that are well-described by visually-distinguishable features. We find that the Markovian Entropy measure is able to distinguish between biologically distinct populations in both datasets, and that it can separate biologically distinct populations to a greater extent than other methods in the dataset exhibiting irregular calcium activity. These results support the benefit of using the Markovian Entropy measure to analyze calcium dynamics, particularly for studies using time series data which do not exhibit easily distinguishable features.
A Markovian Entropy Measure for the Analysis of Calcium Activity Time Series
Rahman, Atiqur; Odorizzi, Laura; LeFew, Michael C.; Golino, Caroline A.; Kemper, Peter; Saha, Margaret S.
2016-01-01
Methods to analyze the dynamics of calcium activity often rely on visually distinguishable features in time series data such as spikes, waves, or oscillations. However, systems such as the developing nervous system display a complex, irregular type of calcium activity which makes the use of such methods less appropriate. Instead, for such systems there exists a class of methods (including information theoretic, power spectral, and fractal analysis approaches) which use more fundamental properties of the time series to analyze the observed calcium dynamics. We present a new analysis method in this class, the Markovian Entropy measure, which is an easily implementable calcium time series analysis method which represents the observed calcium activity as a realization of a Markov Process and describes its dynamics in terms of the level of predictability underlying the transitions between the states of the process. We applied our and other commonly used calcium analysis methods on a dataset from Xenopus laevis neural progenitors which displays irregular calcium activity and a dataset from murine synaptic neurons which displays activity time series that are well-described by visually-distinguishable features. We find that the Markovian Entropy measure is able to distinguish between biologically distinct populations in both datasets, and that it can separate biologically distinct populations to a greater extent than other methods in the dataset exhibiting irregular calcium activity. These results support the benefit of using the Markovian Entropy measure to analyze calcium dynamics, particularly for studies using time series data which do not exhibit easily distinguishable features. PMID:27977764
Single-Case Time Series with Bayesian Analysis: A Practitioner's Guide.
ERIC Educational Resources Information Center
Jones, W. Paul
2003-01-01
This article illustrates a simplified time series analysis for use by the counseling researcher practitioner in single-case baseline plus intervention studies with a Bayesian probability analysis to integrate findings from replications. The C statistic is recommended as a primary analysis tool with particular relevance in the context of actual…
VSAA: A method of tracing variable frequencies in time series analysis
NASA Astrophysics Data System (ADS)
Tsantilas, S.; Rovithis-Livaniou, H.
2008-12-01
In this work we discuss a new method of time series analysis. This new method -Variable Sine Algorithmic Analysis, (VSAA)- is based on a single sine function with variable coefficients and is powered by the simplex algorithm. It can be applied to almost every type of time series, e.g., to orbital period variations, pulsating stars, sunspot activity, etc., providing a very accurate and simple analysis. Especially in cases of phenomena demonstrating variable frequencies but triggered by a single mechanism, VSAA provides a straightforward solution. Applications are given to synthetic and real data.
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. Copyright © 2012 Elsevier Ltd. All rights reserved.
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).
NASA Astrophysics Data System (ADS)
Li, Y.; Bai, C.
2013-12-01
Predicting the distribution of engineered nanomaterials (ENMs) in the environment will provide critical information for risk assessment and policy development to regulate these emerging contaminants. The fate and transport of ENMs in natural subsurface environment is a function of time and subjected to various uncertainties. Here, we explore the feasibility of applying advanced statistical methodologies (i.e., time series analysis) to forecast the ENM concentration distribution in porous media with time. Hypothetical scenarios for the release of nanoparticles into a subsurface aquifer were simulated using randomly generated permeability fields that were based on a mildly heterogeneous field site in Oscoda, MI. A modified Modular Three-Dimensional Multispecies Transport Model (MT3DMS) with a capability to simulate ENM transport was used for simulation. The time series data of five ENM distribution parameters, including the far-front of aqueous phase ENM plume, the far-front of attached phase EMN distribution, the x-centroid of aqueous phase ENM plume, and the x-centroid of attached phase ENM distribution, were calculated based on the simulated results of fifteen random fields. Time series analysis was then applied to forecast the future values of these ENM distribution parameters. The time series predicted future values and confidence interval were find in good agreement with numerically simulated values. This proof-of-concept effort demonstrates the possibility of applying time series analysis to predict the ENM distribution at a field site.
NASA Astrophysics Data System (ADS)
Scafetta, Nicola; West, Bruce J.
2004-04-01
The multiresolution diffusion entropy analysis is used to evaluate the stochastic information left in a time series after systematic removal of certain non-stationarities. This method allows us to establish whether the identified patterns are sufficient to capture all relevant information contained in a time series. If they do not, the method suggests the need for further interpretation to explain the residual memory in the signal. We apply the multiresolution diffusion entropy analysis to the daily count of births to teens in Texas from 1964 through 2000 because it is a typical example of a non-stationary time series, having an anomalous trend, an annual variation, as well as short time fluctuations. The analysis is repeated for the three main racial/ethnic groups in Texas (White, Hispanic and African American), as well as, to married and unmarried teens during the years from 1994 to 2000 and we study the differences that emerge among the groups.
Mousavi, S M; Reihani, S N Seyed; Anvari, G; Anvari, M; Alinezhad, H G; Tabar, M Reza Rahimi
2017-07-06
We propose a nonlinear method for the analysis of the time series for the spatial position of a bead trapped in optical tweezers, which enables us to reconstruct its dynamical equation of motion. The main advantage of the method is that all the functions and parameters of the dynamics are determined directly (non-parametrically) from the measured series. It also allows us to determine, for the first time to our knowledge, the spatial-dependence of the diffusion coefficient of a bead in an optical trap, and to demonstrate that it is not in general constant. This is in contrast with the main assumption of the popularly-used power spectrum calibration method. The proposed method is validated via synthetic time series for the bead position with spatially-varying diffusion coefficients. Our detailed analysis of the measured time series reveals that the power spectrum analysis overestimates considerably the force constant.
Hayashi, Hideaki; Shibanoki, Taro; Shima, Keisuke; Kurita, Yuichi; Tsuji, Toshio
2015-12-01
This paper proposes a probabilistic neural network (NN) developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model with a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into an NN, which is named a time-series discriminant component network (TSDCN), so that parameters of dimensionality reduction and classification can be obtained simultaneously as network coefficients according to a backpropagation through time-based learning algorithm with the Lagrange multiplier method. The TSDCN is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. The validity of the TSDCN is demonstrated for high-dimensional artificial data and electroencephalogram signals in the experiments conducted during the study.
Simulation of Active and Passive Millimeter-Wave (35GHz) Sensors by Time Series Analysis.
1982-11-01
Wave (MMW) Radiometric Data",ARBRL-TR-02338,July 1981. 3 Lon-Mu Liu,"User’s Manual for BMDQ2T(TSPACK) Time Series Analysis (Box- Jenkins)",Technical...ESTIMATED AUTOCORRELATION AND PARTIAL AUTOCORRELATION FUNCIONS OF RESIDUALS (ARTMA(2,0,(2))) MEAN - -.0000403 STANDARD DEVIATION - .0229 ACF 670...R. T.,"A Time Series and !ntervention Analysis of Millimeter-Wave (MMW) Radiometric Data", ARBRL-TR-02338, July 1981. 3. Lon-Mu Liu,"User’s Manual
Attrition and Augmentation Biases in Time Series Analysis: Evaluation of Clinical Programs.
ERIC Educational Resources Information Center
Fitz, Don; Tryon, Warren W.
1989-01-01
Methods of using simplified time series analysis (STSA) in evaluating clinical programs are discussed. STSA assists in addressing problems of attrition/augmentation of subjects in programs with changing populations. Combining individually calculated "C" statistics in a simple aggregate analysis of restraint usage by nursing home staff…
Gavrishchaka, Valeriy; Senyukova, Olga; Davis, Kristina
2015-01-01
Previously, we have proposed to use complementary complexity measures discovered by boosting-like ensemble learning for the enhancement of quantitative indicators dealing with necessarily short physiological time series. We have confirmed robustness of such multi-complexity measures for heart rate variability analysis with the emphasis on detection of emerging and intermittent cardiac abnormalities. Recently, we presented preliminary results suggesting that such ensemble-based approach could be also effective in discovering universal meta-indicators for early detection and convenient monitoring of neurological abnormalities using gait time series. Here, we argue and demonstrate that these multi-complexity ensemble measures for gait time series analysis could have significantly wider application scope ranging from diagnostics and early detection of physiological regime change to gait-based biometrics applications.
Time Series Data Analysis of Wireless Sensor Network Measurements of Temperature.
Bhandari, Siddhartha; Bergmann, Neil; Jurdak, Raja; Kusy, Branislav
2017-05-26
Wireless sensor networks have gained significant traction in environmental signal monitoring and analysis. The cost or lifetime of the system typically depends on the frequency at which environmental phenomena are monitored. If sampling rates are reduced, energy is saved. Using empirical datasets collected from environmental monitoring sensor networks, this work performs time series analyses of measured temperature time series. Unlike previous works which have concentrated on suppressing the transmission of some data samples by time-series analysis but still maintaining high sampling rates, this work investigates reducing the sampling rate (and sensor wake up rate) and looks at the effects on accuracy. Results show that the sampling period of the sensor can be increased up to one hour while still allowing intermediate and future states to be estimated with interpolation RMSE less than 0.2 °C and forecasting RMSE less than 1 °C.
Simulation of active and passive millimeter-wave (35 GHz) sensors by time series analysis
NASA Astrophysics Data System (ADS)
Strenzwilk, D. F.; Maruyama, R. T.
1982-11-01
Analog voltage signals from a millimeter-wave (MMW) radiometer (passive sensor) and radar (active sensor) were collected over varying grassy terrains at Aberdeen Proving Ground (APG), Maryland in July 1980. These measurements were made as part of continuing studies of MMW sensors for smart munitions. The signals were digitized at a rate of 2,000 observations per second and then analyzed by the Box and Jenkins time series approach. This analysis reports on the characterization of these data sets. The passive time series signals resulted in a simple autoregressive-moving average process, similar to a previous set of data taken at Rome Air Development Center in Rome, N.Y. by Ballistic Research Laboratory. On the other hand, the radar data (active sensor) required a data transformation to enhance the analysis. In both cases the signals were well characterized using the Box-Jenkins time series approach.
Time Series Data Analysis of Wireless Sensor Network Measurements of Temperature
Bhandari, Siddhartha; Bergmann, Neil; Jurdak, Raja; Kusy, Branislav
2017-01-01
Wireless sensor networks have gained significant traction in environmental signal monitoring and analysis. The cost or lifetime of the system typically depends on the frequency at which environmental phenomena are monitored. If sampling rates are reduced, energy is saved. Using empirical datasets collected from environmental monitoring sensor networks, this work performs time series analyses of measured temperature time series. Unlike previous works which have concentrated on suppressing the transmission of some data samples by time-series analysis but still maintaining high sampling rates, this work investigates reducing the sampling rate (and sensor wake up rate) and looks at the effects on accuracy. Results show that the sampling period of the sensor can be increased up to one hour while still allowing intermediate and future states to be estimated with interpolation RMSE less than 0.2 °C and forecasting RMSE less than 1 °C. PMID:28587133
Using Time Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit
Kennedy, Curtis E; Aoki, Noriaki; Mariscalco, Michele; Turley, James P
2015-01-01
Objectives To build and test cardiac arrest prediction models in a pediatric intensive care unit, using time series analysis as input, and to measure changes in prediction accuracy attributable to different classes of time series data. Methods A retrospective cohort study of pediatric intensive care patients over a 30 month study period. All subjects identified by code documentation sheets with matches in hospital physiologic and laboratory data repositories and who underwent chest compressions for two minutes were included as arrest cases. Controls were randomly selected from patients that did not experience arrest and who survived to discharge. Modeling data was based on twelve hours of data preceding the arrest (reference time for controls). Measurements and Main Results 103 cases of cardiac arrest and 109 control cases were used to prepare a baseline data set that consisted of 1025 variables in four data classes: multivariate, raw time series, clinical calculations, and time series trend analysis. We trained 20 arrest prediction models using a matrix of five feature sets (combinations of data classes) with four modeling algorithms: linear regression, decision tree, neural network and support vector machine. The reference model (multivariate data with regression algorithm) had an accuracy of 78% and 87% area under the receiver operating characteristic curve (AUROC). The best model (multivariate + trend analysis data with support vector machine algorithm) had an accuracy of 94% and 98% AUROC. Conclusions Cardiac arrest predictions based on a traditional model built with multivariate data and a regression algorithm misclassified cases 3.7 times more frequently than predictions that included time series trend analysis and built with a support vector machine algorithm. Although the final model lacks the specificity necessary for clinical application, we have demonstrated how information from time series data can be used to increase the accuracy of clinical
Using Time Series Analysis to Predict Cardiac Arrest in a PICU.
Kennedy, Curtis E; Aoki, Noriaki; Mariscalco, Michele; Turley, James P
2015-11-01
To build and test cardiac arrest prediction models in a PICU, using time series analysis as input, and to measure changes in prediction accuracy attributable to different classes of time series data. Retrospective cohort study. Thirty-one bed academic PICU that provides care for medical and general surgical (not congenital heart surgery) patients. Patients experiencing a cardiac arrest in the PICU and requiring external cardiac massage for at least 2 minutes. None. One hundred three cases of cardiac arrest and 109 control cases were used to prepare a baseline dataset that consisted of 1,025 variables in four data classes: multivariate, raw time series, clinical calculations, and time series trend analysis. We trained 20 arrest prediction models using a matrix of five feature sets (combinations of data classes) with four modeling algorithms: linear regression, decision tree, neural network, and support vector machine. The reference model (multivariate data with regression algorithm) had an accuracy of 78% and 87% area under the receiver operating characteristic curve. The best model (multivariate + trend analysis data with support vector machine algorithm) had an accuracy of 94% and 98% area under the receiver operating characteristic curve. Cardiac arrest predictions based on a traditional model built with multivariate data and a regression algorithm misclassified cases 3.7 times more frequently than predictions that included time series trend analysis and built with a support vector machine algorithm. Although the final model lacks the specificity necessary for clinical application, we have demonstrated how information from time series data can be used to increase the accuracy of clinical prediction models.
Jandoc, Racquel; Burden, Andrea M; Mamdani, Muhammad; Lévesque, Linda E; Cadarette, Suzanne M
2015-08-01
To describe the use and reporting of interrupted time series methods in drug utilization research. We completed a systematic search of MEDLINE, Web of Science, and reference lists to identify English language articles through to December 2013 that used interrupted time series methods in drug utilization research. We tabulated the number of studies by publication year and summarized methodological detail. We identified 220 eligible empirical applications since 1984. Only 17 (8%) were published before 2000, and 90 (41%) were published since 2010. Segmented regression was the most commonly applied interrupted time series method (67%). Most studies assessed drug policy changes (51%, n = 112); 22% (n = 48) examined the impact of new evidence, 18% (n = 39) examined safety advisories, and 16% (n = 35) examined quality improvement interventions. Autocorrelation was considered in 66% of studies, 31% reported adjusting for seasonality, and 15% accounted for nonstationarity. Use of interrupted time series methods in drug utilization research has increased, particularly in recent years. Despite methodological recommendations, there is large variation in reporting of analytic methods. Developing methodological and reporting standards for interrupted time series analysis is important to improve its application in drug utilization research, and we provide recommendations for consideration. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
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.
Charakopoulos, A K; Karakasidis, T E; Papanicolaou, P N; Liakopoulos, A
2014-03-01
In the present work we approach the hydrodynamic problem of discriminating the state of the turbulent fluid region as a function of the distance from the axis of a turbulent jet axis. More specifically, we analyzed temperature fluctuations in vertical turbulent heated jets where temperature time series were recorded along a horizontal line through the jet axis. We employed data from different sets of experiments with various initial conditions out of circular and elliptical shaped nozzles in order to identify time series taken at the jet axis, and discriminate them from those taken near the boundary with ambient fluid using nonconventional hydrodynamics methods. For each temperature time series measured at a different distance from jet axis, we estimated mainly nonlinear measures such as mutual information combined with descriptive statistics measures, as well as some linear and nonlinear dynamic detectors such as Hurst exponent, detrended fluctuation analysis, and Hjorth parameters. The results obtained in all cases have shown that the proposed methodology allows us to distinguish the flow regime around the jet axis and identify the time series corresponding to the jet axis in agreement with the conventional statistical hydrodynamic method. Furthermore, in order to reject the null hypothesis that the time series originate from a stochastic process, we applied the surrogate data method.
Yamashita Rios de Sousa, Arthur Matsuo; Takayasu, Hideki; Takayasu, Misako
2017-01-01
We extend the concept of statistical symmetry as the invariance of a probability distribution under transformation to analyze binary sign time series data of price difference from the foreign exchange market. We model segments of the sign time series as Markov sequences and apply a local hypothesis test to evaluate the symmetries of independence and time reversion in different periods of the market. For the test, we derive the probability of a binary Markov process to generate a given set of number of symbol pairs. Using such analysis, we could not only segment the time series according the different behaviors but also characterize the segments in terms of statistical symmetries. As a particular result, we find that the foreign exchange market is essentially time reversible but this symmetry is broken when there is a strong external influence.
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.
Time Series Analysis of Sound Data on Interactive Calling Behavior of Japanese Tree Frogs
NASA Astrophysics Data System (ADS)
Horai, Shunsuke; Aihara, Ikkyu; Aihara, Kazuyuki
We have analyzed time series data of sound on interactive calling behavior of two male Japanese tree frogs (Hyla japonica Nihon-Ama-Gaeru). First, we have extracted two time series data mainly corresponding to respective frogs from the single time series data of calls of two frogs by the free and cross-platform sound editor Audacity. Then, we have quantitatively analyzed timing and inter-call intervals of respective frogs. Finally, we have characterized nonstationarily temporal change of the interactive calling behavior of two frogs by analysis of the cross recurrence plot. The results have shown that a pair of male frogs called in almost anti-phase synchronization after a short-term period of nearly in-phase synchronization, which implies existence of complex interactive calling behavior of two male frogs.
The application of neural network to the analysis of earthquake precursor chaotic time series
NASA Astrophysics Data System (ADS)
Qiang, Li
2000-07-01
Artificial neural network (NN) is such a model as to imitate the structure and intelligence feature of human brain. It has strong nonlinear mapping function. To introduce NN into the study of earthquake prediction is not only an extension of the application of artificial neural network model but also a new try for precursor observation to serve the earthquake prediction. In this paper, we analyzed the predictability of time series and gave a method of application of artificial neural network in forecasting earthquake precursor chaotic time series. Besides, taking the ground tilt observation of Jiangning and Xuzhou Station, the bulk strain observation of Liyang station as examples, we analyzed and forecasted their time series respectively. It is indicated that the precision of this method can meet the needs of practical task and therefore of great value in the application to the future practical earthquake analysis and prediction.
Takayasu, Hideki; Takayasu, Misako
2017-01-01
We extend the concept of statistical symmetry as the invariance of a probability distribution under transformation to analyze binary sign time series data of price difference from the foreign exchange market. We model segments of the sign time series as Markov sequences and apply a local hypothesis test to evaluate the symmetries of independence and time reversion in different periods of the market. For the test, we derive the probability of a binary Markov process to generate a given set of number of symbol pairs. Using such analysis, we could not only segment the time series according the different behaviors but also characterize the segments in terms of statistical symmetries. As a particular result, we find that the foreign exchange market is essentially time reversible but this symmetry is broken when there is a strong external influence. PMID:28542208
Interventions to control MRSA: high time for time-series analysis?
Harbarth, S; Samore, M H
2008-09-01
Time-series methods are useful in quasi-experimental study designs in which rates of antibiotic-resistant infections are ascertained before and after an intervention. However, uncertainties remain regarding the use of time-series analysis as an appropriate research methodology for analysing the effect of infection control interventions and antibiotic policies on the epidemiology of methicillin-resistant Staphylococcus aureus (MRSA). In particular, there is still a substantial gap in our understanding of what actually happens to MRSA incidence when a planned intervention is made on use of one or more antibiotic drug classes.
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.
Measuring teaching through hormones and time series analysis: Towards a comparative framework.
Ravignani, Andrea; Sonnweber, Ruth
2015-01-01
Arguments about the nature of teaching have depended principally on naturalistic observation and some experimental work. Additional measurement tools, and physiological variations and manipulations can provide insights on the intrinsic structure and state of the participants better than verbal descriptions alone: namely, time-series analysis, and examination of the role of hormones and neuromodulators on the behaviors of teacher and pupil.
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.
Discriminant Analysis of Time Series in the Presence of Within-Group Spectral Variability.
Krafty, Robert T
2016-07-01
Many studies record replicated time series epochs from different groups with the goal of using frequency domain properties to discriminate between the groups. In many applications, there exists variation in cyclical patterns from time series in the same group. Although a number of frequency domain methods for the discriminant analysis of time series have been explored, there is a dearth of models and methods that account for within-group spectral variability. This article proposes a model for groups of time series in which transfer functions are modeled as stochastic variables that can account for both between-group and within-group differences in spectra that are identified from individual replicates. An ensuing discriminant analysis of stochastic cepstra under this model is developed to obtain parsimonious measures of relative power that optimally separate groups in the presence of within-group spectral variability. The approach possess favorable properties in classifying new observations and can be consistently estimated through a simple discriminant analysis of a finite number of estimated cepstral coefficients. Benefits in accounting for within-group spectral variability are empirically illustrated in a simulation study and through an analysis of gait variability.
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),…
The Effect of Divorce on Suicide in Japan: A Time Series Analysis, 1950-1980.
ERIC Educational Resources Information Center
Stack, Steve
1992-01-01
Explored relationship between divorce and suicide in Japan. Time series analysis was unable to substantiate divorce-suicide pattern for Japan. Although research did not offer support for relationship between divorce and suicide which Durkheim predicted, it did corroborate Durkheim's general theory of family integration. (Author/NB)
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.
Economic Conditions and the Divorce Rate: A Time-Series Analysis of the Postwar United States.
ERIC Educational Resources Information Center
South, Scott J.
1985-01-01
Challenges the belief that the divorce rate rises during prosperity and falls during economic recessions. Time-series regression analysis of postwar United States reveals small but positive effects of unemployment on divorce rate. Stronger influences on divorce rates are changes in age structure and labor-force participation rate of women.…
Discriminant Analysis of Time Series in the Presence of Within-Group Spectral Variability
Krafty, Robert T.
2016-01-01
Many studies record replicated time series epochs from different groups with the goal of using frequency domain properties to discriminate between the groups. In many applications, there exists variation in cyclical patterns from time series in the same group. Although a number of frequency domain methods for the discriminant analysis of time series have been explored, there is a dearth of models and methods that account for within-group spectral variability. This article proposes a model for groups of time series in which transfer functions are modeled as stochastic variables that can account for both between-group and within-group differences in spectra that are identified from individual replicates. An ensuing discriminant analysis of stochastic cepstra under this model is developed to obtain parsimonious measures of relative power that optimally separate groups in the presence of within-group spectral variability. The approach possess favorable properties in classifying new observations and can be consistently estimated through a simple discriminant analysis of a finite number of estimated cepstral coefficients. Benefits in accounting for within-group spectral variability are empirically illustrated in a simulation study and through an analysis of gait variability. PMID:27695143
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.
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…
Harmonic analysis of dense time series of landsat imagery for modeling change in forest conditions
Barry Tyler. Wilson
2015-01-01
This study examined the utility of dense time series of Landsat imagery for small area estimation and mapping of change in forest conditions over time. The study area was a region in north central Wisconsin for which Landsat 7 ETM+ imagery and field measurements from the Forest Inventory and Analysis program are available for the decade of 2003 to 2012. For the periods...
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…
The Effect of Divorce on Suicide in Japan: A Time Series Analysis, 1950-1980.
ERIC Educational Resources Information Center
Stack, Steve
1992-01-01
Explored relationship between divorce and suicide in Japan. Time series analysis was unable to substantiate divorce-suicide pattern for Japan. Although research did not offer support for relationship between divorce and suicide which Durkheim predicted, it did corroborate Durkheim's general theory of family integration. (Author/NB)
Enveloping Spectral Surfaces: Covariate Dependent Spectral Analysis of Categorical Time Series
Krafty, Robert T.; Xiong, Shuangyan; Stoffer, David S.; Buysse, Daniel J.; Hall, Martica
2014-01-01
Motivated by problems in Sleep Medicine and Circadian Biology, we present a method for the analysis of cross-sectional categorical time series collected from multiple subjects where the effect of static continuous-valued covariates is of interest. Toward this goal, we extend the spectral envelope methodology for the frequency domain analysis of a single categorical process to cross-sectional categorical processes that are possibly covariate dependent. The analysis introduces an enveloping spectral surface for describing the association between the frequency domain properties of qualitative time series and covariates. The resulting surface offers an intuitively interpretable measure of association between covariates and a qualitative time series by finding the maximum possible conditional power at a given frequency from scalings of the qualitative time series conditional on the covariates. The optimal scalings that maximize the power provide scientific insight by identifying the aspects of the qualitative series which have the most pronounced periodic features at a given frequency conditional on the value of the covariates. To facilitate the assessment of the dependence of the enveloping spectral surface on the covariates, we include a theory for analyzing the partial derivatives of the surface. Our approach is entirely nonparametric, and we present estimation and asymptotics in the setting of local polynomial smoothing. PMID:24790257
PyRQA-Conducting recurrence quantification analysis on very long time series efficiently
NASA Astrophysics Data System (ADS)
Rawald, Tobias; Sips, Mike; Marwan, Norbert
2017-07-01
PyRQA is a software package that efficiently conducts recurrence quantification analysis (RQA) on time series consisting of more than one million data points. RQA is a method from non-linear time series analysis that quantifies the recurrent behaviour of systems. Existing implementations to RQA are not capable of analysing such very long time series at all or require large amounts of time to calculate the quantitative measures. PyRQA overcomes their limitations by conducting the RQA computations in a highly parallel manner. Building on the OpenCL framework, PyRQA leverages the computing capabilities of a variety of parallel hardware architectures, such as GPUs. The underlying computing approach partitions the RQA computations and enables to employ multiple compute devices at the same time. The goal of this publication is to demonstrate the features and the runtime efficiency of PyRQA. For this purpose we employ a real-world example, comparing the dynamics of two climatological time series, and a synthetic example, reducing the runtime regarding the analysis of a series consisting of over one million data points from almost eight hours using state-of-the-art RQA software to roughly 69 s using PyRQA.
Nonlinear time series analysis of ground-level ozone dynamics in Southern Taiwan.
Weng, Yu-Chi; Chang, Ni-Bin; Lee, T Y
2008-05-01
The ozone pollution at ground level in rural and urban areas has been a long-standing problem in the world. This paper focuses on estimating self-affined nature of nonlinearity of ground-level peak ozone time series, which is analyzed by two nonlinear fractal statistical methods, including R/S analysis and BDS test. To explore the underlying structure of ozone observations at ground level and improve the forecasting capacity in urban region, practical implementation was assessed by a case study via collecting and analyzing the monitoring data at Chaojhou and Zenwu in the Kaohsiung metropolitan region, Taiwan. Based on R/S analysis, the time series can be identified as persistent and long-memory processes with Hurst exponents of both about 0.75. In addition, the V statistics specifies possible fluctuation cycle lengths of 32, 170, and 420 day simultaneously. Such results are consistent with the regional meteorological conditions leading to help characterize the regional scale ozone behavioral trend. Furthermore, the BDS test results confirm a strong nonlinearity of both time series associated with these two cities. Yet in both cases, nonlinearity implies chaos. The R/S analysis and BDS test provide strong evidence for nonlinearity and fractality of ozone time series due to noisy chaos, and we could not rule out the possibility of deterministic chaos in tropospheric ozone system.
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].
1980-12-05
statistical model identification," IEEE Transactions on Automatic Control, AC-19, 716-722, 1974. Akaike, H., " Canonical correlation analysis of time series...OPPLICATION TO LNESTHESIA J]EVEL SLASSIFICATION BY EEG ANALYSIS By f/ J ILL/qERSCH ’ TECHNICAL REPORT NO. 294 ;DECU 80 DTIC ELECTE SMAR 2719g81 Prepared Under...APPLICATION TO ANESTHESIA LEVEL CLASSIFICATION BY EEG ANALYSIS By Will Cersch 1. INTRODUCTION. This paper presents the theory and a prototypic example of an
Visibility graph analysis for re-sampled time series from auto-regressive stochastic processes
NASA Astrophysics Data System (ADS)
Zhang, Rong; Zou, Yong; Zhou, Jie; Gao, Zhong-Ke; Guan, Shuguang
2017-01-01
Visibility graph (VG) and horizontal visibility graph (HVG) play a crucial role in modern complex network approaches to nonlinear time series analysis. However, depending on the underlying dynamic processes, it remains to characterize the exponents of presumably exponential degree distributions. It has been recently conjectured that there is a critical value of exponent λc = ln 3 / 2 , which separates chaotic from correlated stochastic processes. Here, we systematically apply (H)VG analysis to time series from autoregressive (AR) models, which confirms the hypothesis that an increased correlation length results in larger values of λ > λc. On the other hand, we numerically find a regime of negatively correlated process increments where λ < λc, which is in contrast to this hypothesis. Furthermore, by constructing graphs based on re-sampled time series, we find that network measures show non-trivial dependencies on the autocorrelation functions of the processes. We propose to choose the decorrelation time as the maximal re-sampling delay for the algorithm. Our results are detailed for time series from AR(1) and AR(2) processes.
Multiscale multifractal detrended cross-correlation analysis of financial time series
NASA Astrophysics Data System (ADS)
Shi, Wenbin; Shang, Pengjian; Wang, Jing; Lin, Aijing
2014-06-01
In this paper, we introduce a method called multiscale multifractal detrended cross-correlation analysis (MM-DCCA). The method allows us to extend the description of the cross-correlation properties between two time series. MM-DCCA may provide new ways of measuring the nonlinearity of two signals, and it helps to present much richer information than multifractal detrended cross-correlation analysis (MF-DCCA) by sweeping all the range of scale at which the multifractal structures of complex system are discussed. Moreover, to illustrate the advantages of this approach we make use of the MM-DCCA to analyze the cross-correlation properties between financial time series. We show that this new method can be adapted to investigate stock markets under investigation. It can provide a more faithful and more interpretable description of the dynamic mechanism between financial time series than traditional MF-DCCA. We also propose to reduce the scale ranges to analyze short time series, and some inherent properties which remain hidden when a wide range is used may exhibit perfectly in this way.
Spatial analysis of precipitation time series over the Upper Indus Basin
NASA Astrophysics Data System (ADS)
Latif, Yasir; Yaoming, Ma; Yaseen, Muhammad
2016-12-01
The upper Indus basin (UIB) holds one of the most substantial river systems in the world, contributing roughly half of the available surface water in Pakistan. This water provides necessary support for agriculture, domestic consumption, and hydropower generation; all critical for a stable economy in Pakistan. This study has identified trends, analyzed variability, and assessed changes in both annual and seasonal precipitation during four time series, identified herein as: (first) 1961-2013, (second) 1971-2013, (third) 1981-2013, and (fourth) 1991-2013, over the UIB. This study investigated spatial characteristics of the precipitation time series over 15 weather stations and provides strong evidence of annual precipitation by determining significant trends at 6 stations (Astore, Chilas, Dir, Drosh, Gupis, and Kakul) out of the 15 studied stations, revealing a significant negative trend during the fourth time series. Our study also showed significantly increased precipitation at Bunji, Chitral, and Skardu, whereas such trends at the rest of the stations appear insignificant. Moreover, our study found that seasonal precipitation decreased at some locations (at a high level of significance), as well as periods of scarce precipitation during all four seasons. The observed decreases in precipitation appear stronger and more significant in autumn; having 10 stations exhibiting decreasing precipitation during the fourth time series, with respect to time and space. Furthermore, the observed decreases in precipitation appear robust and more significant for regions at high elevation (>1300 m). This analysis concludes that decreasing precipitation dominated the UIB, both temporally and spatially including in the higher areas.
A Space Affine Matching Approach to fMRI Time Series Analysis.
Chen, Liang; Zhang, Weishi; Liu, Hongbo; Feng, Shigang; Chen, C L Philip; Wang, Huili
2016-07-01
For fMRI time series analysis, an important challenge is to overcome the potential delay between hemodynamic response signal and cognitive stimuli signal, namely the same frequency but different phase (SFDP) problem. In this paper, a novel space affine matching feature is presented by introducing the time domain and frequency domain features. The time domain feature is used to discern different stimuli, while the frequency domain feature to eliminate the delay. And then we propose a space affine matching (SAM) algorithm to match fMRI time series by our affine feature, in which a normal vector is estimated using gradient descent to explore the time series matching optimally. The experimental results illustrate that the SAM algorithm is insensitive to the delay between the hemodynamic response signal and the cognitive stimuli signal. Our approach significantly outperforms GLM method while there exists the delay. The approach can help us solve the SFDP problem in fMRI time series matching and thus of great promise to reveal brain dynamics.
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.
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
Analysis of temperature time-series: Embedding dynamics into the MDS method
NASA Astrophysics Data System (ADS)
Lopes, António M.; Tenreiro Machado, J. A.
2014-04-01
Global warming and the associated climate changes are being the subject of intensive research due to their major impact on social, economic and health aspects of the human life. Surface temperature time-series characterise Earth as a slow dynamics spatiotemporal system, evidencing long memory behaviour, typical of fractional order systems. Such phenomena are difficult to model and analyse, demanding for alternative approaches. This paper studies the complex correlations between global temperature time-series using the Multidimensional scaling (MDS) approach. MDS provides a graphical representation of the pattern of climatic similarities between regions around the globe. The similarities are quantified through two mathematical indices that correlate the monthly average temperatures observed in meteorological stations, over a given period of time. Furthermore, time dynamics is analysed by performing the MDS analysis over slices sampling the time series. MDS generates maps describing the stations' locus in the perspective that, if they are perceived to be similar to each other, then they are placed on the map forming clusters. We show that MDS provides an intuitive and useful visual representation of the complex relationships that are present among temperature time-series, which are not perceived on traditional geographic maps. Moreover, MDS avoids sensitivity to the irregular distribution density of the meteorological stations.
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.
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.
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 continuous GPS data in Central Java 2010-2015
NASA Astrophysics Data System (ADS)
Ramadian, Refi Rizqi; Meilano, Irwan; Gunawan, Endra; Susilo, Efendi, Joni
2017-07-01
This paper is aiming at making an improvement in GPS data time series as an input in estimating velocity field in Central Java. In this research, GPS observation data originated from Indonesia Continuously Operating Reference Station (InaCORS) 2010-2015 in Central Java are used and processed using GAMIT 10.6. Vienna Mapping Function 1 (VMF1) and daily IONEX data are used in data processing to improve GPS time series. Velocity is estimated by considering not only linear trend but also seasonal signals from time series analysis. It is found that by applying VMF1 and daily IONEX data has improved GPS time series by 0.14 mm in horizontal and 0.35 mm in vertical direction. The resulting velocity has improved 0.08 mm/yr in both horizontal direction and 0.16 mm/yr in vertical direction by canceling velocity bias due to seasonal signal. The results show that currently Central Java is moving at direction 103-121 degrees at rate 23.7-28.4 mm/yr and experiencing subsidence at rate 2-6 mm/yr, except in CBLR and CPKL stations. Central Java motion is may be influenced by post-seismic effect of 2006 M7.8 earthquake in the west part, inter-seismic effect in southeast part and Kendeng fault system in northeast part.
Traffic time series analysis by using multiscale time irreversibility and entropy.
Wang, Xuejiao; Shang, Pengjian; Fang, Jintang
2014-09-01
Traffic systems, especially urban traffic systems, are regulated by different kinds of interacting mechanisms which operate across multiple spatial and temporal scales. Traditional approaches fail to account for the multiple time scales inherent in time series, such as empirical probability distribution function and detrended fluctuation analysis, which have lead to different results. The role of multiscale analytical method in traffic time series is a frontier area of investigation. In this paper, our main purpose is to introduce a new method-multiscale time irreversibility, which is helpful to extract information from traffic time series we studied. In addition, to analyse the complexity of traffic volume time series of Beijing Ring 2, 3, 4 roads between workdays and weekends, which are from August 18, 2012 to October 26, 2012, we also compare the results by this new method and multiscale entropy method we have known well. The results show that the higher asymmetry index we get, the higher traffic congestion level will be, and accord with those which are obtained by multiscale entropy.
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
Kennedy, Curtis E; Turley, James P
2011-10-24
Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of candidate features; 9
2011-01-01
Background Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. Methods We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Results Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of
Single event time-series analysis in a karst catchment evaluated using a groundwater model
NASA Astrophysics Data System (ADS)
Mayaud, Cyril; Wagner, Thomas; Birk, Steffen
2013-04-01
The Lurbach-Tanneben karst system (Styria, Austria) is drained by two major springs and replenished by both autogenic recharge from the karst massive 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 (Oswald et al., EGU2009-9255) demonstrates that an overflow from one of the sub-catchment to the other is activated if the spring discharge exceeds a threshold. Time-series analysis (e.g., auto-correlation, cross-correlation) was applied to examine how far the various available methods support the identification of the transient inter-catchment flow observed in this karst system. As inter-catchment flow is 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 based on the current conceptual understanding of the karst system. The groundwater model 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 options of recharge (e.g., allogenic versus autogenic) were used 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 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. Thus, the heterogeneity of hydraulic aquifer
Application of nonlinear time series analysis techniques to high-frequency currency exchange data
NASA Astrophysics Data System (ADS)
Strozzi, Fernanda; Zaldívar, José-Manuel; Zbilut, Joseph P.
2002-09-01
In this work we have applied nonlinear time series analysis to high-frequency currency exchange data. The time series studied are the exchange rates between the US Dollar and 18 other foreign currencies from within and without the Euro zone. Our goal was to determine if their dynamical behaviours were in some way correlated. The nonexistence of stationarity called for the application of recurrence quantification analysis as a tool for this analysis, and is based on the definition of several parameters that allow for the quantification of recurrence plots. The method was checked using the European Monetary System currency exchanges. The results show, as expected, the high correlation between the currencies that are part of the Euro, but also a strong correlation between the Japanese Yen, the Canadian Dollar and the British Pound. Singularities of the series are also demonstrated taking into account historical events, in 1996, in the Euro zone.
Challenges to validity in single-group interrupted time series analysis.
Linden, Ariel
2017-04-01
Single-group interrupted time series analysis (ITSA) is a popular evaluation methodology in which a single unit of observation is studied; the outcome variable is serially ordered as a time series, and the intervention is expected to "interrupt" the level and/or trend of the time series, subsequent to its introduction. The most common threat to validity is history-the possibility that some other event caused the observed effect in the time series. Although history limits the ability to draw causal inferences from single ITSA models, it can be controlled for by using a comparable control group to serve as the counterfactual. Time series data from 2 natural experiments (effect of Florida's 2000 repeal of its motorcycle helmet law on motorcycle fatalities and California's 1988 Proposition 99 to reduce cigarette sales) are used to illustrate how history biases results of single-group ITSA results-as opposed to when that group's results are contrasted to those of a comparable control group. In the first example, an external event occurring at the same time as the helmet repeal appeared to be the cause of a rise in motorcycle deaths, but was only revealed when Florida was contrasted with comparable control states. Conversely, in the second example, a decreasing trend in cigarette sales prior to the intervention raised question about a treatment effect attributed to Proposition 99, but was reinforced when California was contrasted with comparable control states. Results of single-group ITSA should be considered preliminary, and interpreted with caution, until a more robust study design can be implemented. © 2016 John Wiley & Sons, Ltd.
Applying fractal analysis to heart rate time series of sheep experiencing pain.
Stubsjøen, Solveig M; Bohlin, Jon; Skjerve, Eystein; Valle, Paul S; Zanella, Adroaldo J
2010-08-04
The objective assessment of pain is difficult in animals and humans alike. Detrended fluctuation analysis (DFA) is a method which extracts "hidden" information from heart rate time series, and may offer a novel way of assessing the subjective experience associated with pain. The aim of this study was to investigate whether any fractal differences could be detected in heart rate time series of sheep due to the infliction of ischaemic pain. Heart rate variability (HRV) was recorded continuously in five ewes during treatment sequences of baseline, intervention and post-intervention for up to 60 min. Heart rate time series were subjected to a DFA, and the median of the scaling coefficients (alpha) was found to be alpha=1.10 for the baseline sequences, 1.01 for the intervention sequences and 1.00 for the post-intervention sequences. The complexity in the regulation of heartbeats decreased between baseline and intervention (p approximately 0.03) and baseline and post-intervention (p approximately 0.01), indicating reperfusion pain and nociceptive sensitization in the post-intervention sequence. Random time series based on Gaussian white noise were generated, with similar mean and variance to the HRV sequences. No difference was found between these series (p approximately 0.28), pointing to a true difference in complexity in the original data. We found no difference in the scaling coefficient alpha between the different treatments, possibly due to the small sample size or a fear induced sympathetic arousal during test day 1 confounding the results. The decrease in the scaling coefficient alpha may be due to sympathetic activation and vagal withdrawal. DFA of heart rate time series may be a useful method to evaluate the progressive shift of cardiac regulation toward sympathetic activation and vagal withdrawal produced by pain or negative emotional responses such as fear.
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.
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.
Fractal time series analysis of postural stability in elderly and control subjects
Amoud, Hassan; Abadi, Mohamed; Hewson, David J; Michel-Pellegrino, Valérie; Doussot, Michel; Duchêne, Jacques
2007-01-01
Background The study of balance using stabilogram analysis is of particular interest in the study of falls. Although simple statistical parameters derived from the stabilogram have been shown to predict risk of falls, such measures offer little insight into the underlying control mechanisms responsible for degradation in balance. In contrast, fractal and non-linear time-series analysis of stabilograms, such as estimations of the Hurst exponent (H), may provide information related to the underlying motor control strategies governing postural stability. In order to be adapted for a home-based follow-up of balance, such methods need to be robust, regardless of the experimental protocol, while producing time-series that are as short as possible. The present study compares two methods of calculating H: Detrended Fluctuation Analysis (DFA) and Stabilogram Diffusion Analysis (SDA) for elderly and control subjects, as well as evaluating the effect of recording duration. Methods Centre of pressure signals were obtained from 90 young adult subjects and 10 elderly subjects. Data were sampled at 100 Hz for 30 s, including stepping onto and off the force plate. Estimations of H were made using sliding windows of 10, 5, and 2.5 s durations, with windows slid forward in 1-s increments. Multivariate analysis of variance was used to test for the effect of time, age and estimation method on the Hurst exponent, while the intra-class correlation coefficient (ICC) was used as a measure of reliability. Results Both SDA and DFA methods were able to identify differences in postural stability between control and elderly subjects for time series as short as 5 s, with ICC values as high as 0.75 for DFA. Conclusion Both methods would be well-suited to non-invasive longitudinal assessment of balance. In addition, reliable estimations of H were obtained from time series as short as 5 s. PMID:17470303
Statistical Analysis of Time-Series from Monitoring of Active Volcanic Vents
NASA Astrophysics Data System (ADS)
Lachowycz, S.; Cosma, I.; Pyle, D. M.; Mather, T. A.; Rodgers, M.; Varley, N. R.
2016-12-01
Despite recent advances in the collection and analysis of time-series from volcano monitoring, and the resulting insights into volcanic processes, challenges remain in forecasting and interpreting activity from near real-time analysis of monitoring data. Statistical methods have potential to characterise the underlying structure and facilitate intercomparison of these time-series, and so inform interpretation of volcanic activity. We explore the utility of multiple statistical techniques that could be widely applicable to monitoring data, including Shannon entropy and detrended fluctuation analysis, by their application to various data streams from volcanic vents during periods of temporally variable activity. Each technique reveals changes through time in the structure of some of the data that were not apparent from conventional analysis. For example, we calculate the Shannon entropy (a measure of the randomness of a signal) of time-series from the recent dome-forming eruptions of Volcán de Colima (Mexico) and Soufrière Hills (Montserrat). The entropy of real-time seismic measurements and the count rate of certain volcano-seismic event types from both volcanoes is found to be temporally variable, with these data generally having higher entropy during periods of lava effusion and/or larger explosions. In some instances, the entropy shifts prior to or coincident with changes in seismic or eruptive activity, some of which were not clearly recognised by real-time monitoring. Comparison with other statistics demonstrates the sensitivity of the entropy to the data distribution, but that it is distinct from conventional statistical measures such as coefficient of variation. We conclude that each analysis technique examined could provide valuable insights for interpretation of diverse monitoring time-series.
Cluster analysis of resting-state fMRI time series.
Mezer, Aviv; Yovel, Yossi; Pasternak, Ofer; Gorfine, Tali; Assaf, Yaniv
2009-05-01
Functional MRI (fMRI) has become one of the leading methods for brain mapping in neuroscience. Recent advances in fMRI analysis were used to define the default state of brain activity, functional connectivity and basal activity. Basal activity measured with fMRI raised tremendous interest among neuroscientists since synchronized brain activity pattern could be retrieved while the subject rests (resting state fMRI). During recent years, a few signal processing schemes have been suggested to analyze the resting state blood oxygenation level dependent (BOLD) signal from simple correlations to spectral decomposition. In most of these analysis schemes, the question asked was which brain areas "behave" in the time domain similarly to a pre-specified ROI. In this work we applied short time frequency analysis and clustering to study the spatial signal characteristics of resting state fMRI time series. Such analysis revealed that clusters of similar BOLD fluctuations are found in the cortex but also in the white matter and sub-cortical gray matter regions (thalamus). We found high similarities between the BOLD clusters and the neuro-anatomical appearance of brain regions. Additional analysis of the BOLD time series revealed a strong correlation between head movements and clustering quality. Experiments performed with T1-weighted time series also provided similar quality of clustering. These observations led us to the conclusion that non-functional contributions to the BOLD time series can also account for symmetric appearance of signal fluctuations. These contributions may include head motions, the underling microvasculature anatomy and cellular morphology.
Imai, Chisato; Hashizume, Masahiro
2015-03-01
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. 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. 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. 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.
Comparison of nonparametric trend analysis according to the types of time series data
NASA Astrophysics Data System (ADS)
Heo, J.; Shin, H.; Kim, T.; Jang, H.; Kim, H.
2013-12-01
In the analysis of hydrological data, the determination of the existence of overall trend due to climate change has been a major concern and the important part of design and management of water resources for the future. The existence of trend could be identified by plotting hydrologic time series. However, statistical methods are more accurate and objective tools to perform trend analysis. Statistical methods divided into parametric and nonparametric methods. In the case of parametric method, the population should be assumed to be normally distributed. However, most of hydrological data tend to be represented by non-normal distribution, then the nonparametric method considered more suitable than parametric method. In this study, simulations were performed with different types of time series data and four nonparametric methods (Mann-Kendall test, Spearman's rho test, SEN test, and Hotelling-Pabst test) generally used in trend analysis were applied to assess the power of each trend analysis. The time series data were classified into three types which are Trend+Random, Trend+Cycle+Random, and Trend+Non-random. In order to add a change to the data, 11 kinds of different slopes were overlapped at each simulation. As the results, nonparametric methods have almost similar power for Trend+random type and Trend+Non-random series. On the other hand, Mann-Kendall and SEN tests have slightly higher power than Spearman's rho and Hotelling-Pabst tests for Trend+Cycle+Random series.
Bayesian time-series analysis of a repeated-measures poisson outcome with excess zeroes.
Murphy, Terrence E; Van Ness, Peter H; Araujo, Katy L B; Pisani, Margaret A
2011-12-01
In this article, the authors demonstrate a time-series analysis based on a hierarchical Bayesian model of a Poisson outcome with an excessive number of zeroes. The motivating example for this analysis comes from the intensive care unit (ICU) of an urban university teaching hospital (New Haven, Connecticut, 2002-2004). Studies of medication use among older patients in the ICU are complicated by statistical factors such as an excessive number of zero doses, periodicity, and within-person autocorrelation. Whereas time-series techniques adjust for autocorrelation and periodicity in outcome measurements, Bayesian analysis provides greater precision for small samples and the flexibility to conduct posterior predictive simulations. By applying elements of time-series analysis within both frequentist and Bayesian frameworks, the authors evaluate differences in shift-based dosing of medication in a medical ICU. From a small sample and with adjustment for excess zeroes, linear trend, autocorrelation, and clinical covariates, both frequentist and Bayesian models provide evidence of a significant association between a specific nursing shift and dosing level of a sedative medication. Furthermore, the posterior distributions from a Bayesian random-effects Poisson model permit posterior predictive simulations of related results that are potentially difficult to model.
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.
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
Adventures in Modern Time Series Analysis: From the Sun to the Crab Nebula and Beyond
NASA Astrophysics Data System (ADS)
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.
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.
Complex-valued time-series correlation increases sensitivity in FMRI analysis.
Kociuba, Mary C; Rowe, Daniel B
2016-07-01
To develop a linear matrix representation of correlation between complex-valued (CV) time-series in the temporal Fourier frequency domain, and demonstrate its increased sensitivity over correlation between magnitude-only (MO) time-series in functional MRI (fMRI) analysis. The standard in fMRI is to discard the phase before the statistical analysis of the data, despite evidence of task related change in the phase time-series. With a real-valued isomorphism representation of Fourier reconstruction, correlation is computed in the temporal frequency domain with CV time-series data, rather than with the standard of MO data. A MATLAB simulation compares the Fisher-z transform of MO and CV correlations for varying degrees of task related magnitude and phase amplitude change in the time-series. The increased sensitivity of the complex-valued Fourier representation of correlation is also demonstrated with experimental human data. Since the correlation description in the temporal frequency domain is represented as a summation of second order temporal frequencies, the correlation is easily divided into experimentally relevant frequency bands for each voxel's temporal frequency spectrum. The MO and CV correlations for the experimental human data are analyzed for four voxels of interest (VOIs) to show the framework with high and low contrast-to-noise ratios in the motor cortex and the supplementary motor cortex. The simulation demonstrates the increased strength of CV correlations over MO correlations for low magnitude contrast-to-noise time-series. In the experimental human data, the MO correlation maps are noisier than the CV maps, and it is more difficult to distinguish the motor cortex in the MO correlation maps after spatial processing. Including both magnitude and phase in the spatial correlation computations more accurately defines the correlated left and right motor cortices. Sensitivity in correlation analysis is important to preserve the signal of interest in f
Symbolic Time-Series Analysis for Anomaly Detection in Mechanical Systems
2006-08-01
IEEE/ASME TRANSACTIONS ON MECHATRONICS , VOL. 11, NO. 4, AUGUST 2006 439 Symbolic Time-Series Analysis for Anomaly Detection in Mechanical Systems ...fabricated as a multi- degree-of-freedom (DOF) mass-beam structure excited by oscillatory motion of two vibrators as shown in Fig. 1. Physical dimensions of...1. The dynamical system attains stationary behavior, in the fast time scale of machine vibrations , under persistent excitation in the vicinity of the
Li, Cheng; Ding, Guang-Hong; Wu, Guo-Qiang; Poon, Chi-Sang
2009-01-01
A wide variety of methods based on fractal, entropic or chaotic approaches have been applied to the analysis of complex physiological time series. In this paper, we show that fractal and entropy measures are poor indicators of nonlinearity for gait data and heart rate variability data. In contrast, the noise titration method based on Volterra autoregressive modeling represents the most reliable currently available method for testing nonlinear determinism and chaotic dynamics in the presence of measurement noise and dynamic noise.
Time series analysis and long range correlations of Nordic spot electricity market data
NASA Astrophysics Data System (ADS)
Erzgräber, Hartmut; Strozzi, Fernanda; Zaldívar, José-Manuel; Touchette, Hugo; Gutiérrez, Eugénio; Arrowsmith, David K.
2008-11-01
The electricity system price of the Nord Pool spot market is analysed. Different time scale analysis tools are assessed with focus on the Hurst exponent and long range correlations. Daily and weekly periodicities of the spot market are identified. Even though space time separation plots suggest more stationary behaviour than other financial time series, we find large fluctuations of the spot price market which suggest time-dependent scaling parameters.
The Fourier decomposition method for nonlinear and non-stationary time series analysis.
Singh, Pushpendra; Joshi, Shiv Dutt; Patney, Rakesh Kumar; Saha, Kaushik
2017-03-01
for many decades, there has been a general perception in the literature that Fourier methods are not suitable for the analysis of nonlinear and non-stationary data. In this paper, we propose a novel and adaptive Fourier decomposition method (FDM), based on the Fourier theory, and demonstrate its efficacy for the analysis of nonlinear and non-stationary time series. The proposed FDM decomposes any data into a small number of 'Fourier intrinsic band functions' (FIBFs). The FDM presents a generalized Fourier expansion with variable amplitudes and variable frequencies of a time series by the Fourier method itself. We propose an idea of zero-phase filter bank-based multivariate FDM (MFDM), for the analysis of multivariate nonlinear and non-stationary time series, using the FDM. We also present an algorithm to obtain cut-off frequencies for MFDM. The proposed MFDM generates a finite number of band-limited multivariate FIBFs (MFIBFs). The MFDM preserves some intrinsic physical properties of the multivariate data, such as scale alignment, trend and instantaneous frequency. The proposed methods provide a time-frequency-energy (TFE) distribution that reveals the intrinsic structure of a data. Numerical computations and simulations have been carried out and comparison is made with the empirical mode decomposition algorithms.
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
The Relevance of Coordinate Time Series Analysis In Permanent GPS Networks: The European Example
NASA Astrophysics Data System (ADS)
Kenyeres, A.
An evolving research tendency in Earth sciences is the use and exploitation of multi- purpose, permanently operating GNSS stations. They may serve the everyday practice (navigation, geodesy), specific research activities (e.g. geokinematics) and near-future applications (e.g. meteorology) as well. The long-term maintenance of station and product consistency is vital to guarantee the quality in all cases. This paper concen- trates on the geokinematic application of the GPS, where station velocities are derived from the long-term observation series of the permanent stations. This application re- quires the most sophisticated and careful analysis of station coordinate time series in order to derive mm-accuracy velocities. However the long term consistency of the times series is corrupted by events caused by technical interruptions (hardware config- uration changes), environmental effects (emerging noise and multipath sources) and human interactions (changes in network configuration and processing scheme). The general effects of all these phenomena are seen in the time series as jumps and outlier periods and they decrease the accuracy and reliability of the estimated velocities. The careful analysis, identification, bookkeeping and elimination of the nuisance factors should be a basic research activity in order to recover the capabilities of the observing system. Within the EUREF Permanent Network (EPN) an effort has been started in 2000, in order to perform a review-analysis of the EPN weekly combined solutions. The coordinate time series of all stations are examined, the jumps and outliers are eliminated and a cleaned multi-year combination will be computed. This retrospective work will be completed in 2002, which will be followed by a continuous tracking of the station performance. In this paper the experiences and the actual status of this work are presented.
Trend and seasonality in hospitalizations for pulmonary embolism: a time-series analysis.
Guijarro, R; Trujillo-Santos, J; Bernal-Lopez, M R; de Miguel-Díez, J; Villalobos, A; Salazar, C; Fernandez-Fernandez, R; Guijarro-Contreras, A; Gómez-Huelgas, R; Monreal, M
2015-01-01
The existence of seasonal variability in patients with acute pulmonary embolism (PE) has been debated for years, with contradictory results. The aim of this study was to identify the trend and possible existence of a seasonal pattern in hospitalizations for PE in Spain. We analyzed the hospital discharge database of the Spanish National Health System from 2001 to 2010. Patients aged > 14 years diagnosed with PE were selected and a time series was constructed considering mean daily admissions for PE by month. The trend and seasonality factor of the series were determined using time-series analysis, and time-series modeling was used for analysis. Exponential smoothing models and the autoregressive integrated moving average test were used to generate a predictive model. From 2001 to 2010, there were 162,032 diagnoses of PE (5.07 per 1000 hospitalizations). In 105,168 cases, PE was the reason for admission. The PE diagnosis rate ranged from 4.14 per 1000 in 2001 to 6.56 per 1000 in 2010; and hospital admissions due to PE ranged from 2.67 to 4.28 per 1000 hospital discharges. Time-series analysis showed a linear increase in the incidence and a significant seasonal pattern with 17% more admissions in February and 12% fewer in June-July with respect to the central tendency (difference from February to June, 29%). The incidence of hospitalizations for PE showed a linear increase and a seasonal pattern, with the highest number of admissions in winter and the lowest number in summer. © 2014 International Society on Thrombosis and Haemostasis.
Exploring Two Inflationary Regimes in Latin-American Economies:. a Binary Time Series Analysis
NASA Astrophysics Data System (ADS)
Brida, Juan Gabriel; Garrido, Nicolas
The aim of this paper is to apply the methods of Symbolic Time Series Analysis (STSA) to a series of inflation from a group of Latin-American economies. Starting with a partition of two inflation regimes, we use data symbolization for identifying temporal patterns. Afterwards the statistical information obtained from the patterns is used to estimate the parameters of a nonlinear model proposed by Brida (2000).1 We compare the performance of the model against a naive benchmark predictor to verify its power to anticipate the qualitative behavior of the inflation time series. When the use of STSA is made through pure optimization criteria, the performance of the model is poor. However, when the partition of the space of states is made according to economics intuition, the performance of the model increases considerably.
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.
Recent advances in SAR interferometry time series analysis for measuring crustal deformation
NASA Astrophysics Data System (ADS)
Hooper, Andrew; Bekaert, David; Spaans, Karsten; Arıkan, Mahmut
2012-01-01
Synthetic aperture radar (SAR) interferometry is a technique that permits remote detection of deformation at the Earth's surface, and has been used extensively to measure displacements associated with earthquakes, volcanic activity and many other crustal deformation phenomena. Analysis of a time series of SAR images extends the area where interferometry can be successfully applied, and also allows detection of smaller displacements, through the reduction of error sources. Here, we review recent advances in time series SAR interferometry methods that further improve accuracy. This is particularly important when constraining displacements due to processes with low strain rates, such as interseismic deformation. We include examples of improved algorithms applied to image deformation associated with the 2010 eruption of Eyjafjallajökull volcano in Iceland, slow slip on the Guerrero subduction zone in Mexico, and tectonic deformation in western Anatolia, Turkey.
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.
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.
Li, Shuying; Zhuang, Jun; Shen, Shifei
2017-07-01
In recent years, various types of terrorist attacks occurred, causing worldwide catastrophes. According to the Global Terrorism Database (GTD), among all attack tactics, bombing attacks happened most frequently, followed by armed assaults. In this article, a model for analyzing and forecasting the conditional probability of bombing attacks (CPBAs) based on time-series methods is developed. In addition, intervention analysis is used to analyze the sudden increase in the time-series process. The results show that the CPBA increased dramatically at the end of 2011. During that time, the CPBA increased by 16.0% in a two-month period to reach the peak value, but still stays 9.0% greater than the predicted level after the temporary effect gradually decays. By contrast, no significant fluctuation can be found in the conditional probability process of armed assault. It can be inferred that some social unrest, such as America's troop withdrawal from Afghanistan and Iraq, could have led to the increase of the CPBA in Afghanistan, Iraq, and Pakistan. The integrated time-series and intervention model is used to forecast the monthly CPBA in 2014 and through 2064. The average relative error compared with the real data in 2014 is 3.5%. The model is also applied to the total number of attacks recorded by the GTD between 2004 and 2014. © 2016 Society for Risk Analysis.
Time-series analysis of the transcriptome and proteome of Escherichia coli upon glucose repression.
Borirak, Orawan; Rolfe, Matthew D; de Koning, Leo J; Hoefsloot, Huub C J; Bekker, Martijn; Dekker, Henk L; Roseboom, Winfried; Green, Jeffrey; de Koster, Chris G; Hellingwerf, Klaas J
2015-10-01
Time-series transcript- and protein-profiles were measured upon initiation of carbon catabolite repression in Escherichia coli, in order to investigate the extent of post-transcriptional control in this prototypical response. A glucose-limited chemostat culture was used as the CCR-free reference condition. Stopping the pump and simultaneously adding a pulse of glucose, that saturated the cells for at least 1h, was used to initiate the glucose response. Samples were collected and subjected to quantitative time-series analysis of both the transcriptome (using microarray analysis) and the proteome (through a combination of 15N-metabolic labeling and mass spectrometry). Changes in the transcriptome and corresponding proteome were analyzed using statistical procedures designed specifically for time-series data. By comparison of the two sets of data, a total of 96 genes were identified that are post-transcriptionally regulated. This gene list provides candidates for future in-depth investigation of the molecular mechanisms involved in post-transcriptional regulation during carbon catabolite repression in E. coli, like the involvement of small RNAs. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.
The promise of the state space approach to time series analysis for nursing research.
Levy, Janet A; Elser, Heather E; Knobel, Robin B
2012-01-01
Nursing research, particularly related to physiological development, often depends on the collection of time series data. The state space approach to time series analysis has great potential to answer exploratory questions relevant to physiological development but has not been used extensively in nursing. The aim of the study was to introduce the state space approach to time series analysis and demonstrate potential applicability to neonatal monitoring and physiology. We present a set of univariate state space models; each one describing a process that generates a variable of interest over time. Each model is presented algebraically and a realization of the process is presented graphically from simulated data. This is followed by a discussion of how the model has been or may be used in two nursing projects on neonatal physiological development. The defining feature of the state space approach is the decomposition of the series into components that are functions of time; specifically, slowly varying level, faster varying periodic, and irregular components. State space models potentially simulate developmental processes where a phenomenon emerges and disappears before stabilizing, where the periodic component may become more regular with time, or where the developmental trajectory of a phenomenon is irregular. The ultimate contribution of this approach to nursing science will require close collaboration and cross-disciplinary education between nurses and statisticians.
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.
BSMART: A Matlab/C toolbox for analysis of multichannel neural time series
Cui, Jie; Xu, Lei; Bressler, Steven L.; Ding, Mingzhou; Liang, Hualou
2008-01-01
We have developed a Matlab/C toolbox, Brain-SMART (System for Multivariate AutoRegressive Time series, or BSMART), for spectral analysis of continuous neural time series data recorded simultaneously from multiple sensors. Available functions include time series data importing/exporting, preprocessing (normalization and trend removal), AutoRegressive (AR) modeling (multivariate/bivariate model estimation and validation), spectral quantity estimation (auto power, coherence and Granger causality spectra), network analysis (including coherence and causality networks) and visualization (including data, power, coherence and causality views). The tools for investigating causal network structures are unique functions provided by this toolbox. All functionality has been integrated into a simple and user-friendly graphical user interface (GUI) environment designed for easy accessibility. Although we have tested the toolbox only on Windows and Linux operating systems, BSMART itself is system independent. This toolbox is freely available (http://www.sahs.uth.tmc.edu/hliang/software.htm) under the GNU public license for open source development. PMID:18599267
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
Work-related accidents among the Iranian population: a time series analysis, 2000-2011.
Karimlou, Masoud; Salehi, Masoud; Imani, Mehdi; Hosseini, Agha-Fatemeh; Dehnad, Afsaneh; Vahabi, Nasim; Bakhtiyari, Mahmood
2015-01-01
Work-related accidents result in human suffering and economic losses and are considered as a major health problem worldwide, especially in the economically developing world. To introduce seasonal autoregressive moving average (ARIMA) models for time series analysis of work-related accident data for workers insured by the Iranian Social Security Organization (ISSO) between 2000 and 2011. In this retrospective study, all insured people experiencing at least one work-related accident during a 10-year period were included in the analyses. We used Box-Jenkins modeling to develop a time series model of the total number of accidents. There was an average of 1476 accidents per month (1476·05±458·77, mean±SD). The final ARIMA (p,d,q) (P,D,Q)s model for fitting to data was: ARIMA(1,1,1)×(0,1,1)12 consisting of the first ordering of the autoregressive, moving average and seasonal moving average parameters with 20·942 mean absolute percentage error (MAPE). The final model showed that time series analysis of ARIMA models was useful for forecasting the number of work-related accidents in Iran. In addition, the forecasted number of work-related accidents for 2011 explained the stability of occurrence of these accidents in recent years, indicating a need for preventive occupational health and safety policies such as safety inspection.
Steed, Chad A.; Halsey, William; Dehoff, Ryan; ...
2017-02-16
Flexible visual analysis of long, high-resolution, and irregularly sampled time series data from multiple sensor streams is a challenge in several domains. In the field of additive manufacturing, this capability is critical for realizing the full potential of large-scale 3D printers. Here, we propose a visual analytics approach that helps additive manufacturing researchers acquire a deep understanding of patterns in log and imagery data collected by 3D printers. Our specific goals include discovering patterns related to defects and system performance issues, optimizing build configurations to avoid defects, and increasing production efficiency. We introduce Falcon, a new visual analytics system thatmore » allows users to interactively explore large, time-oriented data sets from multiple linked perspectives. Falcon provides overviews, detailed views, and unique segmented time series visualizations, all with adjustable scale options. To illustrate the effectiveness of Falcon at providing thorough and efficient knowledge discovery, we present a practical case study involving experts in additive manufacturing and data from a large-scale 3D printer. The techniques described are applicable to the analysis of any quantitative time series, though the focus of this paper is on additive manufacturing.« less
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
Dynamic analysis of traffic time series at different temporal scales: A complex networks approach
NASA Astrophysics Data System (ADS)
Tang, Jinjun; Wang, Yinhai; Wang, Hua; Zhang, Shen; Liu, Fang
2014-07-01
The analysis of dynamics in traffic flow is an important step to achieve advanced traffic management and control in Intelligent Transportation System (ITS). Complexity and periodicity are definitely two fundamental properties in traffic dynamics. In this study, we first measure the complexity of traffic flow data by Lempel-Ziv algorithm at different temporal scales, and the data are collected from loop detectors on freeway. Second, to obtain more insight into the complexity and periodicity in traffic time series, we then construct complex networks from traffic time series by considering each day as a cycle and each cycle as a single node. The optimal threshold value of complex networks is estimated by the distribution of density and its derivative. In addition, the complex networks are subsequently analyzed in terms of some statistical properties, such as average path length, clustering coefficient, density, average degree and betweenness. Finally, take 2 min aggregation data as example, we use the correlation coefficient matrix, adjacent matrix and closeness to exploit the periodicity of weekdays and weekends in traffic flow data. The findings in this paper indicate that complex network is a practical tool for exploring dynamics in traffic time series.
Taylor, D
1990-04-01
Although graphic representation of time-series data is one method to describe patterns of phenomena, autocorrelations, correlograms, and plots of the autocorrelation function provide descriptive and statistical methods that reveal the structure of a deterministic cycle component within the time-series. The autocorrelation function provides a method for the investigator to test hypotheses about the nature and pattern of relationships between measured and latent variables. Patterns of phenomena can be analyzed statistically using objectively provided criteria. The autocorrelation function can also be used to understand the change in response or behavioral patterns following an experimental intervention. Traditional group comparison designs, using cross-sectional data collection strategies cannot identify differences within the individual nor can they identify patterns of behavior or structural patterns within the data. Autocorrelation and cross-correlation become threats to statistical validity when conventional methods are used; however, in time-series analysis, autocorrelation allows close scrutiny of the pattern of response within an individual across a time-dimension.
Sample entropy applied to the analysis of synthetic time series and tachograms
NASA Astrophysics Data System (ADS)
Muñoz-Diosdado, A.; Gálvez-Coyt, G. G.; Solís-Montufar, E.
2017-01-01
Entropy is a method of non-linear analysis that allows an estimate of the irregularity of a system, however, there are different types of computational entropy that were considered and tested in order to obtain one that would give an index of signals complexity taking into account the data number of the analysed time series, the computational resources demanded by the method, and the accuracy of the calculation. An algorithm for the generation of fractal time-series with a certain value of β was used for the characterization of the different entropy algorithms. We obtained a significant variation for most of the algorithms in terms of the series size, which could result counterproductive for the study of real signals of different lengths. The chosen method was sample entropy, which shows great independence of the series size. With this method, time series of heart interbeat intervals or tachograms of healthy subjects and patients with congestive heart failure were analysed. The calculation of sample entropy was carried out for 24-hour tachograms and time subseries of 6-hours for sleepiness and wakefulness. The comparison between the two populations shows a significant difference that is accentuated when the patient is sleeping.
Complexity analysis of stride interval time series by threshold dependent symbolic entropy.
Aziz, Wajid; Arif, Muhammad
2006-09-01
The stride interval of human gait fluctuates in complex fashion. It reflects the rhythm of the locomotor system. The temporal fluctuations in the stride interval provide us a non-invasive technique to evaluate the effects of neurological impairments on gait and its changes with age and disease. In this paper, we have used threshold dependent symbolic entropy, which is based on symbolic nonlinear time series analysis to study complexity of gait of control and neurodegenerative disease subjects. Symbolic entropy characterizes quantitatively the complexity even in time series having relatively few data points. We have calculated normalized corrected Shannon entropy (NCSE) of symbolic sequences extracted from stride interval time series. This measure of complexity showed significant difference between control and neurodegenerative disease subjects for a certain range of thresholds. We have also investigated complexity of physiological signal and randomized noisy data. In the study, we have found that the complexity of physiological signal was higher than that of random signals at short threshold values.
Forecasting malaria cases using climatic factors in delhi, India: a time series analysis.
Kumar, Varun; Mangal, Abha; Panesar, Sanjeet; Yadav, Geeta; Talwar, Richa; Raut, Deepak; Singh, Saudan
2014-01-01
Background. Malaria still remains a public health problem in developing countries and changing environmental and climatic factors pose the biggest challenge in fighting against the scourge of malaria. Therefore, the study was designed to forecast malaria cases using climatic factors as predictors in Delhi, India. Methods. The total number of monthly cases of malaria slide positives occurring from January 2006 to December 2013 was taken from the register maintained at the malaria clinic at Rural Health Training Centre (RHTC), Najafgarh, Delhi. Climatic data of monthly mean rainfall, relative humidity, and mean maximum temperature were taken from Regional Meteorological Centre, Delhi. Expert modeler of SPSS ver. 21 was used for analyzing the time series data. Results. Autoregressive integrated moving average, ARIMA (0,1,1) (0,1,0)(12), was the best fit model and it could explain 72.5% variability in the time series data. Rainfall (P value = 0.004) and relative humidity (P value = 0.001) were found to be significant predictors for malaria transmission in the study area. Seasonal adjusted factor (SAF) for malaria cases shows peak during the months of August and September. Conclusion. ARIMA models of time series analysis is a simple and reliable tool for producing reliable forecasts for malaria in Delhi, India.
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
Hazledine, Saul; Sun, Jongho; Wysham, Derin; Downie, J Allan; Oldroyd, Giles E D; Morris, Richard J
2009-08-13
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.
Capturing Context-Related Change in Emotional Dynamics via Fixed Moderated Time Series Analysis.
Adolf, Janne K; Voelkle, Manuel C; Brose, Annette; Schmiedek, Florian
2017-01-01
Much of recent affect research relies on intensive longitudinal studies to assess daily emotional experiences. The resulting data are analyzed with dynamic models to capture regulatory processes involved in emotional functioning. Daily contexts, however, are commonly ignored. This may not only result in biased parameter estimates and wrong conclusions, but also ignores the opportunity to investigate contextual effects on emotional dynamics. With fixed moderated time series analysis, we present an approach that resolves this problem by estimating context-dependent change in dynamic parameters in single-subject time series models. The approach examines parameter changes of known shape and thus addresses the problem of observed intra-individual heterogeneity (e.g., changes in emotional dynamics due to observed changes in daily stress). In comparison to existing approaches to unobserved heterogeneity, model estimation is facilitated and different forms of change can readily be accommodated. We demonstrate the approach's viability given relatively short time series by means of a simulation study. In addition, we present an empirical application, targeting the joint dynamics of affect and stress and how these co-vary with daily events. We discuss potentials and limitations of the approach and close with an outlook on the broader implications for understanding emotional adaption and development.
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.
Multifractal analysis of visibility graph-based Ito-related connectivity time series.
Czechowski, Zbigniew; Lovallo, Michele; Telesca, Luciano
2016-02-01
In this study, we investigate multifractal properties of connectivity time series resulting from the visibility graph applied to normally distributed time series generated by the Ito equations with multiplicative power-law noise. We show that multifractality of the connectivity time series (i.e., the series of numbers of links outgoing any node) increases with the exponent of the power-law noise. The multifractality of the connectivity time series could be due to the width of connectivity degree distribution that can be related to the exit time of the associated Ito time series. Furthermore, the connectivity time series are characterized by persistence, although the original Ito time series are random; this is due to the procedure of visibility graph that, connecting the values of the time series, generates persistence but destroys most of the nonlinear correlations. Moreover, the visibility graph is sensitive for detecting wide "depressions" in input time series.
Applications and development of new algorithms for displacement analysis using InSAR time series
NASA Astrophysics Data System (ADS)
Osmanoglu, Batuhan
Time series analysis of Synthetic Aperture Radar Interferometry (InSAR) data has become an important scientific tool for monitoring and measuring the displacement of Earth's surface due to a wide range of phenomena, including earthquakes, volcanoes, landslides, changes in ground water levels, and wetlands. Time series analysis is a product of interferometric phase measurements, which become ambiguous when the observed motion is larger than half of the radar wavelength. Thus, phase observations must first be unwrapped in order to obtain physically meaningful results. Persistent Scatterer Interferometry (PSI), Stanford Method for Persistent Scatterers (StaMPS), Short Baselines Interferometry (SBAS) and Small Temporal Baseline Subset (STBAS) algorithms solve for this ambiguity using a series of spatio-temporal unwrapping algorithms and filters. In this dissertation, I improve upon current phase unwrapping algorithms, and apply the PSI method to study subsidence in Mexico City. PSI was used to obtain unwrapped deformation rates in Mexico City (Chapter 3),where ground water withdrawal in excess of natural recharge causes subsurface, clay-rich sediments to compact. This study is based on 23 satellite SAR scenes acquired between January 2004 and July 2006. Time series analysis of the data reveals a maximum line-of-sight subsidence rate of 300mm/yr at a high enough resolution that individual subsidence rates for large buildings can be determined. Differential motion and related structural damage along an elevated metro rail was evident from the results. Comparison of PSI subsidence rates with data from permanent GPS stations indicate root mean square (RMS) agreement of 6.9 mm/yr, about the level expected based on joint data uncertainty. The Mexico City results suggest negligible recharge, implying continuing degradation and loss of the aquifer in the third largest metropolitan area in the world. Chapters 4 and 5 illustrate the link between time series analysis and three
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
Highway Subsidence Analysis Based on the Advanced InSAR Time Series Analysis Method
NASA Astrophysics Data System (ADS)
Zhang, Qingyun; Zhang, Jingfa; Liu, Guolin; Li, Yongsheng
2016-08-01
The synthetic aperture radar (InSAR) measurements have the advantages of all-weather, wide range, high precision on the surface deformation monitoring. Highway as an important index of modern social and economic development, the quality and deformation changes in the process of using have a significant impact in the social development and people's life and property security. In practical applications the InSAR technology should do a variety of error correction analysis. By using a new analysis method – FRAM- SBAS time-series analysis method, to analyze the settlement of highway on Yanzhou area by the ALOS PALSAR datas. Use FRAM- SBAS timing analysis method to obtain the surface timing changes during 2008-09-21 to 2010-07-18 in the Jining area and obtained good results, the Jining area maximum timing settlement is 60mm, the maximum settlement rate reached 30mm/yr. The maximum settlement of the highway section is 53mm, the maximum settlement rate is 32mm/yr. And the settlement of highway worst sections were in severe ground subsidence, thus proving the mining and vehicle load effect on settlement of highway. And it is proved that the timing method on the ground and highway subsidence monitoring is feasible.
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.
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.
Methods for serial analysis of long time series in the study of biological rhythms
2013-01-01
When one is faced with the analysis of long time series, one often finds that the characteristics of circadian rhythms vary with time throughout the series. To cope with this situation, the whole series can be fragmented into successive sections which are analyzed one after the other, which constitutes a serial analysis. This article discusses serial analysis techniques, beginning with the characteristics that the sections must have and how they can affect the results. After consideration of the effects of some simple filters, different types of serial analysis are discussed systematically according to the variable analyzed or the estimated parameters: scalar magnitudes, angular magnitudes (time or phase), magnitudes related to frequencies (or periods), periodograms, and derived and / or special magnitudes and variables. The use of wavelet analysis and convolutions in long time series is also discussed. In all cases the fundamentals of each method are exposed, jointly with practical considerations and graphic examples. The final section provides information about software available to perform this type of analysis. PMID:23867052
Multivariate change point analysis in time series for volcano unrest detection
NASA Astrophysics Data System (ADS)
Aliotta, M. A.; Cassisi, C.; Fiumara, S.; Montalto, P.
2016-12-01
The detection of unrest in volcanic areas represents a key task for civil protection purposes. Nowadays, large networks for different kinds of measurements deployed in most of active volcanoes supply huge amount of data, mainly in the form of time series. Automatic techniques are needed to perform the analysis of such amount of data. In this sense, time series analysis techniques can contribute to exploit the information coming from the measurements to identify possible changes into volcanic behaviour. In particular, the change point analysis can be used to this aim. The change point analysis is the process of detecting distributional changes within time-ordered observations. Among the different techniques proposed for this kind of analysis, we chose to use the SeqDrift (Sakthithasan et al., 2013) technique for its ability to deal with real time data. The algorithm iteratively compares two consecutive sliding windows coming from the data stream to choose whether the boundary point (in the between of the two windows) is a change point. The check is carried out by a non-parametric statistical test. We applied the proposed approach to a test case on Mt. Etna using large multivariate dataset from 2011-2015. The results indicate that the technique is effective to detect volcanic state changes. Sakthithasan, S., Pears, R., Koh, Y. S. (2013). One Pass Concept Change Detection for Data Streams. PAKDD (2): 461-472.
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
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
Interpretation of engine cycle-to-cycle variation by chaotic time series analysis
Daw, C.S.; Kahl, W.K.
1990-01-01
In this paper we summarize preliminary results from applying a new mathematical technique -- chaotic time series analysis (CTSA) -- to cylinder pressure data from a spark-ignition (SI) four-stroke engine fueled with both methanol and iso-octane. Our objective is to look for the presence of deterministic chaos'' dynamics in peak pressure variations and to investigate the potential usefulness of CTSA as a diagnostic tool. Our results suggest that sequential peak cylinder pressures exhibit some characteristic features of deterministic chaos and that CTSA can extract previously unrecognized information from such data. 18 refs., 11 figs., 2 tabs.
Analysis of MALDI FT-ICR Mass Spectrometry Data: a Time Series Approach
Kronewitter, Scott R.; Lebrilla, Carlito B.; Rocke, David M.
2009-01-01
Matrix-assisted laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry is a technique for high mass-resolution analysis of substances that is rapidly gaining popularity as an analytic tool. Extracting signal from the background noise, however, poses significant challenges. In this article, we model the noise part of a spectrum as an autoregressive, moving average (ARMA) time series with innovations given by a generalized gamma distribution with varying scale parameter but constant shape parameter and exponent. This enables us to classify peaks found in actual spectra as either noise or signal using a reasonable criterion that outperforms a standard threshold criterion. PMID:19646586
Analysis of MALDI FT-ICR mass spectrometry data: a time series approach.
Barkauskas, Donald A; Kronewitter, Scott R; Lebrilla, Carlito B; Rocke, David M
2009-08-26
Matrix-assisted laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry is a technique for high mass-resolution analysis of substances that is rapidly gaining popularity as an analytic tool. Extracting signal from the background noise, however, poses significant challenges. In this article, we model the noise part of a spectrum as an autoregressive, moving average (ARMA) time series with innovations given by a generalized gamma distribution with varying scale parameter but constant shape parameter and exponent. This enables us to classify peaks found in actual spectra as either noise or signal using a reasonable criterion that outperforms a standard threshold criterion.
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.
Measurement error in time-series analysis: a simulation study comparing modelled and monitored data.
Butland, Barbara K; Armstrong, Ben; Atkinson, Richard W; Wilkinson, Paul; Heal, Mathew R; Doherty, Ruth M; Vieno, Massimo
2013-11-13
Assessing health effects from background exposure to air pollution is often hampered by the sparseness of pollution monitoring networks. However, regional atmospheric chemistry-transport models (CTMs) can provide pollution data with national coverage at fine geographical and temporal resolution. We used statistical simulation to compare the impact on epidemiological time-series analysis of additive measurement error in sparse monitor data as opposed to geographically and temporally complete model data. Statistical simulations were based on a theoretical area of 4 regions each consisting of twenty-five 5 km × 5 km grid-squares. In the context of a 3-year Poisson regression time-series analysis of the association between mortality and a single pollutant, we compared the error impact of using daily grid-specific model data as opposed to daily regional average monitor data. We investigated how this comparison was affected if we changed the number of grids per region containing a monitor. To inform simulations, estimates (e.g. of pollutant means) were obtained from observed monitor data for 2003-2006 for national network sites across the UK and corresponding model data that were generated by the EMEP-WRF CTM. Average within-site correlations between observed monitor and model data were 0.73 and 0.76 for rural and urban daily maximum 8-hour ozone respectively, and 0.67 and 0.61 for rural and urban loge(daily 1-hour maximum NO2). When regional averages were based on 5 or 10 monitors per region, health effect estimates exhibited little bias. However, with only 1 monitor per region, the regression coefficient in our time-series analysis was attenuated by an estimated 6% for urban background ozone, 13% for rural ozone, 29% for urban background loge(NO2) and 38% for rural loge(NO2). For grid-specific model data the corresponding figures were 19%, 22%, 54% and 44% respectively, i.e. similar for rural loge(NO2) but more marked for urban loge(NO2). Even if correlations between
Analysis of epileptic seizure count time series by ensemble state space modelling.
Galka, Andreas; Boor, Rainer; Doege, Corinna; von Spiczak, Sarah; Stephani, Ulrich; Siniatchkin, Michael
2015-08-01
We propose an approach for the analysis of epileptic seizure count time series within a state space framework. Time-dependent dosages of several simultaneously administered anticonvulsants are included as external inputs. The method aims at distinguishing which temporal correlations in the data are due to the medications, and which correspond to an unrelated background signal. Through this method it becomes possible to disentagle the effects of the individual anticonvulsants, i.e., to decide which anticonvulsant in a particular patient decreases or rather increases the number of seizures.
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.
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 JEPX Spot Price with the Box-Jenkins Method
NASA Astrophysics Data System (ADS)
Nishikawa, Hiroshi
Following the examples of other countries, in April 2005 Japan launched wholesale electric power exchange operations as a primary item of system reform in line with electric liberalization. Only two years have passed since the initiation of these operations. However, in the summer of 2005, the surge in market prices was evident, which suggested that certain measures should be taken to confront potential market risks. Establishing a useful system for forecasting market prices through the modeling of price fluctuations in the wholesale electric market became essential. Currently, various price models are being proposed. Taking both the limited amount of data and the model's purpose into consideration, this study adopted the univariate time series model. We conducted a time series analysis on the open price indexes in the JEPX spot market with the Box-Jenkins method. Since a seven-day cycle can be observed in the data, we adopted the seasonal ARIMA model. In accordance with the procedures of the Box-Jenkins method, we determined the degree of the model's polynomial using the autocorrelation and partial autocorrelation of the data and estimated the parameters of the model with the maximum likelihood method. We conducted a forecast on next day JEPX spot market prices with this time series model and examined its validity and utility as a forecasting tool. Price forecasts made with this model require only a small amount of data and will save substantial analysis work. Consequently, this method is expected to be widely used by market participants as the reference data for their bid pricing.
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.
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
Catalán-Arlandis, J L
2011-01-01
To describe the monthly costs of drugs per standardized patient in the Doctor Peset health department in Valencia, Spain and to correlate them with the computerization of prescription in primary care clinics using time series analysis. Other objectives were to identify internal causes that could explain the variations observed, and to evaluate the drug costs associated with these variations. Observational and retrospective study to analyse the variables: a) monthly costs of drugs by standardized patient and b) number of defined daily doses (DDD) per 1000 inhabitants and per day of drugs used to prevent cardiovascular risk prescribed by the Primary Care doctors of the Doctor Peset health department in Valencia, between January 2001 and August 2009. Time series were adjusted using ARIMA models. The impact of computerization was assessed using an intervention analysis on time series. ARIMA models for the monthly costs of drugs by standardized patient shows that this indicator was increased by 4.9% in August 2007. The modelling of daily DDD per 1000 inhabitants showed an increase of 8.5% on the same date. Both increases occurred together with the introduction of the computerized prescription in Primary Care clinics. The pharmacoeconomic study estimated that drug costs associated with the trend changes increased by 11,365,409 euros within the period September 2007 to August 2009. The computerization of prescription in Primary Care clinics can be correlated with an increase in the monthly costs of drugs per standardized patient and with the number of daily DDD per 1000 inhabitants of drugs to reduce the cardiovascular risk by 4.5% and 8.5%, respectively. Copyright © 2009 SECA. Published by Elsevier Espana. All rights reserved.
Error Analysis of the IGS repro2 Station Position Time Series
NASA Astrophysics Data System (ADS)
Rebischung, P.; Ray, J.; Benoist, C.; Metivier, L.; Altamimi, Z.
2015-12-01
Eight Analysis Centers (ACs) of the International GNSS Service (IGS) have completed a second reanalysis campaign (repro2) of the GNSS data collected by the IGS global tracking network back to 1994, using the latest available models and methodology. The AC repro2 contributions include in particular daily terrestrial frame solutions, the first time with sub-weekly resolution for the full IGS history. The AC solutions, comprising positions for 1848 stations with daily polar motion coordinates, were combined to form the IGS contribution to the next release of the International Terrestrial Reference Frame (ITRF2014). Inter-AC position consistency is excellent, about 1.5 mm horizontal and 4 mm vertical. The resulting daily combined frames were then stacked into a long-term cumulative frame assuming generally linear motions, which constitutes the GNSS input to the ITRF2014 inter-technique combination. A special challenge involved identifying the many position discontinuities, averaging about 1.8 per station. A stacked periodogram of the station position residual time series from this long-term solution reveals a number of unexpected spectral lines (harmonics of the GPS draconitic year, fortnightly tidal lines) on top of a white+flicker background noise and strong seasonal variations. In this study, we will present results from station- and AC-specific analyses of the noise and periodic errors present in the IGS repro2 station position time series. So as to better understand their sources, and in view of developing a spatio-temporal error model, we will focus in particular on the spatial distribution of the noise characteristics and of the periodic errors. By computing AC-specific long-term frames and analyzing the respective residual time series, we will additionally study how the characteristics of the noise and of the periodic errors depend on the adopted analysis strategy and reduction software.
Time series analysis of onchocerciasis data from Mexico: a trend towards elimination.
Lara-Ramírez, Edgar E; Rodríguez-Pérez, Mario A; Pérez-Rodríguez, Miguel A; Adeleke, Monsuru A; Orozco-Algarra, María E; Arrendondo-Jiménez, Juan I; Guo, Xianwu
2013-01-01
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. 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. 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.
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
NASA Astrophysics Data System (ADS)
Assireu, A. T.; Rosa, R. R.; Vijaykumar, N. L.; Lorenzzetti, J. A.; Rempel, E. L.; Ramos, F. M.; Abreu Sá, L. D.; Bolzan, M. J. A.; Zanandrea, A.
2002-08-01
Based on the gradient pattern analysis (GPA) technique we introduce a new methodology for analyzing short nonstationary time series. Using the asymmetric amplitude fragmentation (AAF) operator from GPA we analyze Lagrangian data observed as velocity time series for ocean flow. The results show that quasi-periodic, chaotic and turbulent regimes can be well characterized by means of this new geometrical approach.
NASA Astrophysics Data System (ADS)
Langbein, J. O.
2016-12-01
Most time series of geophysical phenomena are contaminated with temporally correlated errors that limit the precision of any derived parameters. Ignoring temporal correlations will result in biased and unrealistic estimates of velocity and its error estimated from geodetic position measurements. Obtaining better estimates of uncertainties is limited by several factors, including selection of the correct model for the background noise and the computational requirements to estimate the parameters of the selected noise model when there are numerous observations. Here, I address the second problem of computational efficiency using maximum likelihood estimates (MLE). Most geophysical time series have background noise processes that can be represented as a combination of white and power-law noise, 1/fn , with frequency, f. Time domain techniques involving construction and inversion of large data covariance matrices are employed. Bos et al. [2012] demonstrate one technique that substantially increases the efficiency of the MLE methods, but it provides only an approximate solution for power-law indices greater than 1.0. That restriction can be removed by simply forming a data-filter that adds noise processes rather than combining them in quadrature. Consequently, the inversion of the data covariance matrix is simplified and it provides robust results for a wide range of power-law indices. With the new formulation, the efficiency is typically improved by about a factor of 8 over previous MLE algorithms [Langbein, 2004]. The new algorithm can be downloaded at http://earthquake.usgs.gov/research/software/#est_noise. The main program provides a number of basic functions that can be used to model the time-dependent part of time series and a variety of models that describe the temporal covariance of the data. In addition, the program is packaged with a few companion programs and scripts that can help with data analysis and with interpretation of the noise modeling.
Analysis of Solar Influence on Tropospheric Weather Using a New Time Series of Weather Types
NASA Astrophysics Data System (ADS)
Schwander, Mikhaël; Brönnimann, Stefan
2016-04-01
A new daily weather types time series is used to analyse the influence of solar activity on European weather patterns. This new weather type classification is a reconstruction of an existing classification (CAP). MeteoSwiss have computed daily weather types for the Alpine Region from 1957 onward using ERA-40 and ERA-Interim reanalyses dataset with the CAP method (cluster analysis of principal components). Our new method uses early instrumental data from European weather stations to reconstruct the CAP9 classification. The new classification contains 7 types and covers the period 1763-2009. This new time series is used to study the impact of the 11-year cycle on European tropospheric weather. For this, changes in the frequency of occurrence of the weather types are analysed. The sunspot number time series allows us to analyse changes in weather types over almost 250 years. We divide the solar activity in 3 classes (low, moderate, high) for January, February and March using subjective thresholds (33rd and 66th percentiles). The days in the 3 classes are then classified according to the new weather types. The first results show a tendency to have more days with an easterly or northerly flow over Europe under low solar activity. On the other hand, there is a higher occurrence of westerly types under high solar activity. This differences are more pronounced during the 1958-2009 period. The within types differences are also investigated with composites computed with ERA-40/-Interim from 1958 to 2009. The mean sea level pressure tends to be lower over the North Atlantic under high solar activity. This study shows a change in the frequency of occurrence of weather types as well as change in the mean sea level pressure. The reasons of these changes will be further investigated.
NASA Astrophysics Data System (ADS)
Scargle, J.
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. Examples of application of these tools for automated time series discovery will be given.
Spatial change analysis of paddy cropping pattern using MODIS time series imagery in Central Java
NASA Astrophysics Data System (ADS)
Arif Fatoni, Muhammad; Dwi Nugroho, Kreshna; Fatikhunnada, Alvin; Liyantono; Setiawan, Yudi
2017-01-01
Central Java had the diverse paddy field cropping patterns and it was influenced by several factors such as water availability, land condition, paddy fields ownership, and local culture. This research was aimed to analyze dynamic changes of paddy cropping pattern using MODIS imagery (MOD13Q1 16-day composite from 2001 to 2015). This research used k-means clustering algorithm for classified cropping pattern in Central Java based on similarity pattern of annual data from vegetation index. The result of this research classified cropping pattern become a main class and produced 15 maps of distribution cropping patterns (from 2001 to 2015). The result also divided Central Java’s paddy fields become 2 section (constant and change) based on cropping pattern that majority was caused by water availability. This research got the better accuracy (77.67%) of cropping pattern than long time series analysis from previous research. Although some classes successfully obtained upon annual time series analysis, MODIS still difficult to detect mixed crop pattern.
Li, Shi; Mukherjee, Bhramar; Batterman, Stuart; Ghosh, Malay
2013-12-01
Case-crossover designs are widely used to study short-term exposure effects on the risk of acute adverse health events. While the frequentist literature on this topic is vast, there is no Bayesian work in this general area. The contribution of this paper is twofold. First, the paper establishes Bayesian equivalence results that require characterization of the set of priors under which the posterior distributions of the risk ratio parameters based on a case-crossover and time-series analysis are identical. Second, the paper studies inferential issues under case-crossover designs in a Bayesian framework. Traditionally, a conditional logistic regression is used for inference on risk-ratio parameters in case-crossover studies. We consider instead a more general full likelihood-based approach which makes less restrictive assumptions on the risk functions. Formulation of a full likelihood leads to growth in the number of parameters proportional to the sample size. We propose a semi-parametric Bayesian approach using a Dirichlet process prior to handle the random nuisance parameters that appear in a full likelihood formulation. We carry out a simulation study to compare the Bayesian methods based on full and conditional likelihood with the standard frequentist approaches for case-crossover and time-series analysis. The proposed methods are illustrated through the Detroit Asthma Morbidity, Air Quality and Traffic study, which examines the association between acute asthma risk and ambient air pollutant concentrations.
Visibility graph analysis of heart rate time series and bio-marker of congestive heart failure
NASA Astrophysics Data System (ADS)
Bhaduri, Anirban; Bhaduri, Susmita; Ghosh, Dipak
2017-09-01
Study of RR interval time series for Congestive Heart Failure had been an area of study with different methods including non-linear methods. In this article the cardiac dynamics of heart beat are explored in the light of complex network analysis, viz. visibility graph method. Heart beat (RR Interval) time series data taken from Physionet database [46, 47] belonging to two groups of subjects, diseased (congestive heart failure) (29 in number) and normal (54 in number) are analyzed with the technique. The overall results show that a quantitative parameter can significantly differentiate between the diseased subjects and the normal subjects as well as different stages of the disease. Further, the data when split into periods of around 1 hour each and analyzed separately, also shows the same consistent differences. This quantitative parameter obtained using the visibility graph analysis thereby can be used as a potential bio-marker as well as a subsequent alarm generation mechanism for predicting the onset of Congestive Heart Failure.
Disentangling Time-series Spectra with Gaussian Processes: Applications to Radial Velocity Analysis
NASA Astrophysics Data System (ADS)
Czekala, Ian; Mandel, Kaisey S.; Andrews, Sean M.; Dittmann, Jason A.; Ghosh, Sujit K.; Montet, Benjamin T.; Newton, Elisabeth R.
2017-05-01
Measurements of radial velocity variations from the spectroscopic monitoring of stars and their companions are essential for a broad swath of astrophysics; these measurements provide access to the fundamental physical properties that dictate all phases of stellar evolution and facilitate the quantitative study of planetary systems. The conversion of those measurements into both constraints on the orbital architecture and individual component spectra can be a serious challenge, however, especially for extreme flux ratio systems and observations with relatively low sensitivity. Gaussian processes define sampling distributions of flexible, continuous functions that are well-motivated for modeling stellar spectra, enabling proficient searches for companion lines in time-series spectra. We introduce a new technique for spectral disentangling, where the posterior distributions of the orbital parameters and intrinsic, rest-frame stellar spectra are explored simultaneously without needing to invoke cross-correlation templates. To demonstrate its potential, this technique is deployed on red-optical time-series spectra of the mid-M-dwarf binary LP661-13. We report orbital parameters with improved precision compared to traditional radial velocity analysis and successfully reconstruct the primary and secondary spectra. We discuss potential applications for other stellar and exoplanet radial velocity techniques and extensions to time-variable spectra. The code used in this analysis is freely available as an open-source Python package.
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.
NASA Astrophysics Data System (ADS)
Zunino, Luciano; Olivares, Felipe; Scholkmann, Felix; Rosso, Osvaldo A.
2017-06-01
A symbolic encoding scheme, based on the ordinal relation between the amplitude of neighboring values of a given data sequence, should be implemented before estimating the permutation entropy. Consequently, equalities in the analyzed signal, i.e. repeated equal values, deserve special attention and treatment. In this work, we carefully study the effect that the presence of equalities has on permutation entropy estimated values when these ties are symbolized, as it is commonly done, according to their order of appearance. On the one hand, the analysis of computer-generated time series is initially developed to understand the incidence of repeated values on permutation entropy estimations in controlled scenarios. The presence of temporal correlations is erroneously concluded when true pseudorandom time series with low amplitude resolutions are considered. On the other hand, the analysis of real-world data is included to illustrate how the presence of a significant number of equal values can give rise to false conclusions regarding the underlying temporal structures in practical contexts.
Modern trends in Class III orthognathic treatment: A time series analysis.
Lee, Chang-Hoon; Park, Hyun-Hee; Seo, Byoung-Moo; Lee, Shin-Jae
2017-03-01
To examine the current trends in surgical-orthodontic treatment for patients with Class III malocclusion using time-series analysis. The records of 2994 consecutive patients who underwent orthognathic surgery from January 1, 2004, through December 31, 2015, at Seoul National University Dental Hospital, Seoul, Korea, were reviewed. Clinical data from each surgical and orthodontic treatment record included patient's sex, age at the time of surgery, malocclusion classification, type of orthognathic surgical procedure, place where the orthodontic treatment was performed, orthodontic treatment modality, and time elapsed for pre- and postoperative orthodontic treatment. Out of the orthognathic surgery patients, 86% had Class III malocclusion. Among them, two-jaw surgeries have become by far the most common orthognathic surgical treatment these days. The age at the time of surgery and the number of new patients had seasonal variations, which demonstrated opposing patterns. There was neither positive nor negative correlation between pre- and postoperative orthodontic treatment time. Elapsed orthodontic treatment time for both before and after Class III orthognathic surgeries has been decreasing over the years. Results of the time series analysis might provide clinicians with some insights into current surgical and orthodontic management.
Modern trends in Class III orthognathic treatment: A time series analysis.
Lee, Chang-Hoon; Park, Hyun-Hee; Seo, Byoung-Moo; Lee, Shin-Jae
2016-07-19
To examine the current trends in surgical-orthodontic treatment for patients with Class III malocclusion using time-series analysis. The records of 2994 consecutive patients who underwent orthognathic surgery from January 1, 2004, through December 31, 2015, at Seoul National University Dental Hospital, Seoul, Korea, were reviewed. Clinical data from each surgical and orthodontic treatment record included patient's sex, age at the time of surgery, malocclusion classification, type of orthognathic surgical procedure, place where the orthodontic treatment was performed, orthodontic treatment modality, and time elapsed for pre- and postoperative orthodontic treatment. Out of the orthognathic surgery patients, 86% had Class III malocclusion. Among them, two-jaw surgeries have become by far the most common orthognathic surgical treatment these days. The age at the time of surgery and the number of new patients had seasonal variations, which demonstrated opposing patterns. There was neither positive nor negative correlation between pre- and postoperative orthodontic treatment time. Elapsed orthodontic treatment time for both before and after Class III orthognathic surgeries has been decreasing over the years. Results of the time series analysis might provide clinicians with some insights into current surgical and orthodontic management.
Fractal analysis of GPS time series for early detection of disastrous seismic events
NASA Astrophysics Data System (ADS)
Filatov, Denis M.; Lyubushin, Alexey A.
2017-03-01
A new method of fractal analysis of time series for estimating the chaoticity of behaviour of open stochastic dynamical systems is developed. The method is a modification of the conventional detrended fluctuation analysis (DFA) technique. We start from analysing both methods from the physical point of view and demonstrate the difference between them which results in a higher accuracy of the new method compared to the conventional DFA. Then, applying the developed method to estimate the measure of chaoticity of a real dynamical system - the Earth's crust, we reveal that the latter exhibits two distinct mechanisms of transition to a critical state: while the first mechanism has already been known due to numerous studies of other dynamical systems, the second one is new and has not previously been described. Using GPS time series, we demonstrate efficiency of the developed method in identification of critical states of the Earth's crust. Finally we employ the method to solve a practically important task: we show how the developed measure of chaoticity can be used for early detection of disastrous seismic events and provide a detailed discussion of the numerical results, which are shown to be consistent with outcomes of other researches on the topic.
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
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.
NASA Astrophysics Data System (ADS)
Finnegan, N. J.; Pritchard, M. E.; Lohman, R.; Lundgren, P. R.
2007-12-01
Satellite radar interferometry (InSAR) time series analysis (e.g., Lundgren et al., 2001) can reveal rich patterns of deformation in both time and space. As the technique is sensitive to mm-scale vertical deformation over large and spatially extensive regions, it provides a useful geodetic tool where satellite coverage and radar phase coherence permit. Here we apply InSAR time series techniques based on the Small BAseline Subset Algorithm (SBAS) (Berardino et al., 2002) using data from three satellites (ERS 1, ERS2, and RADARSAT) to the urban corridor between Tacoma, Seattle and Everett, WA, over the time period 1992 - 2007. The target of our work is to better characterize the nature of active faulting and deep-seated landsliding within the densely populated study area. Additionally, we seek to independently quantify how localized short-wavelength deformation is contaminating data collected from the ~ 12 GPS stations in the eastern Puget Sound region. Comparisons of InSAR time series inversions to data from 4 GPS stations temporally and spatially overlapping the available InSAR observations reveal that surface displacement computed from InSAR matches the GPS deformation within the range of error reported for vertical GPS data (~ 4mm). Contemporaneous surface velocity maps generated via linear regression to two independent time series inversions from overlapping ERS satellite tracks 428 and 156 show striking agreement in the pattern of surface velocity, and effectively resolve rates as low as 1 mm/yr. Based on the results of our velocity mapping, we provide new constraints on surface deformation in the Seattle metro region. First, between 1992 and 2007 we document subsidence (~ 1-3 mm/yr) over much of the region characterized by Holocene infilling of the Puget Sound by lahar and floodplain sedimentation. This deformation is consistent with subsidence due to sediment compaction and de-watering. Second, between 1992 and 2007 we document no slow landslide deformation
Probing Prominence Formation with Time Series Analysis of Models and AIA Data
NASA Astrophysics Data System (ADS)
Kucera, T. A.; Viall, N. M.; Karpen, J. T.
2016-12-01
We present a observational and modeling study of the formation and dynamics of prominence plasma, using a time series analysis of data from the Solar Dynamic Observatory's Atmospheric Imaging Assembly (SDO/AIA). The analysis consists of a diagnosis of heating and cooling events by comparing the time profiles of emission formed at different temperatures and observed by different AIA bands. We apply this analysis both to prominences observed by AIA and to model runs from the thermal non-equilibrium model in which heating at the foot-points of sheared coronal flux-tubes results in evaporation of hot (a few MK) material into the corona and subsequent catastrophic cooling of the hot material to form the cool ( 10,000 K) prominence material. We find that both the data and model show characteristic heating and cooling signatures that are significantly different from those seen in active regions. Supported by NASA's Living with a Star program.
Del Sorbo, Maria Rosaria; Balzano, Walter; Donato, Michele; Draghici, Sorin
2013-11-01
Differential expression of genes detected with the analysis of high throughput genomic experiments is a commonly used intermediate step for the identification of signaling pathways involved in the response to different biological conditions. The impact analysis was the first approach for the analysis of signaling pathways involved in a certain biological process that was able to take into account not only the magnitude of the expression change of the genes but also the topology of signaling pathways including the type of each interactions between the genes. In the impact analysis, signaling pathways are represented as weighted directed graphs with genes as nodes and the interactions between genes as edges. Edges weights are represented by a β factor, the regulatory efficiency, which is assumed to be equal to 1 in inductive interactions between genes and equal to -1 in repressive interactions. This study presents a similarity analysis between gene expression time series aimed to find correspondences with the regulatory efficiency, i.e. the β factor as found in a widely used pathway database. Here, we focused on correlations among genes directly connected in signaling pathways, assuming that the expression variations of upstream genes impact immediately downstream genes in a short time interval and without significant influences by the interactions with other genes. Time series were processed using three different similarity metrics. The first metric is based on the bit string matching; the second one is a specific application of the Dynamic Time Warping to detect similarities even in presence of stretching and delays; the third one is a quantitative comparative analysis resulting by an evaluation of frequency domain representation of time series: the similarity metric is the correlation between dominant spectral components. These three approaches are tested on real data and pathways, and a comparison is performed using Information Retrieval benchmark tools
Wavelet-based multifractal analysis on a time series of solar activity and PDO climate index
NASA Astrophysics Data System (ADS)
Maruyama, Fumio; Kai, Kenji; Morimoto, Hiroshi
2017-09-01
There is increasing interest in finding the relation between solar activity and climate change. In general, fractal properties may be observed in the time series of the dynamics of complex systems, such as solar activity and climate. This study investigates the relations among solar activity, geomagnetic activity, and climatic regime shift by performing a multifractal analysis. To investigate the change in multifractality, we apply a wavelet transform to time series. The change in fractality of the sunspot number (SSN) correlates closely with that of the solar polar field strength. For the SSN and solar polar field strength, a weak multifractality or monofractality is present at the maximum SSN, minimum SSN, and maximum solar polar field strength. A strong multifractality is present two years before the maximum SSN. The climatic regime shift occurs when the SSN increases and the disturbance of the geomagnetic activity is large. At the climatic regime shift, the changes in the fractality of the Pacific Decadal Oscillation (PDO) index and changes in that of the solar activity indices corresponded with each other. From the fractals point of view, we clarify the relations among solar activity, geomagnetic activity, and climatic regime shift. The formation of the magnetic field of the sunspots is correlated with the solar polar field strength. The solar activity seems to influence the climatic regime shift. These findings will contribute to investigating the relation between solar activity and climate change.
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.
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.
Higa, Carlos Ha; Louzada, Vitor Hp; Andrade, Tales P; Hashimoto, Ronaldo F
2011-05-28
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. 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. 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.
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)
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.
Modelling the dynamics of nonlinear time series using canonical variate analysis
NASA Astrophysics Data System (ADS)
Pilgram, Berndt; Judd, Kevin; Mees, Alistair
2002-09-01
We report on a novel prediction method of nonlinear time series based on canonical variate analysis (CVA) and radial basis modelling. Nonlinear models of possibly chaotic and noisy systems are constructed from data via a nonlinear CVA of the past and future of the process. The canonical variables give an optimal linear combination of nonlinear coordinates of the past for describing the future. We show how our method can be used for prediction, give a comparison with other methods, and apply our prediction algorithm to simulated data from the Lorenz system and the Logistic map, to a laser experimental time series, and to sunspot data. The focus of this work is to obtain models that accurately reflect the dynamics of the system: A model should not only fit data and predict it well, but should also have a dynamical behaviour similar to that of the measured system. The results indicate that the algorithm presented is able to capture the dynamics of complex systems and gives reliable predictions when using only short data sets.
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
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
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
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
Assessment of seismic hazard of the Japanese islands based on fractal analysis of GPS time series
NASA Astrophysics Data System (ADS)
Filatov, D. M.; Lyubushin, A. A.
2017-07-01
Based on the fractal analysis of the time series of the Earth's surface vertical displacements in the region of the Japanese Archipelago, the maps of the estimates of seismic activity in the region over 2015 are constructed. The analysis of the maps revealed several segments of the territory which are prone to the emergence of significant earthquakes. The characteristic peculiarity is noted in the change of the behavior of the geophysical dynamic system—the Earth's crust—before the occurrence of seismic events: the mechanism of transition to the critical state demonstrates the energy preservation of the low frequencies with the simultaneous energy decay of the middle and high frequencies, which differs from the behavior of the other dynamical systems.
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.
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.
Statistical analysis of rainfall and streamflow time series in Lake Tana Basin, Ethiopia.
NASA Astrophysics Data System (ADS)
Tigabu, T. B.
2016-12-01
This research focuses on thestatistical analysis of rainfall and streamflow time series of Lake Tana Basin, Ethiopia. Statistical methods such as auto and cross correlations and trend analysis were applied. The study showedthat mean annual rainfalls, streamflows and the Lake level are decreasing significantly from decade to decade. The decadal mean of Abbay flow was 142.54 m3/s during 1990s and dropped to 131.20 m3/s for 2000s.Similarly, the Lake water level has shown a decreasing trend from 1990s to 2000s. It showed a negative difference of 0.31 m and 0.29 m between 1980s-1990s and 1990s-2000s respectively. The autocorrelation for both rainfall and streamflow were significantly different from zero with maximum value at time lag 1 that decreases linearlyas the time lag increases indicating that the sample data are nonrandom.
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.
A modelling framework for the analysis of artificial-selection time series.
Le Rouzic, Arnaud; Houle, David; Hansen, Thomas F
2011-04-01
Artificial-selection experiments constitute an important source of empirical information for breeders, geneticists and evolutionary biologists. Selected characters can generally be shifted far from their initial state, sometimes beyond what is usually considered as typical inter-specific divergence. A careful analysis of the data collected during such experiments may thus reveal the dynamical properties of the genetic architecture that underlies the trait under selection. Here, we propose a statistical framework describing the dynamics of selection-response time series. We highlight how both phenomenological models (which do not make assumptions on the nature of genetic phenomena) and mechanistic models (explaining the temporal trends in terms of e.g. mutations, epistasis or canalization) can be used to understand and interpret artificial-selection data. The practical use of the models and their implementation in a software package are demonstrated through the analysis of a selection experiment on the shape of the wing in Drosophila melanogaster.
Time series analysis of sferics rate data associated with severe weather patterns
NASA Technical Reports Server (NTRS)
Wang, P. P.; Burns, R. C.
1976-01-01
Data obtained by an electronic transducer measuring the rate of occurrence of electrical disturbances in the atmosphere (the sferic rate in the form of a time series) over the life of electrical storms are analyzed. It is found that the sferic rate time series are not stationary. The sferics rate time series has a complete life cycle associated with a particular storm. The approach to recognition of a spectral pattern is somewhat similar to real-time recognition of the spoken word.
Time series analysis of sferics rate data associated with severe weather patterns
NASA Technical Reports Server (NTRS)
Wang, P. P.; Burns, R. C.
1976-01-01
Data obtained by an electronic transducer measuring the rate of occurrence of electrical disturbances in the atmosphere (the sferic rate in the form of a time series) over the life of electrical storms are analyzed. It is found that the sferic rate time series are not stationary. The sferics rate time series has a complete life cycle associated with a particular storm. The approach to recognition of a spectral pattern is somewhat similar to real-time recognition of the spoken word.
NASA Astrophysics Data System (ADS)
Curceac, S.; Ternynck, C.; Ouarda, T.
2015-12-01
Over the past decades, a substantial amount of research has been conducted to model and forecast climatic variables. In this study, Nonparametric Functional Data Analysis (NPFDA) methods are applied to forecast air temperature and wind speed time series in Abu Dhabi, UAE. The dataset consists of hourly measurements recorded for a period of 29 years, 1982-2010. The novelty of the Functional Data Analysis approach is in expressing the data as curves. In the present work, the focus is on daily forecasting and the functional observations (curves) express the daily measurements of the above mentioned variables. We apply a non-linear regression model with a functional non-parametric kernel estimator. The computation of the estimator is performed using an asymmetrical quadratic kernel function for local weighting based on the bandwidth obtained by a cross validation procedure. The proximities between functional objects are calculated by families of semi-metrics based on derivatives and Functional Principal Component Analysis (FPCA). Additionally, functional conditional mode and functional conditional median estimators are applied and the advantages of combining their results are analysed. A different approach employs a SARIMA model selected according to the minimum Akaike (AIC) and Bayessian (BIC) Information Criteria and based on the residuals of the model. The performance of the models is assessed by calculating error indices such as the root mean square error (RMSE), relative RMSE, BIAS and relative BIAS. The results indicate that the NPFDA models provide more accurate forecasts than the SARIMA models. Key words: Nonparametric functional data analysis, SARIMA, time series forecast, air temperature, wind speed
Lecca, Paola; Mura, Ivan; Re, Angela; Barker, Gary C.; Ihekwaba, Adaoha E. C.
2016-01-01
Chaotic behavior refers to a behavior which, albeit irregular, is generated by an underlying deterministic process. Therefore, a chaotic behavior is potentially controllable. This possibility becomes practically amenable especially when chaos is shown to be low-dimensional, i.e., to be attributable to a small fraction of the total systems components. In this case, indeed, including the major drivers of chaos in a system into the modeling approach allows us to improve predictability of the systems dynamics. Here, we analyzed the numerical simulations of an accurate ordinary differential equation model of the gene network regulating sporulation initiation in Bacillus subtilis to explore whether the non-linearity underlying time series data is due to low-dimensional chaos. Low-dimensional chaos is expectedly common in systems with few degrees of freedom, but rare in systems with many degrees of freedom such as the B. subtilis sporulation network. The estimation of a number of indices, which reflect the chaotic nature of a system, indicates that the dynamics of this network is affected by deterministic chaos. The neat separation between the indices obtained from the time series simulated from the model and those obtained from time series generated by Gaussian white and colored noise confirmed that the B. subtilis sporulation network dynamics is affected by low dimensional chaos rather than by noise. Furthermore, our analysis identifies the principal driver of the networks chaotic dynamics to be sporulation initiation phosphotransferase B (Spo0B). We then analyzed the parameters and the phase space of the system to characterize the instability points of the network dynamics, and, in turn, to identify the ranges of values of Spo0B and of the other drivers of the chaotic dynamics, for which the whole system is highly sensitive to minimal perturbation. In summary, we described an unappreciated source of complexity in the B. subtilis sporulation network by gathering
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
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
[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.
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.
Spectral analysis of time series of categorical variables in earth sciences
NASA Astrophysics Data System (ADS)
Pardo-Igúzquiza, Eulogio; Rodríguez-Tovar, Francisco J.; Dorador, Javier
2016-10-01
Time series of categorical variables often appear in Earth Science disciplines and there is considerable interest in studying their cyclic behavior. This is true, for example, when the type of facies, petrofabric features, ichnofabrics, fossil assemblages or mineral compositions are measured continuously over a core or throughout a stratigraphic succession. Here we deal with the problem of applying spectral analysis to such sequences. A full indicator approach is proposed to complement the spectral envelope often used in other disciplines. Additionally, a stand-alone computer program is provided for calculating the spectral envelope, in this case implementing the permutation test to assess the statistical significance of the spectral peaks. We studied simulated sequences as well as real data in order to illustrate the methodology.
Alcohol sales and fatal alcohol poisonings: a time-series analysis.
Poikolainen, Kari; Leppänen, Kalervo; Vuori, Erkki
2002-08-01
To discover whether the number of fatal alcohol peaks during festivities characterized by unrestrained drinking and relates to sales of alcoholic beverages. Time-series and cross-sectional. Fatal alcohol poisonings and retail alcohol sales in Finland in 1983-99. Fatal alcohol poisonings were found to peak during weekends and in the May Day, Midsummer Day and Christmas celebrations. Regression analysis of quarterly series lead to a model showing that 1% increase in the sales of spirits increases the number of fatal alcohol poisonings by 0.4%. At the population level, increases in the sales of spirits and periods of hard drinking seem to increase deaths from alcohol poisoning. The findings could be of use in efforts to decrease hard drinking.
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.
Evaluating disease management program effectiveness: an introduction to time-series analysis.
Linden, Ariel; Adams, John L; Roberts, Nancy
2003-01-01
Currently, the most widely used method in the disease management (DM) industry for evaluating program effectiveness is referred to as the "total population approach." This model is a pretest-posttest design, with the most basic limitation being that without a control group, there may be sources of bias and/or competing extraneous confounding factors that offer a plausible rationale explaining the change from baseline. Furthermore, with the current inclination of DM programs to use financial indicators rather than program-specific utilization indicators as the principal measure of program success, additional biases are introduced that may cloud evaluation results. This paper presents a non-technical introduction to time-series analysis (using disease-specific utilization measures) as an alternative, and more appropriate, approach to evaluating DM program effectiveness than the current total population approach.
The Effects of Data Set Size on Nonlinear Time Series Analysis
NASA Astrophysics Data System (ADS)
James, John; Tolle, Charles
2000-09-01
We present the results of our study in which we investigated the effects small data sets have on nonlinear time series analysis tools, namely average mutual information, false nearest-neighbors tests and the dominant Lyapunov exponent. We also looked at the subsequent effects on attractor reconstruction. We drew our data from four well-known systems: Henon map, Rossler (3D), Lorenz (3D), and the Pinsky-Rinzel neuron model (8D), as well as an integrate-and-fire version of the Rossler system. Using results from the TISEAN and Nonlinear Dynamics Toolbox software packages, we show that for properly sampled data, there is a limiting set size less than which the algorithms fail to give clear or accurate results and complicates or prevents attractor reconstruction.
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.
Multiscale analysis of the intensity fluctuation in a time series of dynamic speckle patterns.
Federico, Alejandro; Kaufmann, Guillermo H
2007-04-10
We propose the application of a method based on the discrete wavelet transform to detect, identify, and measure scaling behavior in dynamic speckle. The multiscale phenomena presented by a sample and displayed by its speckle activity are analyzed by processing the time series of dynamic speckle patterns. The scaling analysis is applied to the temporal fluctuation of the speckle intensity and also to the two derived data sets generated by its magnitude and sign. The application of the method is illustrated by analyzing paint-drying processes and bruising in apples. The results are discussed taking into account the different time organizations obtained for the scaling behavior of the magnitude and the sign of the intensity fluctuation.
On the Character and Mitigation of Atmospheric Noise in InSAR Time Series Analysis (Invited)
NASA Astrophysics Data System (ADS)
Barnhart, W. D.; Fielding, E. J.; Fishbein, E.
2013-12-01
Time series analysis of interferometric synthetic aperture radar (InSAR) data, with its broad spatial coverage and ability to image regions that are sometimes very difficult to access, is a powerful tool for characterizing continental surface deformation and its temporal variations. With the impending launch of dedicated SAR missions such as Sentinel-1, ALOS-2, and the planned NASA L-band SAR mission, large volume data sets will allow researchers to further probe ground displacement processes with increased fidelity. Unfortunately, the precision of measurements in individual interferograms is impacted by several sources of noise, notably spatially correlated signals caused by path delays through the stratified and turbulent atmosphere and ionosphere. Spatial and temporal variations in atmospheric water vapor often introduce several to tens of centimeters of apparent deformation in the radar line-of-sight, correlated over short spatial scales (<10 km). Signals resulting from atmospheric path delays are particularly problematic because, like the subsidence and uplift signals associated with tectonic deformation, they are often spatially correlated with topography. In this talk, we provide an overview of the effects of spatially correlated tropospheric noise in individual interferograms and InSAR time series analysis, and we highlight where common assumptions of the temporal and spatial characteristics of tropospheric noise fail. Next, we discuss two classes of methods for mitigating the effects of tropospheric water vapor noise in InSAR time series analysis and single interferograms: noise estimation and characterization with independent observations from multispectral sensors such as MODIS and MERIS; and noise estimation and removal with weather models, multispectral sensor observations, and GPS. Each of these techniques can provide independent assessments of the contribution of water vapor in interferograms, but each technique also suffers from several pitfalls
Anomaly detection in flexible mechanical couplings via symbolic time series analysis
NASA Astrophysics Data System (ADS)
Khatkhate, Amol; Gupta, Shalabh; Ray, Asok; Patankar, Ravi
2008-04-01
Critical components of a rotating machinery such as bearings and couplings are often subjected to unbalanced axial and radial loads due to excessive machine vibrations arising from shaft misalignment(s). The paper presents Symbolic Time Series Analysis (STSA) of bearing acceleration data for detection and estimation of gradually developing parametric changes in flexible disc/diaphragm couplings. The analytical method is built upon the principles of Symbolic Dynamics, Automata Theory, and Statistical Pattern Recognition. The anomaly detection methodology is validated on a real-time simulation test bed, where the dynamic model of a flexible mechanical coupling is subjected to angular misalignment(s) leading to coupling failure. Damage patterns are identified from STSA of multiple data sets generated for different input torques. Statistical estimates are obtained for small changes in the coupling stiffness based on the information derived from the ensemble of damage patterns.
NASA Astrophysics Data System (ADS)
de Lauro, E.; de Martino, S.; Falanga, M.; Ciaramella, A.; Tagliaferri, R.
2005-10-01
A not trivial problem for every experimental time series associated to a natural system is to individuate the significant variables to describe the dynamics, i.e., the effective degrees of freedom. The application of independent component analysis (ICA) has provided interesting results in this direction, e.g., in the seismological and atmospheric field. Since all natural phenomena can be represented by dynamical systems, our aim is to check the performance of ICA in this general context to avoid ambiguities when investigating an unknown experimental system. We show many examples, representing linear, nonlinear, and stochastic processes, in which ICA seems to be an efficacious preanalysis able to give information about the complexity of the dynamics.
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.
Schaffner, D. A.; Brown, M. R.; Rock, A. B.
2016-05-15
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.
Singular spectrum analysis in nonlinear dynamics, with applications to paleoclimatic time series
NASA Technical Reports Server (NTRS)
Vautard, R.; Ghil, M.
1989-01-01
Two dimensions of a dynamical system given by experimental time series are distinguished. Statistical dimension gives a theoretical upper bound for the minimal number of degrees of freedom required to describe the attractor up to the accuracy of the data, taking into account sampling and noise problems. The dynamical dimension is the intrinsic dimension of the attractor and does not depend on the quality of the data. Singular Spectrum Analysis (SSA) provides estimates of the statistical dimension. SSA also describes the main physical phenomena reflected by the data. It gives adaptive spectral filters associated with the dominant oscillations of the system and clarifies the noise characteristics of the data. SSA is applied to four paleoclimatic records. The principal climatic oscillations and the regime changes in their amplitude are detected. About 10 degrees of freedom are statistically significant in the data. Large noise and insufficient sample length do not allow reliable estimates of the dynamical dimension.
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.
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.
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.
Automatic Segmentation of Invasive Breast Carcinomas from DCE-MRI using Time Series Analysis
Jayender, Jagadaeesan; Chikarmane, Sona; Jolesz, Ferenc A.; Gombos, Eva
2013-01-01
Purpose Quantitative segmentation methods based on black-box modeling and pharmacokinetic modeling are highly dependent on imaging pulse sequence, timing of bolus injection, arterial input function, imaging noise and fitting algorithms. To accurately segment invasive ductal carcinomas (IDCs) from dynamic contrast enhanced MRI (DCE-MRI) using time series analysis based on linear dynamic system (LDS) modeling. Methods We modeled the underlying dynamics of the tumor by a LDS and use the system parameters to segment the carcinoma on the DCE-MRI. Twenty-four patients with biopsy-proven IDCs were analyzed. The lesions segmented by the algorithm were compared with an expert radiologist’s segmentation and the output of a commercial software, CADstream. The results are quantified in terms of the accuracy and sensitivity of detecting the lesion and the amount of overlap, measured in terms of the Dice similarity coefficient (DSC). Results The segmentation algorithm detected the tumor with 90% accuracy and 100% sensitivity when compared to the radiologist’s segmentation and 82.1% accuracy and 100% sensitivity when compared to the CADstream output. The overlap of the algorithm output with the radiologist’s segmentation and CADstream output, computed in terms of the DSC was 0.77 and 0.72 respectively. The algorithm also shows robust stability to imaging noise. Simulated imaging noise with zero mean and standard deviation equal to 25% of the base signal intensity was added to the DCE-MRI series. The amount of overlap between the tumor maps generated by the LDS-based algorithm from the noisy and original DCE-MRI was DSC=0.95. Conclusion The time-series analysis based segmentation algorithm provides high accuracy and sensitivity in delineating the regions of enhanced perfusion corresponding to tumor from DCE-MRI. PMID:24115175
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
Predicting microRNA targets in time-series microarray experiments via functional data analysis.
Parker, Brian J; Wen, Jiayu
2009-01-30
MicroRNA (miRNA) target prediction is an important component in understanding gene regulation. One approach is computational: searching nucleotide sequences for miRNA complementary base pairing. An alternative approach explored in this paper is the use of gene expression profiles from time-series microarray experiments to aid in miRNA target prediction. This requires distinguishing genuine targets from genes that are secondarily down-regulated as part of the same regulatory module. We use a functional data analytic (FDA) approach, FDA being a subfield of statistics that extends standard multivariate techniques to datasets with predictor and/or response variables that are functional. In a miR-124 transfection experiment spanning 120 hours, for genes with measurably down-regulated mRNA, exploratory functional data analysis showed differences in expression profiles over time between directly and indirectly down-regulated genes, such as response latency and biphasic response for direct miRNA targets. For prediction, an FDA approach was shown to effectively classify direct miR-124 targets from time-series microarray data (accuracy 88%; AUC 0.96), providing better performance than multivariate approaches. Exploratory FDA analysis can reveal interesting aspects of dynamic microarray miRNA studies. Predictive FDA models can be applied where computational miRNA target predictors fail or are unreliable, e.g. when there is a lack of evolutionary conservation, and can provide posterior probabilities to provide additional confirmatory evidence to validate candidate miRNA targets computationally predicted using sequence information. This approach would be applicable to the investigation of other miRNAs and suggests that dynamic microarray studies at a higher time resolution could reveal further details on miRNA regulation.
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.
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
Trend analysis using non-stationary time series clustering based on the finite element method
NASA Astrophysics Data System (ADS)
Gorji Sefidmazgi, M.; Sayemuzzaman, M.; Homaifar, A.; Jha, M. K.; Liess, S.
2014-05-01
In order to analyze low-frequency variability of climate, it is useful to model the climatic time series with multiple linear trends and locate the times of significant changes. In this paper, we have used non-stationary time series clustering to find change points in the trends. Clustering in a multi-dimensional non-stationary time series is challenging, since the problem is mathematically ill-posed. Clustering based on the finite element method (FEM) is one of the methods that can analyze multidimensional time series. One important attribute of this method is that it is not dependent on any statistical assumption and does not need local stationarity in the time series. In this paper, it is shown how the FEM-clustering method can be used to locate change points in the trend of temperature time series from in situ observations. This method is applied to the temperature time series of North Carolina (NC) and the results represent region-specific climate variability despite higher frequency harmonics in climatic time series. Next, we investigated the relationship between the climatic indices with the clusters/trends detected based on this clustering method. It appears that the natural variability of climate change in NC during 1950-2009 can be explained mostly by AMO and solar activity.
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.
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.
Multivariate analysis of GPS position time series of JPL second reprocessing campaign
NASA Astrophysics Data System (ADS)
Amiri-Simkooei, A. R.; Mohammadloo, T. H.; Argus, D. F.
2017-01-01
The second reprocessing of all GPS data gathered by the Analysis Centers of IGS was conducted in late 2013 using the latest models and methodologies. Improved models of antenna phase center variations and solar radiation pressure in JPL's reanalysis are expected to significantly reduce errors. In an earlier work, JPL estimates of position time series, termed first reprocessing campaign, were examined in terms of their spatial and temporal correlation, power spectra, and draconitic signal. Similar analyses are applied to GPS time series at 89 and 66 sites of the second reanalysis with the time span of 7 and 21 years, respectively, to study possible improvements. Our results indicate that the spatial correlations are reduced on average by a factor of 1.25. While the white and flicker noise amplitudes for all components are reduced by 29-56 %, the random walk amplitude is enlarged. The white, flicker, and random walk noise amount to rate errors of, respectively, 0.01, 0.12, and 0.09 mm/yr in the horizontal and 0.04, 0.41 and 0.3 mm/yr in the vertical. Signals reported previously, such as those with periods of 13.63, 14.76, 5.5, and 351.4 / n for n=1,2,ldots,8 days, are identified in multivariate spectra of both data sets. The oscillation of the draconitic signal is reduced by factors of 1.87, 1.87, and 1.68 in the east, north and up components, respectively. Two other signals with Chandlerian period and a period of 380 days can also be detected.
Time series analysis of Mexico City subsidence constrained by radar interferometry
NASA Astrophysics Data System (ADS)
López-Quiroz, Penélope; Doin, Marie-Pierre; Tupin, Florence; Briole, Pierre; Nicolas, Jean-Marie
2009-09-01
In Mexico City, subsidence rates reach up to 40 cm/yr mainly due to soil compaction led by the over exploitation of the Mexico Basin aquifer. In this paper, we map the spatial and temporal patterns of the Mexico City subsidence by differential radar interferometry, using 38 ENVISAT images acquired between end of 2002 and beginning of 2007. We present the severe interferogram unwrapping problems partly due to the coherence loss but mostly due to the high fringe rates. These difficulties are overcome by designing a new methodology that helps the unwrapping step. Our approach is based on the fact that the deformation shape is stable for similar time intervals during the studied period. As a result, a stack of the five best interferograms can be used to compute an average deformation rate for a fixed time interval. Before unwrapping, the number of fringes is then decreased in wrapped interferograms using a scaled version of the stack together with the estimation of the atmospheric phase contribution related with the troposphere vertical stratification. The residual phase, containing less fringes, is more easily unwrapped than the original interferogram. The unwrapping procedure is applied in three iterative steps. The 71 small baseline unwrapped interferograms are inverted to obtain increments of radar propagation delays between the 38 acquisition dates. Based on the redundancy of the interferometric data base, we quantify the unwrapping errors and show that they are strongly decreased by iterations in the unwrapping process. A map of the RMS interferometric system misclosure allows to define the unwrapping reliability for each pixel. Finally, we present a new algorithm for time series analysis that differs from classical SVD decomposition and is best suited to the present data base. Accurate deformation time series are then derived over the metropolitan area of the city with a spatial resolution of 30 × 30 m.
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.
Time series in analysis of yerba-mate biennial growth modified by environment.
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.
Population fluctuations and regulation in great snipe: a time-series analysis.
Kölzsch, Andrea; Saether, Stein Are; Gustafsson, Henrik; Fiske, Peder; Höglund, Jacob; Kålås, John Atle
2007-07-01
1. During the last centuries, the breeding range of the great snipe Gallinago media has declined dramatically in the western part of its distribution. To examine present population dynamics in the Scandinavian mountains, we collected and analysed a 19-year time series of counts of great snipe males at leks in central Norway, 1987-2005. 2. The population showed large annual fluctuations in the number of males displaying at lek sites (range 45-90 males at the peak of the mating season), but no overall trend. 3. We detected presence of direct density-dependent mechanisms regulating this population. Inclusion of the density-dependent term in a Ricker-type model significantly improved the fit with observed data (evaluated with Parametric Bootstrap Likelihood Ratio tests and Akaike's Information Criterion for small sample size). 4. An analysis of (a number of a priori likely) environmental covariates suggests that the population dynamics were affected by conditions influencing reproduction and survival of offspring during the summer, but not by conditions influencing survival at the wintering grounds in Africa. This is in contrast to many altricial birds breeding in the northern hemisphere, and supports the idea that population dynamics of migratory nidifugous birds are more influenced by conditions during reproduction. 5. Inclusion of these external factors into our model improved the detectability of density dependence. This illustrates that allowing for external effects may increase statistical power of density dependence tests and thus be of particular importance in relatively short time series. 6. In our best model of the population dynamics, two likely density-independent offspring survival covariates explained 47.3% of the variance in great snipe numbers (predation pressure estimated by willow grouse reproductive success and food availability estimated by the amount of precipitation in June), whereas density dependence explained 35.5%. Demographic stochasticity
Multivariate analysis of GPS position time series of JPL second reprocessing campaign
NASA Astrophysics Data System (ADS)
Amiri-Simkooei, A. R.; Mohammadloo, T. H.; Argus, D. F.
2017-06-01
The second reprocessing of all GPS data gathered by the Analysis Centers of IGS was conducted in late 2013 using the latest models and methodologies. Improved models of antenna phase center variations and solar radiation pressure in JPL's reanalysis are expected to significantly reduce errors. In an earlier work, JPL estimates of position time series, termed first reprocessing campaign, were examined in terms of their spatial and temporal correlation, power spectra, and draconitic signal. Similar analyses are applied to GPS time series at 89 and 66 sites of the second reanalysis with the time span of 7 and 21 years, respectively, to study possible improvements. Our results indicate that the spatial correlations are reduced on average by a factor of 1.25. While the white and flicker noise amplitudes for all components are reduced by 29-56 %, the random walk amplitude is enlarged. The white, flicker, and random walk noise amount to rate errors of, respectively, 0.01, 0.12, and 0.09 mm/yr in the horizontal and 0.04, 0.41 and 0.3 mm/yr in the vertical. Signals reported previously, such as those with periods of 13.63, 14.76, 5.5, and 351.4 / n for n=1,2,{\\ldots },8 days, are identified in multivariate spectra of both data sets. The oscillation of the draconitic signal is reduced by factors of 1.87, 1.87, and 1.68 in the east, north and up components, respectively. Two other signals with Chandlerian period and a period of 380 days can also be detected.
Hsu, P. J.; Lai, S. K.; Cheong, S. A.
2014-05-28
Folded conformations of proteins in thermodynamically stable states have long lifetimes. Before it folds into a stable conformation, or after unfolding from a stable conformation, the protein will generally stray from one random conformation to another leading thus to rapid fluctuations. Brief structural changes therefore occur before folding and unfolding events. These short-lived movements are easily overlooked in studies of folding/unfolding for they represent momentary excursions of the protein to explore conformations in the neighborhood of the stable conformation. The present study looks for precursory signatures of protein folding/unfolding within these rapid fluctuations through a combination of three techniques: (1) ultrafast shape recognition, (2) time series segmentation, and (3) time series correlation analysis. The first procedure measures the differences between statistical distance distributions of atoms in different conformations by calculating shape similarity indices from molecular dynamics simulation trajectories. The second procedure is used to discover the times at which the protein makes transitions from one conformation to another. Finally, we employ the third technique to exploit spatial fingerprints of the stable conformations; this procedure is to map out the sequences of changes preceding the actual folding and unfolding events, since strongly correlated atoms in different conformations are different due to bond and steric constraints. The aforementioned high-frequency fluctuations are therefore characterized by distinct correlational and structural changes that are associated with rate-limiting precursors that translate into brief segments. Guided by these technical procedures, we choose a model system, a fragment of the protein transthyretin, for identifying in this system not only the precursory signatures of transitions associated with α helix and β hairpin, but also the important role played by weaker correlations in such protein
Ferrand, Yann; Kelton, Christina M L; Guo, Jeff J; Levy, Martin S; Yu, Yan
2011-03-01
Medicaid programs' spending on antidepressants increased from $159 million in 1991 to $2 billion in 2005. The National Institute for Health Care Management attributed this expenditure growth to increases in drug utilization, entry of newer higher-priced antidepressants, and greater prescription drug insurance coverage. Rising enrollment in Medicaid has also contributed to this expenditure growth. This research examines the impact of specific events, including branded-drug and generic entry, a black box warning, direct-to-consumer advertising (DTCA), and new indication approval, on Medicaid spending on antidepressants. Using quarterly expenditure data for 1991-2005 from the national Medicaid pharmacy claims database maintained by the Centers for Medicare and Medicaid Services, a time-series autoregressive integrated moving average (ARIMA) intervention analysis was performed on 6 specific antidepressant drugs and on overall antidepressant spending. Twenty-nine potentially relevant interventions and their dates of occurrence were identified from the literature. Each was tested for an impact on the time series. Forecasts from the models were compared with a holdout sample of actual expenditure data. Interventions with significant impacts on Medicaid expenditures included the patent expiration of Prozac® (P<0.01) and the entry of generic paroxetine producers (P=0.04), which reduced expenditures on Prozac® and Paxil®, respectively, and the 1997 increase in DTCA (P=0.05), which increased spending on Wellbutrin®. Except for Paxil®, the ARIMA models had low prediction errors. Generic entry at the aggregate level did not lead to a reduction in overall expenditures (P>0.05), implying that the expanding market for antidepressants overwhelmed the effect of generic competition. Copyright © 2011 Elsevier Inc. All rights reserved.
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.
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
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
Nonlinear Multivariate and Time Series Analysis by Neural Network Methods, with Applications to ENSO
NASA Astrophysics Data System (ADS)
Hsieh, W. W.
2003-12-01
Methods in multivariate statistical analysis are essential for working with large amounts of geophysical data--- data from observational arrays, from satellites or from numerical model output. In classical multivariate statistical analysis, there is a hierarchy of methods, starting with linear regression (LR) at the base, followed by principal component analysis (PCA), and finally canonical correlation analysis (CCA). A multivariate time series method, the singular spectrum analysis (SSA), has been a fruitful extension of the PCA technique. The common drawback of these classical methods is that only linear structures can be correctly extracted from the data. Since the late 1980s, neural network methods have become popular for performing nonlinear regression (NLR) and classification. More recently, multi-layer perceptron neural network methods have been extended to perform nonlinear PCA (NLPCA), nonlinear CCA (NLCCA) and nonlinear SSA (NLSSA). This paper presents a unified view of the NLPCA, NLCCA and NLSSA techniques, and their applications to various datasets of the atmosphere and the ocean, especially in the nonlinear study of the El Niño-Southern Oscillation (ENSO) phenomenon.
Tolimieri, Nick; Holmes, Elizabeth E; Williams, Gregory D; Pacunski, Robert; Lowry, Dayv
2017-04-01
Estimating a population's growth rate and year-to-year variance is a key component of population viability analysis (PVA). However, standard PVA methods require time series of counts obtained using consistent survey methods over many years. In addition, it can be difficult to separate observation and process variance, which is critical for PVA. Time-series analysis performed with multivariate autoregressive state-space (MARSS) models is a flexible statistical framework that allows one to address many of these limitations. MARSS models allow one to combine surveys with different gears and across different sites for estimation of PVA parameters, and to implement replication, which reduces the variance-separation problem and maximizes informational input for mean trend estimation. Even data that are fragmented with unknown error levels can be accommodated. We present a practical case study that illustrates MARSS analysis steps: data choice, model set-up, model selection, and parameter estimation. Our case study is an analysis of the long-term trends of rockfish in Puget Sound, Washington, based on citizen science scuba surveys, a fishery-independent trawl survey, and recreational fishery surveys affected by bag-limit reductions. The best-supported models indicated that the recreational and trawl surveys tracked different, temporally independent assemblages that declined at similar rates (an average of -3.8% to -3.9% per year). The scuba survey tracked a separate increasing and temporally independent assemblage (an average of 4.1% per year). Three rockfishes (bocaccio, canary, and yelloweye) are listed in Puget Sound under the US Endangered Species Act (ESA). These species are associated with deep water, which the recreational and trawl surveys sample better than the scuba survey. All three ESA-listed rockfishes declined as a proportion of recreational catch between the 1970s and 2010s, suggesting that they experienced similar or more severe reductions in abundance
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
FunPat: function-based pattern analysis on RNA-seq time series data.
Sanavia, Tiziana; Finotello, Francesca; Di Camillo, Barbara
2015-01-01
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. 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. 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. A novel analysis pipeline was developed to search the main temporal patterns in classes of genes similarly annotated, improving the sensitivity of gene selection by integrating the
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
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.
[Time-series analysis on the relationship between air pollution and daily mortality in Beijing].
Chang, Guiqiu; Pan, Xiaochuan; Xie, Xueqin; Gao, Yanlin
2003-11-01
To quantitively evaluate the associations between ambient air pollutant and daily mortality of Beijing and to supply the scientific bases for formulating control measures. Air pollutants including CO, SO2, NOx, TSP, PM10. time series analysis Poisson regression was used to evaluate the relationship between cause-specific deaths and air pollutant, considering the potential confounding factors such as seasonal and long-term patterns, meteorological factors (air temperature, air humidity), as well as adjusting the influence of flu epidemics in winter of 1998. The results showed that in single-factor Poisson regression analysis, there is a significant positive correlation between the four pollutants and daily mortality except for the relationship between TSP and coronary heart disease deaths. In multi-factor Poisson regression analysis, when SO2 increase in 100 micrograms/m3, respiratory deaths, cardiovascular and cerebro-vascular deaths, coronary heart disease deaths and chronic obstructive pulmonary deaths increased by 4.21%, 3.97%, 10.68%, 19.22% respectively. Meanwhile, each 100 micrograms/m3 increase in TSP associated with 3.19% increase in the respiratory deaths and 0.62% increase in the cardiovascular and cerebrovascular deaths. It is suggested that air pollution is a risk factor for health and an increase of air pollution level might lead to a raise in daily mortality.
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 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.
Scaling behaviour of heartbeat intervals obtained by wavelet-based time-series analysis
NASA Astrophysics Data System (ADS)
Ivanov, Plamen Ch.; Rosenblum, Michael G.; Peng, C.-K.; Mietus, Joseph; Havlin, Shlomo; Stanley, H. Eugene; Goldberger, Ary L.
1996-09-01
BIOLOGICAL time-series analysis is used to identify hidden dynamical patterns which could yield important insights into underlying physiological mechanisms. Such analysis is complicated by the fact that biological signals are typically both highly irregular and non-stationary, that is, their statistical character changes slowly or intermittently as a result of variations in background influences1-3. Previous statistical analyses of heartbeat dynamics4-6 have identified long-range correlations and power-law scaling in the normal heartbeat, but not the phase interactions between the different frequency components of the signal. Here we introduce a new approach, based on the wavelet transform and an analytic signal approach, which can characterize non-stationary behaviour and elucidate such phase interactions. We find that, when suitably rescaled, the distributions of the variations in the beat-to-beat intervals for all healthy subjects are described by a single function stable over a wide range of timescales. However, a similar scaling function does not exist for a group with cardiopulmonary instability caused by sleep apnoea. We attribute the functional form of the scaling observed in the healthy subjects to underlying nonlinear dynamics, which seem to be essential to normal heart function. The approach introduced here should be useful in the analysis of other nonstationary biological signals.
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.
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 groundwater chemistry in the west Tennessee sand aquifers
Wilson, T.M.; Ogden, A.E.; Mills, H.H. III )
1992-07-01
In West Tennessee, nearly all municipalities, industries, and rural residents rely on groundwater. To understand water quality changes caused by mans' activities, it is first important to establish background ionic concentrations from a time-series analysis of past data for wellhead protection purposes. For this analysis, well-water quality data from a 1964-1965 survey by the Tennessee Stream Pollution Control Board was compared to precipitation data. In all of the aquifers studied, calcium increased with depth probably due to the continual solution of calcium minerals as water moved downgradient. Iron also increased with depth to the aquifer associated with a change from oxidizing to reducing conditions downdip. A spatial analysis of nitrite and iron levels was made using data from the Tennessee Division of Water Supply for the period 1982 through 1987. Nitrate and chloride concentrations were higher in recharge areas possibly due to surface contamination sources. The significant variability of chemical constituents was found to be related to recharge events, depth to the aquifer, spatial changes in aquifer lithology, and man's activities in the recharge areas. The inter-relationships of these factors must be understood for determining site-specific ambient quality conditions before implementing a wellhead protection program. 9 refs., 6 figs.
Wu, Jianyu; Xiao, Zhifu; Zhao, Xiulei; Wu, Xiangsong
2015-05-01
Cholangiocarcinoma (CC) is a rapidly lethal malignancy and currently is considered to be incurable. Biomarkers related to the development of CC remain unclear. The present study aimed to identify differentially expressed genes (DEGs) between normal tissue and intrahepatic CC, as well as specific gene expression patterns that changed together with the development of CC. By using a two‑way analysis of variance test, the biomarkers that could distinguish between normal tissue and intrahepatic CC dissected from different days were identified. A k‑means cluster method was used to identify gene clusters associated with the development of CC according to their changing expression pattern. Functional enrichment analysis was used to infer the function of each of the gene sets. A time series analysis was constructed to reveal gene signatures that were associated with the development of CC based on gene expression profile changes. Genes related to CC were shown to be involved in 'mitochondrion' and 'focal adhesion'. Three interesting gene groups were identified by the k‑means cluster method. Gene clusters with a unique expression pattern are related with the development of CC. The data of this study will facilitate novel discoveries regarding the genetic study of CC by further work.
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.
Kassakian, Steven Z; Yackel, Thomas R; Deloughery, Thomas; Dorr, David A
2016-06-01
Red blood cell transfusion is the most common procedure in hospitalized patients in the US. Growing evidence suggests that a sizeable percentage of these transfusions are inappropriate, putting patients at significant risk and increasing costs to the health care system. We performed a retrospective quasi-experimental study from November 2008 until November 2014 in a 576-bed tertiary care hospital. The intervention consisted of an interruptive clinical decision support alert shown to a provider when a red blood cell transfusion was ordered in a patient whose most recent hematocrit was ≥21%. We used interrupted time series analysis to determine whether our primary outcome of interest, rate of red blood cell transfusion in patients with hematocrit ≥21% per 100 patient (pt) days, was reduced by the implementation of the clinical decision support tool. The rate of platelet transfusions was used as a nonequivalent dependent control variable. A total of 143,000 hospital admissions were included in our analysis. Red blood cell transfusions decreased from 9.4 to 7.8 per 100 pt days after the clinical decision support intervention was implemented. Interrupted time series analysis showed that significant decline of 0.05 (95% confidence interval [CI], 0.03-0.07; P < .001) units of red blood cells transfused per 100 pt days per month was already underway in the preintervention period. This trend accelerated to 0.1 (95% CI, 0.09-0.12; P < .001) units of red blood cells transfused per 100 pt days per month following the implementation of the clinical decision support tool. There was no statistical change in the rate of platelet transfusion resulting from the intervention. The implementation of an evidence-based clinical decision support tool was associated with a significant decline in the overuse of red blood cell transfusion. We believe this intervention could be easily replicated in other hospitals using commercial electronic health records and a similar reduction in
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.
A New Modified Histogram Matching Normalization for Time Series Microarray Analysis.
Astola, Laura; Molenaar, Jaap
2014-07-01
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
Preliminary characterization of binary karst aquifers with tracer tests and time series analysis
NASA Astrophysics Data System (ADS)
Ferrari, J. A.; Calux, A. S.; Hiruma, S. T.; Armani, G.; Karmann, I.
2016-12-01
The studied site is a polygonal karst developed in a synclinal structure in the Atlantic Rainforest, southeastern Brazil. The carbonatic surface (10.4 km2) receives allogenic recharge from drainage basins (13.9 km2) formed in psammitic rocks. Two main springs drains the karst on the opposite flanks of the synclinal: Alambari (AL) and Ouro Grosso (OG). The karst is inserted in a conservation unit and the hydrological investigation supports its management. Qualitative dye tracer tests were performed to identify recharge areas of the two springs. Monitoring stations at springs measured the water discharge (Q) and the specific conductance, (SC) every hour. The rainfall (R) was measured by a pluviometer connected to an event logger. The time series (2014 to 2016) were analyzed with autocorrelation (ACF) and cross-correlation functions (CCF) to compare the flow dynamics of both systems. Tracer tests indicate that AL spring drains most of the area. Field observations show that the main volume of perennial sink waters is related with this spring. The average values of the parameters from the hydrologic monitoring are: AL - Q= 0.6 m3/s, SC = 137.7 µS cm-1; OG - Q= 0.1 m3/s, SC=158.2 µS cm-1. The mean annual rainfall in the region is 1250 mm. The global analysis of Q (daily average) with ACF shows that memory effect in OG is 3 times higher than the obtained for AL. The same analysis for SC shows that the memory is 1.5 times higher in AL. The CCF was also used to analyze the relations between R, Q and SC time series (in hour basis). When analyzing CCF for R x Q, the maximum value occurs after 4 h for AL (r= 0.31) and after 3 h for OG (r= 0.25). Contrasting results were observed when CCF was applied for R x SC. The CCF for AL shows the usual behavior with a "negative peak" (after 13 h) that represents the pulse of fresh infiltrated rainwater, whereas OG shows a "negative peak" (after 2 h), followed by a 50 h peak (peaks identified with 99% of confidence intervals). The
NASA Astrophysics Data System (ADS)
Kim, J.; Park, M.; Baik, H. S.; Choi, Y.
2016-12-01
At the present time, arguments continue regarding the migration speeds of Martian dune fields and their correlation with atmospheric circulation. However, precisely measuring the spatial translation of Martian dunes has rarely conducted only a very few times Therefore, we developed a generic procedure to precisely measure the migration of dune fields with recently introduced 25-cm resolution High Resolution Imaging Science Experimen (HIRISE) employing a high-accuracy photogrammetric processor and sub-pixel image correlator. The processor was designed to trace estimated dune migration, albeit slight, over the Martian surface by 1) the introduction of very high resolution ortho images and stereo analysis based on hierarchical geodetic control for better initial point settings; 2) positioning error removal throughout the sensor model refinement with a non-rigorous bundle block adjustment, which makes possible the co-alignment of all images in a time series; and 3) improved sub-pixel co-registration algorithms using optical flow with a refinement stage conducted on a pyramidal grid processor and a blunder classifier. Moreover, volumetric changes of Martian dunes were additionally traced by means of stereo analysis and photoclinometry. The established algorithms have been tested using high-resolution HIRISE images over a large number of Martian dune fields covering whole Mars Global Dune Database. Migrations over well-known crater dune fields appeared to be almost static for the considerable temporal periods and were weakly correlated with wind directions estimated by the Mars Climate Database (Millour et al. 2015). Only over a few Martian dune fields, such as Kaiser crater, meaningful migration speeds (>1m/year) compared to phtotogrammetric error residual have been measured. Currently a technical improved processor to compensate error residual using time series observation is under developing and expected to produce the long term migration speed over Martian dune
Time series analysis of Japanese encephalitis and weather in Linyi City, China.
Lin, Hualiang; Yang, Liuqing; Liu, Qiyong; Wang, Tong; Hossain, Sarah R; Ho, Suzanne C; Tian, Linwei
2012-04-01
To examine the relationship between meteorological factors and epidemiological pattern of Japanese encephalitis in Linyi City during 1956-2004. Autoregressive integrated moving average (ARIMA) models were used to evaluate the relationship between monthly JE incidence and weather factors. Logarithmic transformation was applied to the JE incidence series to assure the normality and homogeneity of variance of the residuals. The effect of mass vaccination on JE incidence was also evaluated using a transfer function in the time series analysis. The analysis suggested that monthly average temperature [β = 0.0574, 95% confidence interval (CI) = (0.0172, 0.0976)] and relative humidity [β = 0.0082, 95% CI = (0.0004, 0.0158)] were positively associated with the logarithmic incidence of Japanese encephalitis after adjusting for mass vaccination in this area. Weather variables might be treated as possible predictors of Japanese encephalitis incidence for regions with similar geographic, weather, and socio-economic conditions to Linyi, China.
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
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.
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.
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.
NASA Astrophysics Data System (ADS)
Lopes, A.; Dracup, J. A.
2011-12-01
The statistical analysis of multiyear drought events in streamflow records is often restricted by the size of samples since only a few number of droughts events can be extracted from common river flow time series data. An alternative to those conventional datasets is the use of paleo hydrologic data such as streamflow time series reconstructed from tree ring analysis. In this study, we analyze the statistical characteristics of drought events present in a 1439 year long time series of reconstructed annual streamflow records at the Feather river inflow to the Oreville reservoir, California. Also, probabilistic distributions were used to describe duration and severity of drought events and the results were compared with previous studies that used only the observed streamflow data. Finally, a stochastic simulation model was developed to synthetically generate sequences of drought and high-flow events with the same characteristics of the paleo hydrologic record. The long term mean flow was used as the single truncation level to define 248 drought events and 248 high flow events with specific duration and severity. The longest drought and high flow events had 13 years (1471 to 1483) and 9 years of duration (1903 to 1911), respectively. A strong relationship between event duration and severity in both drought and high flow events were found so the longest droughts also corresponded to the more severe ones. Therefore, the events were classified by duration (in years) and probability distributions were fitted to the frequency distribution of drought and high flow severity for each duration. As a result, it was found that the gamma distribution describes well the frequency distribution of drought severities for all durations. For high flow events, the exponential distribution is more adequate for one year events while the gamma distribution is better suited for the longer events. Those distributions can be used to estimate the recurrence time of drought events according to
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.
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)
Interrupted time-series analysis of regulations to reduce paracetamol (acetaminophen) poisoning.
Morgan, Oliver W; Griffiths, Clare; Majeed, Azeem
2007-04-01
Paracetamol (acetaminophen) poisoning is the leading cause of acute liver failure in Great Britain and the United States. Successful interventions to reduced harm from paracetamol poisoning are needed. To achieve this, the government of the United Kingdom introduced legislation in 1998 limiting the pack size of paracetamol sold in shops. Several studies have reported recent decreases in fatal poisonings involving paracetamol. We use interrupted time-series analysis to evaluate whether the recent fall in the number of paracetamol deaths is different to trends in fatal poisoning involving aspirin, paracetamol compounds, antidepressants, or nondrug poisoning suicide. We calculated directly age-standardised mortality rates for paracetamol poisoning in England and Wales from 1993 to 2004. We used an ordinary least-squares regression model divided into pre- and postintervention segments at 1999. The model included a term for autocorrelation within the time series. We tested for changes in the level and slope between the pre- and postintervention segments. To assess whether observed changes in the time series were unique to paracetamol, we compared against poisoning deaths involving compound paracetamol (not covered by the regulations), aspirin, antidepressants, and nonpoisoning suicide deaths. We did this comparison by calculating a ratio of each comparison series with paracetamol and applying a segmented regression model to the ratios. No change in the ratio level or slope indicated no difference compared to the control series. There were about 2,200 deaths involving paracetamol. The age-standardised mortality rate rose from 8.1 per million in 1993 to 8.8 per million in 1997, subsequently falling to about 5.3 per million in 2004. After the regulations were introduced, deaths dropped by 2.69 per million (p = 0.003). Trends in the age-standardised mortality rate for paracetamol compounds, aspirin, and antidepressants were broadly similar to paracetamol, increasing until
Interrupted Time-Series Analysis of Regulations to Reduce Paracetamol (Acetaminophen) Poisoning
Morgan, Oliver W; Griffiths, Clare; Majeed, Azeem
2007-01-01
Background Paracetamol (acetaminophen) poisoning is the leading cause of acute liver failure in Great Britain and the United States. Successful interventions to reduced harm from paracetamol poisoning are needed. To achieve this, the government of the United Kingdom introduced legislation in 1998 limiting the pack size of paracetamol sold in shops. Several studies have reported recent decreases in fatal poisonings involving paracetamol. We use interrupted time-series analysis to evaluate whether the recent fall in the number of paracetamol deaths is different to trends in fatal poisoning involving aspirin, paracetamol compounds, antidepressants, or nondrug poisoning suicide. Methods and Findings We calculated directly age-standardised mortality rates for paracetamol poisoning in England and Wales from 1993 to 2004. We used an ordinary least-squares regression model divided into pre- and postintervention segments at 1999. The model included a term for autocorrelation within the time series. We tested for changes in the level and slope between the pre- and postintervention segments. To assess whether observed changes in the time series were unique to paracetamol, we compared against poisoning deaths involving compound paracetamol (not covered by the regulations), aspirin, antidepressants, and nonpoisoning suicide deaths. We did this comparison by calculating a ratio of each comparison series with paracetamol and applying a segmented regression model to the ratios. No change in the ratio level or slope indicated no difference compared to the control series. There were about 2,200 deaths involving paracetamol. The age-standardised mortality rate rose from 8.1 per million in 1993 to 8.8 per million in 1997, subsequently falling to about 5.3 per million in 2004. After the regulations were introduced, deaths dropped by 2.69 per million (p = 0.003). Trends in the age-standardised mortality rate for paracetamol compounds, aspirin, and antidepressants were broadly similar to
Kastner, Monika; Sawka, Anna M; Hamid, Jemila; Chen, Maggie; Thorpe, Kevin; Chignell, Mark; Ewusie, Joycelyne; Marquez, Christine; Newton, David; Straus, Sharon E
2014-09-25
Osteoporosis affects over 200 million people worldwide at a high cost to healthcare systems, yet gaps in management still exist. In response, we developed a multi-component osteoporosis knowledge translation (Op-KT) tool involving a patient-initiated risk assessment questionnaire (RAQ), which generates individualized best practice recommendations for physicians and customized education for patients at the point of care. The objective of this study was to evaluate the effectiveness of the Op-KT tool for appropriate disease management by physicians. The Op-KT tool was evaluated using an interrupted time series design. This involved multiple assessments of the outcomes 12 months before (baseline) and 12 months after tool implementation (52 data points in total). Inclusion criteria were family physicians and their patients at risk for osteoporosis (women aged ≥ 50 years, men aged ≥ 65 years). Primary outcomes were the initiation of appropriate osteoporosis screening and treatment. Analyses included segmented linear regression modeling and analysis of variance. The Op-KT tool was implemented in three family practices in Ontario, Canada representing 5 family physicians with 2840 age eligible patients (mean age 67 years; 76% women). Time series regression models showed an overall increase from baseline in the initiation of screening (3.4%; P < 0.001), any osteoporosis medications (0.5%; P = 0.006), and calcium or vitamin D (1.2%; P = 0.001). Improvements were also observed at site level for all the three sites considered, but these results varied across the sites. Of 351 patients who completed the RAQ unprompted (mean age 64 years, 77% women), the mean time for completing the RAQ was 3.43 minutes, and 56% had any disease management addressed by their physician. Study limitations included the inherent susceptibility of our design compared with a randomized trial. The multicomponent Op-KT tool significantly increased osteoporosis investigations in three
Scott, John W; Nyinawankusi, Jeanne D'Arc; Enumah, Samuel; Maine, Rebecca; Uwitonze, Eric; Hu, Yihan; Kabagema, Ignace; Byiringiro, Jean Claude; Riviello, Robert; Jayaraman, Sudha
2017-07-01
Injury is a major cause of premature death and disability in East Africa, and high-quality pre-hospital care is essential for optimal trauma outcomes. The Rwandan pre-hospital emergency care service (SAMU) uses an electronic database to evaluate and optimize pre-hospital care through a continuous quality improvement programme (CQIP), beginning March 2014. The SAMU database was used to assess pre-hospital quality metrics including supplementary oxygen for hypoxia (O2), intravenous fluids for hypotension (IVF), cervical collar placement for head injuries (c-collar), and either splinting (splint) or administration of pain medications (pain) for long bone fractures. Targets of >90% were set for each metric and daily team meetings and monthly feedback sessions were implemented to address opportunities for improvement. These five pre-hospital quality metrics were assessed monthly before and after implementation of the CQIP. Met and unmet needs for O2, IVF, and c-collar were combined into a summative monthly SAMU Trauma Quality Scores (STQ score). An interrupted time series linear regression model compared the STQ score during 14 months before the CQIP implementation to the first 14 months after. During the 29-month study period 3,822 patients met study criteria. 1,028 patients needed one or more of the five studied interventions during the study period. All five endpoints had a significant increase between the pre-CQI and post-CQI periods (p<0.05 for all), and all five achieved a post-CQI average of at least 90% completion. The monthly composite STQ scores ranged from 76.5 to 97.9 pre-CQI, but tightened to 86.1-98.7 during the post-CQI period. Interrupted time series analysis of the STQ score showed that CQI programme led to both an immediate improvement of +6.1% (p=0.017) and sustained monthly improvements in care delivery-improving at a rate of 0.7% per month (p=0.028). The SAMU experience demonstrates the utility of a responsive, data-driven quality improvement
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
Vibration-based damage detection in plates by using time series analysis
NASA Astrophysics Data System (ADS)
Trendafilova, Irina; Manoach, Emil
2008-07-01
This paper deals with the problem of vibration health monitoring (VHM) in structures with nonlinear dynamic behaviour. It aims to introduce two viable VHM methods that use large amplitude vibrations and are based on nonlinear time series analysis. The methods suggested explore some changes in the state space geometry/distribution of the structural dynamic response with damage and their use for damage detection purposes. One of the methods uses the statistical distribution of state space points on the attractor of a vibrating structure, while the other one is based on the Poincaré map of the state space projected dynamic response. In this paper both methods are developed and demonstrated for a thin vibrating plate. The investigation is based on finite element modelling of the plate vibration response. The results obtained demonstrate the influence of damage on the local dynamic attractor of the plate state space and the applicability of the proposed strategies for damage assessment. The approach taken in this study and the suggested VHM methods are rather generic and permit development and applications for other more complex nonlinear structures.
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.
Electroencephalography when meditation advances: a case-based time-series analysis.
Tsai, Jui-Feng; Jou, Shaw-Hwa; Cho, WenChun; Lin, Chieh-Min
2013-11-01
Increased alpha and theta activities in electroencephalography (EEG) have been found during various forms of meditation. However, advanced stage of meditation drew less attention to date. We aimed at exploring EEG characteristics during advanced meditation. Bilateral absolute alpha and theta EEG powers were recorded when a single meditator at rest, exercising breath meditation, and reaching the advanced meditative stage in 10 sessions of meditation. Averaged time-series data were analyzed using simulation modeling analysis to compare the powers during different meditative phases. During breath meditation, significantly higher activities compared with baseline were found only in bilateral theta (P = 0.0406, 0.0158 for left and right sides, respectively), but not in alpha (P = 0.1412, 0.0978 for left and right sides, respectively) bands. When meditation advanced, significantly increased activities were found both in bilateral alpha (P = 0.0218, 0.0258 for left and right sides, respectively) and theta (P = 0.0308, 0.0260 for left and right sides, respectively) bands compared against breath meditation. When advanced meditation compared against baseline, bilateral alpha (P = 0.0001, 0.0001 for left and right sides, respectively) and theta (P = 0.0001, 0.0001 for left and right sides, respectively) bands revealed significantly increased activities. Our findings support that internalized attention manifested as theta activity continuingly enhances significantly in sequential phases of meditation, while relaxation manifested as alpha activity is significant only after the advanced meditative phase is reached.
Adegboye, Oyelola A.; Adegboye, Majeed
2017-01-01
Leishmaniasis is the third most common vector-borne disease and a very important protozoan infection. Cutaneous leishmaniasis is one of the most common types of leishmaniasis infectious diseases with up to 1.2 million occurrences of new cases each year worldwide. A dynamic transmission multivariate time series model was applied to the data to account for overdispersion and evaluate the effects of three environmental layers as well as seasonality in the data. Furthermore, ecological niche modeling was used to study the geographically suitable conditions for cutaneous leishmaniasis using temperature, precipitation and altitude as environmental layers, together with the leishmaniasis presence data. A retrospective analysis of the cutaneous leishmaniasis spatial data in Afghanistan between 2003 and 2009 indicates a steady increase from 2003 to 2007, a small decrease in 2008, and then another increase in 2009. An upward trend and regularly repeating patterns of highs and lows were observed related to the months of the year, which suggests seasonality effect in the data. Two peaks were observed in the disease occurrence—January to March and September to December—which coincide with the cold period. Ecological niche modelling indicates that precipitation has the greatest contribution to the potential distribution of leishmaniasis. PMID:28304356
The impact of guidelines on mild hypertension in pregnancy: time series analysis.
Foy, Robbie; Ramsay, Craig R; Grimshaw, Jeremy M; Penney, Gillian C; Vale, Luke; Thomson, Andrew; Greer, Ian A
2004-08-01
To determine the impact of a national strategy to promote implementation of a guideline on the management of mild, non-proteinuric hypertension in pregnancy. Simple, interrupted time series analysis. Four maternity units in Scotland. Women delivering a live or stillborn baby. Dissemination of the guideline under the auspices of a national clinical effectiveness programme, supported by a national launch meeting and feedback from a survey of obstetricians highlighting aspects of care that could be improved. Appropriateness of initial investigation and subsequent clinical management, and costs of guideline development and implementation activities. Twenty-four months pre-intervention and 12 months post-intervention data were abstracted from a random sample of case notes. Initial investigation was consistent with recommendations for 59.9% out of 1263 women and subsequent clinical management for 67.6% out of 1081 in whom a diagnosis could be made from available data. There were no significant changes in the appropriateness of initial investigation (10.6%; 95% confidence interval [CI] -0.1% to 19.3%; decreasing by 1.2% per month post-implementation, 95% CI -2.5% to 0.1%) or clinical management (-0.3%; 95% CI -8.7% to 11.2%). Guideline development and implementation cost an estimated pound 2784 per maternity unit in Scotland. Clinical care of mild hypertension in pregnancy remains highly inconsistent. The lack of the intervention effect may be related to the complexity of the guideline recommendations and the nature of the implementation strategy.
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
Adegboye, Oyelola A; Adegboye, Majeed
2017-03-17
Leishmaniasis is the third most common vector-borne disease and a very important protozoan infection. Cutaneous leishmaniasis is one of the most common types of leishmaniasis infectious diseases with up to 1.2 million occurrences of new cases each year worldwide. A dynamic transmission multivariate time series model was applied to the data to account for overdispersion and evaluate the effects of three environmental layers as well as seasonality in the data. Furthermore, ecological niche modeling was used to study the geographically suitable conditions for cutaneous leishmaniasis using temperature, precipitation and altitude as environmental layers, together with the leishmaniasis presence data. A retrospective analysis of the cutaneous leishmaniasis spatial data in Afghanistan between 2003 and 2009 indicates a steady increase from 2003 to 2007, a small decrease in 2008, and then another increase in 2009. An upward trend and regularly repeating patterns of highs and lows were observed related to the months of the year, which suggests seasonality effect in the data. Two peaks were observed in the disease occurrence-January to March and September to December-which coincide with the cold period. Ecological niche modelling indicates that precipitation has the greatest contribution to the potential distribution of leishmaniasis.
Time series analysis of dengue fever and weather in Guangzhou, China
Lu, Liang; Lin, Hualiang; Tian, Linwei; Yang, Weizhong; Sun, Jimin; Liu, Qiyong
2009-01-01
Background Monitoring and predicting dengue incidence facilitates early public health responses to minimize morbidity and mortality. Weather variables are potential predictors of dengue incidence. This study explored the impact of weather variability on the transmission of dengue fever in the subtropical city of Guangzhou, China. Methods Time series Poisson regression analysis was performed using data on monthly weather variables and monthly notified cases of dengue fever in Guangzhou, China for the period of 2001-2006. Estimates of the Poisson model parameters was implemented using the Generalized Estimating Equation (GEE) approach; the quasi-likelihood based information criterion (QICu) was used to select the most parsimonious model. Results Two best fitting models, with the smallest QICu values, are selected to characterize the relationship between monthly dengue incidence and weather variables. Minimum temperature and wind velocity are significant predictors of dengue incidence. Further inclusion of minimum humidity in the model provides a better fit. Conclusion Minimum temperature and minimum humidity, at a lag of one month, are positively associated with dengue incidence in the subtropical city of Guangzhou, China. Wind velocity is inversely associated with dengue incidence of the same month. These findings should be considered in the prediction of future patterns of dengue transmission. PMID:19860867
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
Air pollution, weather, and violent crimes: concomitant time-series analysis of archival data.
Rotton, J; Frey, J
1985-11-01
Archival data covering a 2-year period were obtained from three sources in order to assess relations among ozone levels, nine measures of meteorological conditions, day of the week, holidays, seasonal trends, family disturbances, and assaults against persons. Confirming results obtained in laboratory studies, more family disturbances were recorded when ozone levels were high than when they were low. Two-stage regression analyses indicated that disturbances and assaults against persons were also positively correlated with daily temperatures and negatively correlated with wind speed and levels of humidity. Further, distributed lag (Box-Jenkins) analyses indicated that high temperatures and low winds preceded violent episodes, which occurred more often on dry than humid days. In addition to hypothesized relations, it was also found that assaults follow complaints about family disturbances, which suggests that the latter could be used to predict and lessen physical violence. It was concluded that atmospheric conditions and violent episodes are not only correlated but also appear to be linked in a causal fashion. This conclusion, however, was qualified by a discussion of the limitations of archival data and concomitant time-series analysis.
Cheng, Qing; Lu, Xin; Wu, Joseph T.; Liu, Zhong; Huang, Jincai
2016-01-01
Guangdong experienced the largest dengue epidemic in recent history. In 2014, the number of dengue cases was the highest in the previous 10 years and comprised more than 90% of all cases. In order to analyze heterogeneous transmission of dengue, a multivariate time series model decomposing dengue risk additively into endemic, autoregressive and spatiotemporal components was used to model dengue transmission. Moreover, random effects were introduced in the model to deal with heterogeneous dengue transmission and incidence levels and power law approach was embedded into the model to account for spatial interaction. There was little spatial variation in the autoregressive component. In contrast, for the endemic component, there was a pronounced heterogeneity between the Pearl River Delta area and the remaining districts. For the spatiotemporal component, there was considerable heterogeneity across districts with highest values in some western and eastern department. The results showed that the patterns driving dengue transmission were found by using clustering analysis. And endemic component contribution seems to be important in the Pearl River Delta area, where the incidence is high (95 per 100,000), while areas with relatively low incidence (4 per 100,000) are highly dependent on spatiotemporal spread and local autoregression. PMID:27666657
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.
Nonlinear time series analysis of knee and ankle kinematics during side by side treadmill walking
NASA Astrophysics Data System (ADS)
Nessler, Jeff A.; De Leone, Charles J.; Gilliland, Sara
2009-06-01
Nonlinear time series analysis was used to estimate maximal Lyapunov exponents of select ankle and knee kinematics during three different conditions of treadmill walking: independent, side by side, and side by side with forced synchronization of stepping. Stride to stride variability was significantly increased for the condition in which individuals walked side by side and synchronized unintentionally when compared to the conditions of forced synchronization and independent walking. In addition, standard deviations of three kinematic variables of lower extremity movement were significantly increased during the condition in which unintentional synchronization occurred. No relationship was found between standard deviation and estimates of maximal Lyapunov exponents. An increase in kinematic variability during side by side walking for nonimpaired individuals who are not at risk of falling suggests that variability in certain aspects of performance might be indicative of a healthy system. Modeling this variability for an impaired individual to imitate may have beneficial effects on locomotor function. These results may therefore have implications for the rehabilitation of gait in humans by suggesting that a different functional outcome might be achieved by practicing side by side walking as opposed to more commonly used strategies involving independent walking.
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.
NASA Astrophysics Data System (ADS)
Amigó, José M.; Hirata, Yoshito; Aihara, Kazuyuki
2017-08-01
In a previous paper, the authors studied the limits of probabilistic prediction in nonlinear time series analysis in a perfect model scenario, i.e., in the ideal case that the uncertainty of an otherwise deterministic model is due to only the finite precision of the observations. The model consisted of the symbolic dynamics of a measure-preserving transformation with respect to a finite partition of the state space, and the quality of the predictions was measured by the so-called ignorance score, which is a conditional entropy. In practice, though, partitions are dispensed with by considering numerical and experimental data to be continuous, which prompts us to trade off in this paper the Shannon entropy for the differential entropy. Despite technical differences, we show that the core of the previous results also hold in this extended scenario for sufficiently high precision. The corresponding imperfect model scenario will be revisited too because it is relevant for the applications. The theoretical part and its application to probabilistic forecasting are illustrated with numerical simulations and a new prediction algorithm.
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.
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.
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.
Monitoring anaerobic sequential batch reactors via fractal analysis of pH time series.
Méndez-Acosta, H O; Hernandez-Martinez, E; Jáuregui-Jáuregui, J A; Alvarez-Ramirez, J; Puebla, H
2013-08-01
Efficient monitoring and control schemes are mandatory in the current operation of biological wastewater treatment plants because they must accomplish more demanding environmental policies. This fact is of particular interest in anaerobic digestion processes where the availability of accurate, inexpensive, and suitable sensors for the on-line monitoring of key process variables remains an open problem nowadays. In particular, this problem is more challenging when dealing with batch processes where the monitoring strategy has to be performed in finite time, which limits the application of current advanced monitoring schemes as those based in the proposal of nonlinear observers (i.e., software sensors). In this article, a fractal time series analysis of pH fluctuations in an anaerobic sequential batch reactor (AnSBR) used for the treatment of tequila vinasses is presented. Results indicated that conventional on-line pH measurements can be correlated with off-line determined key process variables, such as COD, VFA and biogas production via some fractality indexes.
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)
Tang, H.; Huang, C.; Dubayah, R.
2011-12-01
Forest often recovers after a disturbance event until it reaches an equilibrium stage. This process can be observed through examining several geophysical parameters (e.g. biomass, canopy height and LAI). Quantifying these parameters at fine scale is important for understanding carbon stocks and fluxes. The La Selva Biological Station in Costa Rica is a good example for studying secondary forest regrowth from disturbance. Since Lidar can provide the most accurate estimate of biomass compared to other remote sensing methods and Landsat ha