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.
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…
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.
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.
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.
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
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
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
2013-06-06
The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines.
Highly comparative time-series analysis: the empirical structure of time series and their methods
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
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.
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.
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
Three Analysis Examples for Time Series Data
Technology Transfer Automated Retrieval System (TEKTRAN)
With improvements in instrumentation and the automation of data collection, plot level repeated measures and time series data are increasingly available to monitor and assess selected variables throughout the duration of an experiment or project. Records and metadata on variables of interest alone o...
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.
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.
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 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.
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 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.
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.
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.
Time Series Analysis Based on Running Mann Whitney Z Statistics
Technology Transfer Automated Retrieval System (TEKTRAN)
A sensitive and objective time series analysis method based on the calculation of Mann Whitney U statistics is described. This method samples data rankings over moving time windows, converts those samples to Mann-Whitney U statistics, and then normalizes the U statistics to Z statistics using Monte-...
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.
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.
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.
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.
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.
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 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.
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.
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.
Studies in Astronomical Time Series Analysis. VI. Bayesian Block Representations
NASA Technical Reports Server (NTRS)
Scargle, Jeffrey D.; Norris, Jay P.; Jackson, Brad; Chiang, James
2013-01-01
This paper addresses the problem of detecting and characterizing local variability in time series and other forms of sequential data. The goal is to identify and characterize statistically significant variations, at the same time suppressing the inevitable corrupting observational errors. We present a simple nonparametric modeling technique and an algorithm implementing it-an improved and generalized version of Bayesian Blocks [Scargle 1998]-that finds the optimal segmentation of the data in the observation interval. The structure of the algorithm allows it to be used in either a real-time trigger mode, or a retrospective mode. Maximum likelihood or marginal posterior functions to measure model fitness are presented for events, binned counts, and measurements at arbitrary times with known error distributions. Problems addressed include those connected with data gaps, variable exposure, extension to piece- wise linear and piecewise exponential representations, multivariate time series data, analysis of variance, data on the circle, other data modes, and dispersed data. Simulations provide evidence that the detection efficiency for weak signals is close to a theoretical asymptotic limit derived by [Arias-Castro, Donoho and Huo 2003]. In the spirit of Reproducible Research [Donoho et al. (2008)] all of the code and data necessary to reproduce all of the figures in this paper are included as auxiliary material.
A Multiscale Approach to InSAR Time Series Analysis
NASA Astrophysics Data System (ADS)
Hetland, E. A.; Muse, P.; Simons, M.; Lin, N.; Dicaprio, C. J.
2010-12-01
We present a technique to constrain time-dependent deformation from repeated satellite-based InSAR observations of a given region. This approach, which we call MInTS (Multiscale InSAR Time Series analysis), relies on a spatial wavelet decomposition to permit the inclusion of distance based spatial correlations in the observations while maintaining computational tractability. As opposed to single pixel InSAR time series techniques, MInTS takes advantage of both spatial and temporal characteristics of the deformation field. We use a weighting scheme which accounts for the presence of localized holes due to decorrelation or unwrapping errors in any given interferogram. We represent time-dependent deformation using a dictionary of general basis functions, capable of detecting both steady and transient processes. The estimation is regularized using a model resolution based smoothing so as to be able to capture rapid deformation where there are temporally dense radar acquisitions and to avoid oscillations during time periods devoid of acquisitions. MInTS also has the flexibility to explicitly parametrize known time-dependent processes that are expected to contribute to a given set of observations (e.g., co-seismic steps and post-seismic transients, secular variations, seasonal oscillations, etc.). We use cross validation to choose the regularization penalty parameter in the inversion of for the time-dependent deformation field. We demonstrate MInTS using a set of 63 ERS-1/2 and 29 Envisat interferograms for Long Valley Caldera.
Automatising the analysis of stochastic biochemical time-series
2015-01-01
Background Mathematical and computational modelling of biochemical systems has seen a lot of effort devoted to the definition and implementation of high-performance mechanistic simulation frameworks. Within these frameworks it is possible to analyse complex models under a variety of configurations, eventually selecting the best setting of, e.g., parameters for a target system. Motivation This operational pipeline relies on the ability to interpret the predictions of a model, often represented as simulation time-series. Thus, an efficient data analysis pipeline is crucial to automatise time-series analyses, bearing in mind that errors in this phase might mislead the modeller's conclusions. Results For this reason we have developed an intuitive framework-independent Python tool to automate analyses common to a variety of modelling approaches. These include assessment of useful non-trivial statistics for simulation ensembles, e.g., estimation of master equations. Intuitive and domain-independent batch scripts will allow the researcher to automatically prepare reports, thus speeding up the usual model-definition, testing and refinement pipeline. PMID:26051821
Time-series analysis of Campylobacter incidence in Switzerland.
Wei, W; Schüpbach, G; Held, L
2015-07-01
Campylobacteriosis has been the most common food-associated notifiable infectious disease in Switzerland since 1995. Contact with and ingestion of raw or undercooked broilers are considered the dominant risk factors for infection. In this study, we investigated the temporal relationship between the disease incidence in humans and the prevalence of Campylobacter in broilers in Switzerland from 2008 to 2012. We use a time-series approach to describe the pattern of the disease by incorporating seasonal effects and autocorrelation. The analysis shows that prevalence of Campylobacter in broilers, with a 2-week lag, has a significant impact on disease incidence in humans. Therefore Campylobacter cases in humans can be partly explained by contagion through broiler meat. We also found a strong autoregressive effect in human illness, and a significant increase of illness during Christmas and New Year's holidays. In a final analysis, we corrected for the sampling error of prevalence in broilers and the results gave similar conclusions.
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.
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
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…
Nonlinear times series analysis of epileptic human electroencephalogram (EEG)
NASA Astrophysics Data System (ADS)
Li, Dingzhou
The problem of seizure anticipation in patients with epilepsy has attracted significant attention in the past few years. In this paper we discuss two approaches, using methods of nonlinear time series analysis applied to scalp electrode recordings, which is able to distinguish between epochs temporally distant from and just prior to, the onset of a seizure in patients with temporal lobe epilepsy. First we describe a method involving a comparison of recordings taken from electrodes adjacent to and remote from the site of the seizure focus. In particular, we define a nonlinear quantity which we call marginal predictability. This quantity is computed using data from remote and from adjacent electrodes. We find that the difference between the marginal predictabilities computed for the remote and adjacent electrodes decreases several tens of minutes prior to seizure onset, compared to its value interictally. We also show that these difl'crcnc es of marginal predictability intervals are independent of the behavior state of the patient. Next we examine the please coherence between different electrodes both in the long-range and the short-range. When time is distant from seizure onsets ("interictally"), epileptic patients have lower long-range phase coherence in the delta (1-4Hz) and beta (18-30Hz) frequency band compared to nonepileptic subjects. When seizures approach (''preictally"), we observe an increase in phase coherence in the beta band. However, interictally there is no difference in short-range phase coherence between this cohort of patients and non-epileptic subjects. Preictally short-range phase coherence also increases in the alpha (10-13Hz) and the beta band. Next we apply the quantity marginal predictability on the phase difference time series. Such marginal predictabilities are lower in the patients than in the non-epileptic subjects. However, when seizure approaches, the former moves asymptotically towards the latter.
Time series power flow analysis for distribution connected PV generation.
Broderick, Robert Joseph; Quiroz, Jimmy Edward; Ellis, Abraham; Reno, Matthew J.; Smith, Jeff; Dugan, Roger
2013-01-01
Distributed photovoltaic (PV) projects must go through an interconnection study process before connecting to the distribution grid. These studies are intended to identify the likely impacts and mitigation alternatives. In the majority of the cases, system impacts can be ruled out or mitigation can be identified without an involved study, through a screening process or a simple supplemental review study. For some proposed projects, expensive and time-consuming interconnection studies are required. The challenges to performing the studies are twofold. First, every study scenario is potentially unique, as the studies are often highly specific to the amount of PV generation capacity that varies greatly from feeder to feeder and is often unevenly distributed along the same feeder. This can cause location-specific impacts and mitigations. The second challenge is the inherent variability in PV power output which can interact with feeder operation in complex ways, by affecting the operation of voltage regulation and protection devices. The typical simulation tools and methods in use today for distribution system planning are often not adequate to accurately assess these potential impacts. This report demonstrates how quasi-static time series (QSTS) simulation and high time-resolution data can be used to assess the potential impacts in a more comprehensive manner. The QSTS simulations are applied to a set of sample feeders with high PV deployment to illustrate the usefulness of the approach. The report describes methods that can help determine how PV affects distribution system operations. The simulation results are focused on enhancing the understanding of the underlying technical issues. The examples also highlight the steps needed to perform QSTS simulation and describe the data needed to drive the simulations. The goal of this report is to make the methodology of time series power flow analysis readily accessible to utilities and others responsible for evaluating
Wavelet analysis and scaling properties of time series
NASA Astrophysics Data System (ADS)
Manimaran, P.; Panigrahi, Prasanta K.; Parikh, Jitendra C.
2005-10-01
We propose a wavelet based method for the characterization of the scaling behavior of nonstationary time series. It makes use of the built-in ability of the wavelets for capturing the trends in a data set, in variable window sizes. Discrete wavelets from the Daubechies family are used to illustrate the efficacy of this procedure. After studying binomial multifractal time series with the present and earlier approaches of detrending for comparison, we analyze the time series of averaged spin density in the 2D Ising model at the critical temperature, along with several experimental data sets possessing multifractal behavior.
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.
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.
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.
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 offshore-wind-wave groupiness
Liang, H.B.
1988-01-01
This research is to applies basic time-series-analysis techniques on the complex envelope function where the study of the offshore-wind-wave groupiness is a relevant interest. In constructing the complex envelope function, a phase-unwrapping technique is integrated into the algorithm for estimating the carrier frequency and preserving the phase information for further studies. The Gaussian random wave model forms the basis of the wave-group statistics by the envelope-amplitude crossings. Good agreement between the theory and the analysis of field records is found. Other linear models, such as the individual-waves approach and the energy approach, are compared to the envelope approach by analyzing the same set of records. It is found that the character of the filter used in each approach dominates the wave-group statistics. Analyses indicate that the deep offshore wind waves are weakly nonlinear and the Gaussian random assumption remains appropriate for describing the sea state. Wave groups statistics derived from the Gaussian random wave model thus become applicable.
Interglacial climate dynamics and advanced time series analysis
NASA Astrophysics Data System (ADS)
Mudelsee, Manfred; Bermejo, Miguel; Köhler, Peter; Lohmann, Gerrit
2013-04-01
Studying the climate dynamics of past interglacials (IGs) helps to better assess the anthropogenically influenced dynamics of the current IG, the Holocene. We select the IG portions from the EPICA Dome C ice core archive, which covers the past 800 ka, to apply methods of statistical time series analysis (Mudelsee 2010). The analysed variables are deuterium/H (indicating temperature) (Jouzel et al. 2007), greenhouse gases (Siegenthaler et al. 2005, Loulergue et al. 2008, L¨ü thi et al. 2008) and a model-co-derived climate radiative forcing (Köhler et al. 2010). We select additionally high-resolution sea-surface-temperature records from the marine sedimentary archive. The first statistical method, persistence time estimation (Mudelsee 2002) lets us infer the 'climate memory' property of IGs. Second, linear regression informs about long-term climate trends during IGs. Third, ramp function regression (Mudelsee 2000) is adapted to look on abrupt climate changes during IGs. We compare the Holocene with previous IGs in terms of these mathematical approaches, interprete results in a climate context, assess uncertainties and the requirements to data from old IGs for yielding results of 'acceptable' accuracy. This work receives financial support from the Deutsche Forschungsgemeinschaft (Project ClimSens within the DFG Research Priority Program INTERDYNAMIK) and the European Commission (Marie Curie Initial Training Network LINC, No. 289447, within the 7th Framework Programme). References Jouzel J, Masson-Delmotte V, Cattani O, Dreyfus G, Falourd S, Hoffmann G, Minster B, Nouet J, Barnola JM, Chappellaz J, Fischer H, Gallet JC, Johnsen S, Leuenberger M, Loulergue L, Luethi D, Oerter H, Parrenin F, Raisbeck G, Raynaud D, Schilt A, Schwander J, Selmo E, Souchez R, Spahni R, Stauffer B, Steffensen JP, Stenni B, Stocker TF, Tison JL, Werner M, Wolff EW (2007) Orbital and millennial Antarctic climate variability over the past 800,000 years. Science 317:793. Köhler P, Bintanja R
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.
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.
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.
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.
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.
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.
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.
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)
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…
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.
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.
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.
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
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
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.
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.
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.
On the Interpretation of Running Trends as Summary Statistics for Time Series Analysis
NASA Astrophysics Data System (ADS)
Vigo, Isabel M.; Trottini, Mario; Belda, Santiago
2016-04-01
In recent years, running trends analysis (RTA) has been widely used in climate applied research as summary statistics for time series analysis. There is no doubt that RTA might be a useful descriptive tool, but despite its general use in applied research, precisely what it reveals about the underlying time series is unclear and, as a result, its interpretation is unclear too. This work contributes to such interpretation in two ways: 1) an explicit formula is obtained for the set of time series with a given series of running trends, making it possible to show that running trends, alone, perform very poorly as summary statistics for time series analysis; and 2) an equivalence is established between RTA and the estimation of a (possibly nonlinear) trend component of the underlying time series using a weighted moving average filter. Such equivalence provides a solid ground for RTA implementation and interpretation/validation.
NASA Astrophysics Data System (ADS)
Liu, Bin; Dai, Wujiao; Peng, Wei; Meng, Xiaolin
2015-11-01
GPS has been widely used in the field of geodesy and geodynamics thanks to its technology development and the improvement of positioning accuracy. A time series observed by GPS in vertical direction usually contains tectonic signals, non-tectonic signals, residual atmospheric delay, measurement noise, etc. Analyzing these information is the basis of crustal deformation research. Furthermore, analyzing the GPS time series and extracting the non-tectonic information are helpful to study the effect of various geophysical events. Principal component analysis (PCA) is an effective tool for spatiotemporal filtering and GPS time series analysis. But as it is unable to extract statistically independent components, PCA is unfavorable for achieving the implicit information in time series. Independent component analysis (ICA) is a statistical method of blind source separation (BSS) and can separate original signals from mixed observations. In this paper, ICA is used as a spatiotemporal filtering method to analyze the spatial and temporal features of vertical GPS coordinate time series in the UK and Sichuan-Yunnan region in China. Meanwhile, the contributions from atmospheric and soil moisture mass loading are evaluated. The analysis of the relevance between the independent components and mass loading with their spatial distribution shows that the signals extracted by ICA have a strong correlation with the non-tectonic deformation, indicating that ICA has a better performance in spatiotemporal analysis.
A novel water quality data analysis framework based on time-series data mining.
Deng, Weihui; Wang, Guoyin
2017-03-18
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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
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.
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 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)
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.
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…
Time Series in Education: The Analysis of Daily Attendance in Two High Schools
ERIC Educational Resources Information Center
Koopmans, Matthijs
2011-01-01
This presentation discusses the use of a time series approach to the analysis of daily attendance in two urban high schools over the course of one school year (2009-10). After establishing that the series for both schools were stationary, they were examined for moving average processes, autoregression, seasonal dependencies (weekly cycles),…
NASA Astrophysics Data System (ADS)
Donges, Jonathan F.; Heitzig, Jobst; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik V.; Marwan, Norbert; Dijkstra, Henk A.; Kurths, Jürgen
2015-11-01
We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics, or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis, recurrence networks, visibility graphs, and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology.
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.
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.
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].
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.
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.
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.
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
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.
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.
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.
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.
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
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.
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.
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.
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
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.
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.
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.
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.
Dequéant, Mary-Lee; Fagegaltier, Delphine; Hu, Yanhui; Spirohn, Kerstin; Simcox, Amanda; Hannon, Gregory J.; Perrimon, Norbert
2015-01-01
The use of time series profiling to identify groups of functionally related genes (synexpression groups) is a powerful approach for the discovery of gene function. Here we apply this strategy during RasV12 immortalization of Drosophila embryonic cells, a phenomenon not well characterized. Using high-resolution transcriptional time-series datasets, we generated a gene network based on temporal expression profile similarities. This analysis revealed that common immortalized cells are related to adult muscle precursors (AMPs), a stem cell-like population contributing to adult muscles and sharing properties with vertebrate satellite cells. Remarkably, the immortalized cells retained the capacity for myogenic differentiation when treated with the steroid hormone ecdysone. Further, we validated in vivo the transcription factor CG9650, the ortholog of mammalian Bcl11a/b, as a regulator of AMP proliferation predicted by our analysis. Our study demonstrates the power of time series synexpression analysis to characterize Drosophila embryonic progenitor lines and identify stem/progenitor cell regulators. PMID:26438832
Detection of chaos: New approach to atmospheric pollen time-series analysis
NASA Astrophysics Data System (ADS)
Bianchi, M. M.; Arizmendi, C. M.; Sanchez, J. R.
1992-09-01
Pollen and spores are biological particles that are ubiquitous to the atmosphere and are pathologically significant, causing plant diseases and inhalant allergies. One of the main objectives of aerobiological surveys is forecasting. Prediction models are required in order to apply aerobiological knowledge to medical or agricultural practice; a necessary condition of these models is not to be chaotic. The existence of chaos is detected through the analysis of a time series. The time series comprises hourly counts of atmospheric pollen grains obtained using a Burkard spore trap from 1987 to 1989 at Mar del Plata. Abraham's method to obtain the correlation dimension was applied. A low and fractal dimension shows chaotic dynamics. The predictability of models for atomspheric pollen forecasting is discussed.
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.
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.
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.
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
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.
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.
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
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
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.
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.
Li, Shuying; Zhuang, Jun; Shen, Shifei
2016-08-23
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.
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.
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.
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
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
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.
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.
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
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
Time series analysis of Adaptive Optics wave-front sensor telemetry data
Poyneer, L A; Palmer, D
2004-03-22
Time series analysis techniques are applied to wave-front sensor telemetry data from the Lick Adaptive Optics System. For 28 fully-illuminated subapertures, telemetry data of 4096 consecutive slope estimates for each subaperture are available. The primary problem is performance comparison of alternative wave-front sensing algorithms. Using direct comparison of data in open loop and closed-loop trials, we analyze algorithm performance in terms of gain, noise and residual power. We also explore the benefits of multi-input Wiener filtering and analyze the open-loop and closed-loop spatial correlations of the sensor measurements.
NASA Astrophysics Data System (ADS)
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.
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.
Online Time Series Analysis of Land Products over Asia Monsoon Region via Giovanni
NASA Technical Reports Server (NTRS)
Shen, Suhung; Leptoukh, Gregory G.; Gerasimov, Irina
2011-01-01
Time series analysis is critical to the study of land cover/land use changes and climate. Time series studies at local-to-regional scales require higher spatial resolution, such as 1km or less, data. MODIS land products of 250m to 1km resolution enable such studies. However, such MODIS land data files are distributed in 10ox10o tiles, due to large data volumes. Conducting a time series study requires downloading all tiles that include the study area for the time period of interest, and mosaicking the tiles spatially. This can be an extremely time-consuming process. In support of the Monsoon Asia Integrated Regional Study (MAIRS) program, NASA GES DISC (Goddard Earth Sciences Data and Information Services Center) has processed MODIS land products at 1 km resolution over the Asia monsoon region (0o-60oN, 60o-150oE) with a common data structure and format. The processed data have been integrated into the Giovanni system (Goddard Interactive Online Visualization ANd aNalysis Infrastructure) that enables users to explore, analyze, and download data over an area and time period of interest easily. Currently, the following regional MODIS land products are available in Giovanni: 8-day 1km land surface temperature and active fire, monthly 1km vegetation index, and yearly 0.05o, 500m land cover types. More data will be added in the near future. By combining atmospheric and oceanic data products in the Giovanni system, it is possible to do further analyses of environmental and climate changes associated with the land, ocean, and atmosphere. This presentation demonstrates exploring land products in the Giovanni system with sample case scenarios.
Time Series Analysis of Onchocerciasis Data from Mexico: A Trend towards Elimination
Pérez-Rodríguez, Miguel A.; Adeleke, Monsuru A.; Orozco-Algarra, María E.; Arrendondo-Jiménez, Juan I.; Guo, Xianwu
2013-01-01
Background In Latin America, there are 13 geographically isolated endemic foci distributed among Mexico, Guatemala, Colombia, Venezuela, Brazil and Ecuador. The communities of the three endemic foci found within Mexico have been receiving ivermectin treatment since 1989. In this study, we predicted the trend of occurrence of cases in Mexico by applying time series analysis to monthly onchocerciasis data reported by the Mexican Secretariat of Health between 1988 and 2011 using the software R. Results A total of 15,584 cases were reported in Mexico from 1988 to 2011. The data of onchocerciasis cases are mainly from the main endemic foci of Chiapas and Oaxaca. The last case in Oaxaca was reported in 1998, but new cases were reported in the Chiapas foci up to 2011. Time series analysis performed for the foci in Mexico showed a decreasing trend of the disease over time. The best-fitted models with the smallest Akaike Information Criterion (AIC) were Auto-Regressive Integrated Moving Average (ARIMA) models, which were used to predict the tendency of onchocerciasis cases for two years ahead. According to the ARIMA models predictions, the cases in very low number (below 1) are expected for the disease between 2012 and 2013 in Chiapas, the last endemic region in Mexico. Conclusion The endemic regions of Mexico evolved from high onchocerciasis-endemic states to the interruption of transmission due to the strategies followed by the MSH, based on treatment with ivermectin. The extremely low level of expected cases as predicted by ARIMA models for the next two years suggest that the onchocerciasis is being eliminated in Mexico. To our knowledge, it is the first study utilizing time series for predicting case dynamics of onchocerciasis, which could be used as a benchmark during monitoring and post-treatment surveillance. PMID:23459370
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.
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.
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.
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
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
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.
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.
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.
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.
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
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.
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.
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
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.
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.
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
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
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.
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
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.
Nonlinear Analysis on Cross-Correlation of Financial Time Series by Continuum Percolation System
NASA Astrophysics Data System (ADS)
Niu, Hongli; Wang, Jun
We establish a financial price process by continuum percolation system, in which we attribute price fluctuations to the investors’ attitudes towards the financial market, and consider the clusters in continuum percolation as the investors share the same investment opinion. We investigate the cross-correlations in two return time series, and analyze the multifractal behaviors in this relationship. Further, we study the corresponding behaviors for the real stock indexes of SSE and HSI as well as the liquid stocks pair of SPD and PAB by comparison. To quantify the multifractality in cross-correlation relationship, we employ multifractal detrended cross-correlation analysis method to perform an empirical research for the simulation data and the real markets data.
The 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.
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.
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.
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.
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.
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.
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.
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.
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
NASA Astrophysics Data System (ADS)
Gualandi, Adriano; Serpelloni, Enrico; Elina Belardinelli, Maria; Bonafede, Maurizio; Pezzo, Giuseppe; Tolomei, Cristiano
2015-04-01
A critical point in the analysis of ground displacement time series, as those measured by modern space geodetic techniques (primarly continuous GPS/GNSS and InSAR) is the development of data driven methods that allow to discern and characterize the different sources that generate the observed displacements. A widely used multivariate statistical technique is the Principal Component Analysis (PCA), which allows to reduce the dimensionality of the data space maintaining most of the variance of the dataset explained. It reproduces the original data using a limited number of Principal Components, but it also shows some deficiencies, since PCA does not perform well in finding the solution to the so-called Blind Source Separation (BSS) problem. The recovering and separation of the different sources that generate the observed ground deformation is a fundamental task in order to provide a physical meaning to the possible different sources. PCA fails in the BSS problem since it looks for a new Euclidean space where the projected data are uncorrelated. Usually, the uncorrelation condition is not strong enough and it has been proven that the BSS problem can be tackled imposing on the components to be independent. The Independent Component Analysis (ICA) is, in fact, another popular technique adopted to approach this problem, and it can be used in all those fields where PCA is also applied. An ICA approach enables us to explain the displacement time series imposing a fewer number of constraints on the model, and to reveal anomalies in the data such as transient deformation signals. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. To work around this problem, we use a variational bayesian ICA (vbICA) method, which models the probability density function (pdf) of each source signal using a mix of Gaussian distributions. This technique allows for more flexibility in the description of the pdf of the sources
Time series analysis of the developed financial markets' integration using visibility graphs
NASA Astrophysics Data System (ADS)
Zhuang, Enyu; Small, Michael; Feng, Gang
2014-09-01
A time series representing the developed financial markets' segmentation from 1973 to 2012 is studied. The time series reveals an obvious market integration trend. To further uncover the features of this time series, we divide it into seven windows and generate seven visibility graphs. The measuring capabilities of the visibility graphs provide means to quantitatively analyze the original time series. It is found that the important historical incidents that influenced market integration coincide with variations in the measured graphical node degree. Through the measure of neighborhood span, the frequencies of the historical incidents are disclosed. Moreover, it is also found that large "cycles" and significant noise in the time series are linked to large and small communities in the generated visibility graphs. For large cycles, how historical incidents significantly affected market integration is distinguished by density and compactness of the corresponding communities.
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
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.
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
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.
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.
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
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
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
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.
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.
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 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.
NASA Astrophysics Data System (ADS)
Bock, Y.; Crowell, B. W.; Dong, D.; Fang, P.; Kedar, S.; Liu, Z.; Moore, A. W.; Owen, S. E.; Prawirodirdjo, L. M.; Squibb, M. B.; Webb, F.
2011-12-01
As part of a NASA MEaSUREs project and its contribution to EarthScope, we are producing a combined 24-hour position time series for more than 1000 stations in Western North America based on independent analyses of continuous GPS data at JPL (using GIPSY software) and at SIO (using GAMIT software), using the SOPAC archive as a common source of metadata. Included are all EarthScope/PBO stations as well as stations from other networks still active (SCIGN, BARD and PANGA), and pre-PBO era data some already two decades old. The time series are appended weekly and the entire data set is filtered once a week using a modified principle component analysis (PCA) algorithm using the st_filter software. Both the unfiltered and filtered data undergo a time series analysis with the analyze_tseri software. All relevant time series are available through NASA's GPS Explorer data portal and its interactive Java-based time series utility. After a comprehensive process of re-analysis and quality control, we have evaluated the time series for transient deformation, that is, time series that deviate from linear behavior due to coseismic and postseismic deformation, slow slip events, volcanic events, and strain anomalies. In addition, we have observed non-tectonic effects from hydrologic, magmatic and anthropogenic sources which are manifested primarily in the vertical but sometimes bleed over into the horizontal and make tectonic interpretation and transient detection difficult. Other sources of anomalous deformation are due to dam deformation such as Diamond Valley Lake an important water reservoir in Southern California, and structural deformation including the Harvest oil platform used by NASA for altimeter calibrations. We present a compendium of transient deformation discovered in our time series analysis, including duration, geographical extent and magnitudes.
McKenna, Thomas M; Bawa, Gagandeep; Kumar, Kamal; Reifman, Jaques
2007-04-01
The physiology analysis system (PAS) was developed as a resource to support the efficient warehousing, management, and analysis of physiology data, particularly, continuous time-series data that may be extensive, of variable quality, and distributed across many files. The PAS incorporates time-series data collected by many types of data-acquisition devices, and it is designed to free users from data management burdens. This Web-based system allows both discrete (attribute) and time-series (ordered) data to be manipulated, visualized, and analyzed via a client's Web browser. All processes occur on a server, so that the client does not have to download data or any application programs, and the PAS is independent of the client's computer operating system. The PAS contains a library of functions, written in different computer languages that the client can add to and use to perform specific data operations. Functions from the library are sequentially inserted into a function chain-based logical structure to construct sophisticated data operators from simple function building blocks, affording ad hoc query and analysis of time-series data. These features support advanced mining of physiology data.
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
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.
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
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
Perpinan, O.; Lorenzo, E.
2011-01-15
The irradiance fluctuations and the subsequent variability of the power output of a PV system are analysed with some mathematical tools based on the wavelet transform. It can be shown that the irradiance and power time series are nonstationary process whose behaviour resembles that of a long memory process. Besides, the long memory spectral exponent {alpha} is a useful indicator of the fluctuation level of a irradiance time series. On the other side, a time series of global irradiance on the horizontal plane can be simulated by means of the wavestrapping technique on the clearness index and the fluctuation behaviour of this simulated time series correctly resembles the original series. Moreover, a time series of global irradiance on the inclined plane can be simulated with the wavestrapping procedure applied over a signal previously detrended by a partial reconstruction with a wavelet multiresolution analysis, and, once again, the fluctuation behaviour of this simulated time series is correct. This procedure is a suitable tool for the simulation of irradiance incident over a group of distant PV plants. Finally, a wavelet variance analysis and the long memory spectral exponent show that a PV plant behaves as a low-pass filter. (author)
NASA Astrophysics Data System (ADS)
Wesfreid, Eva; Billat, Véronique
2009-02-01
Data power law scaling behavior is observed in many fields. Velocity of fully developed turbulent flow, telecommunication traffic in networks, financial time series are some examples among many others. The goal of the present contribution is to show the scaling behavior of physiological time series in marathon races using wavelet leaders and the Detrended Fluctuation Analysis. Marathon race is an exhausting exercise, it is referenced as being a model for studying the limits of human ambulatory abilities. We analyzed the athlete's heart rate and speed time series recorded simultaneously. We find that the heart cost time series, number of heart beats per meter, increases with the fatigue appearing during the marathon race, its tendency grows in the second half of the race for all athletes. For most physiological time series, we observed a concave behavior of the wavelet leaders scaling exponents which suggests a multifractal behavior. Otherwise, the Detrended Fluctuation Analysis shows short and long range time-scale power law exponents with the same break point for each physiological time series and each athlete. The short range time-scale exponent increases with fatigue in most physiological signals.
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.
Forecasting Container Throughput at the Doraleh Port in Djibouti through Time Series Analysis
NASA Astrophysics Data System (ADS)
Mohamed Ismael, Hawa; Vandyck, George Kobina
The Doraleh Container Terminal (DCT) located in Djibouti has been noted as the most technologically advanced container terminal on the African continent. DCT's strategic location at the crossroads of the main shipping lanes connecting Asia, Africa and Europe put it in a unique position to provide important shipping services to vessels plying that route. This paper aims to forecast container throughput through the Doraleh Container Port in Djibouti by Time Series Analysis. A selection of univariate forecasting models has been used, namely Triple Exponential Smoothing Model, Grey Model and Linear Regression Model. By utilizing the above three models and their combination, the forecast of container throughput through the Doraleh port was realized. A comparison of the different forecasting results of the three models, in addition to the combination forecast is then undertaken, based on commonly used evaluation criteria Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE). The study found that the Linear Regression forecasting Model was the best prediction method for forecasting the container throughput, since its forecast error was the least. Based on the regression model, a ten (10) year forecast for container throughput at DCT has been made.
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.
Assessing coal-mine safety regulation: A pooled time-series analysis
Chun Youngpyoung.
1991-01-01
This study attempts to assess the independent, relative, and conjoint effects of four types of variables on coal-mine safety: administrative (mine inspections, mine investigations, and mine safety grants); political (state party competition, gubernatorial party affiliation, and deregulation); economic (state per-capita income and unemployment rates); task-related (mine size, technology, and type of mining), and state dummy variables. Trend, Pearson correlation, and pooled time-series analyses are performed on fatal and nonfatal injury rates reported in 25 coal-producing states during the 1975-1985 time period. These are then interpreted in light of three competing theories of regulation: capture, nonmarket failure, and threshold. Analysis reveals: (1) distinctions in the total explanatory power of the model across different types of injuries, as well as across presidential administrations; (2) a consistently more powerful impact on safety of informational implementation tools (safety education grants) over command-and-control approaches (inspections and investigations) or political variables; and (3) limited, albeit conjectural, support for a threshold theory of regulation in the coal mine safety arena.
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.
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.
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 has produced the longest imagery record of forest, we will explore the dynamics of tropical forest with both medium footprint lidar and landsat images. LVIS, a medium footprint airborne scanning lidar, has surveyed this area in March of 1998 and 2005. A highly automated algorithm, vegetation change tracker (VCT) has been developed for reconstructing recent forest disturbance history starting from 1984 using Landsat time series stacks (LTSS).Need to discuss what you will do, what are the expected results and their significances. We will try to establish empirical relationship between Lidar and landsat images to analysis tropical forest dynamics from 1984 to 2005.
Rotavirus infections and climate variability in Dhaka, Bangladesh: a time-series analysis.
Hashizume, M; Armstrong, B; Wagatsuma, Y; Faruque, A S G; Hayashi, T; Sack, D A
2008-09-01
Attempts to explain the clear seasonality of rotavirus infections have been made by relating disease incidence to climate factors; however, few studies have disentangled the effects of weather from other factors that might cause seasonality. We investigated the relationships between hospital visits for rotavirus diarrhoea and temperature, humidity and river level, in Dhaka, Bangladesh, using time-series analysis adjusting for other confounding seasonal factors. There was strong evidence for an increase in rotavirus diarrhoea at high temperatures, by 40.2% for each 1 degrees C increase above a threshold (29 degrees C). Relative humidity had a linear inverse relationship with the number of cases of rotavirus diarrhoea. River level, above a threshold (4.8 m), was associated with an increase in cases of rotavirus diarrhoea, by 5.5% per 10-cm river-level rise. Our findings provide evidence that factors associated with high temperature, low humidity and high river-level increase the incidence of rotavirus diarrhoea in Dhaka.
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
The relationship between pay day and violent death in Guatemala: a time series analysis
Ramírez, Dorian E; Branas, Charles C; Richmond, Therese S; Bream, Kent; Xie, Dawei; Velásquez-Tohom, Magda; Wiebe, Douglas J
2016-01-01
Objective To assess if violent deaths were associated with pay days in Guatemala. Design Interrupted time series analysis. Setting Guatemalan national autopsy databases. Participants Daily violence-related autopsy data for 22 418 decedents from 2009 to 2012. Data were provided by the Guatemalan National Institute of Forensic Sciences. Multiple pay-day lags and other important days such as holidays were tested. Outcome measures Absolute and relative estimates of excess violent deaths on pay days and holidays. Results The occurrence of violent deaths was not associated with pay days. However, a significant association was observed for national holidays, and this association was more pronounced when national holidays and pay days occurred simultaneously. This effect was observed mainly in males, who constituted the vast majority of violent deaths in Guatemala. An estimated 112 (coefficient=3.12; 95% CI 2.15 to 4.08; p<0.01) more male violent deaths occurred on holidays than were expected. An estimated 121 (coefficient=4.64; 95% CI 3.41 to 5.88; p<0.01) more male violent deaths than expected occurred on holidays that coincided with the first 2 days following a pay day. Conclusions Men in Guatemala experience violent deaths at an elevated rate when pay days coincide with national holidays. Efforts to be better prepared for violence during national holidays and to prevent violent deaths by rescheduling pay days when these days co-occur with national holidays should be considered. PMID:27697828
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
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.
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.
Advanced SuperDARN meteor wind observations based on raw time series analysis technique
NASA Astrophysics Data System (ADS)
Tsutsumi, M.; Yukimatu, A. S.; Holdsworth, D. A.; Lester, M.
2009-04-01
The meteor observation technique based on SuperDARN raw time series analysis has been upgraded. This technique extracts meteor information as biproducts and does not degrade the quality of normal SuperDARN operations. In the upgrade the radar operating system (RADOPS) has been modified so that it can oversample every 15 km during the normal operations, which have a range resolution of 45 km. As an alternative method for better range determination a frequency domain interferometry (FDI) capability was also coded in RADOPS, where the operating radio frequency can be changed every pulse sequence. Test observations were conducted using the CUTLASS Iceland East and Finland radars, where oversampling and FDI operation (two frequencies separated by 3 kHz) were simultaneously carried out. Meteor ranges obtained in both ranging techniques agreed very well. The ranges were then combined with the interferometer data to estimate meteor echo reflection heights. Although there were still some ambiguities in the arrival angles of echoes because of the rather long antenna spacing of the interferometers, the heights and arrival angles of most of meteor echoes were more accurately determined than previously. Wind velocities were successfully estimated over the height range of 84 to 110 km. The FDI technique developed here can be further applied to the common SuperDARN operation, and study of fine horizontal structures of F region plasma irregularities is expected in the future.
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.
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.
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.
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
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
NASA Astrophysics Data System (ADS)
Ku, Taeyun; Lee, Jungsul; Choi, Chulhee
2010-02-01
Measurement of cerebral perfusion is important for study of various brain disorders such as stroke, epilepsy, and vascular dementia; however, efficient and convenient methods which can provide quantitative information about cerebral blood flow are not developed. Here we propose an optical imaging method using time-series analysis of dynamics of indocyanine green (ICG) fluorescence to generate cerebral blood flow maps. In scalp-removed mice, ICG was injected intravenously, and 740nm LED light was illuminated for fluorescence emission signals around 820nm acquired by cooled-CCD. Time-lapse 2-dimensional images were analyzed by custom-built software, and the maximal time point of fluorescent influx in each pixel was processed as a blood flow-related parameter. The generated map exactly reflected the shape of the brain without any interference of the skull, the dura mater, and other soft tissues. This method may be further applicable for study of other disease models in which the cerebral hemodynamics is changed either acutely or chronically.
The Terror Attacks of 9/11 and Suicides in Germany: A Time Series Analysis.
Medenwald, Daniel
2016-04-01
Data on the effect of the September 11, 2001 (9/11) terror attacks on suicide rates remain inconclusive. Reportedly, even people located far from the attack site have considerable potential for personalizing the events that occurred on 9/11. Durkheim's theory states that suicides decrease during wartime; thus, a decline in suicides might have been expected after 9/11. We conducted a time series analysis of 164,136 officially recorded suicides in Germany between 1995 and 2009 using the algorithm introduced by Box and Jenkins. Compared with the average death rate, we observed no relevant change in the suicide rate of either sex after 9/11. Our estimates of an excess of suicides approached the null effect value on and within a 7-day period after 9/11, which also held when subsamples of deaths in urban or rural settings were examined. No evidence of Durkheim's theory attributable to the 9/11attacks was found in this sample.
Discrete Fourier analysis of ultrasound RF time series for detection of prostate cancer.
Moradi, M; Mousavi, P; Siemens, D R; Sauerbrei, E E; Isotalo, P; Boag, A; Abolmaesumi, P
2007-01-01
In this paper, we demonstrate that a set of six features extracted from the discrete Fourier transform of ultrasound Radio-Frequency (RF) time series can be used to detect prostate cancer with high sensitivity and specificity. Ultrasound RF time series refer to a series of echoes received from one spatial location of tissue while the imaging probe and the tissue are fixed in position. Our previous investigations have shown that at least one feature, fractal dimension, of these signals demonstrates strong correlation with the tissue microstructure. In the current paper, six new features that represent the frequency spectrum of the RF time series have been used, in conjunction with a neural network classification approach, to detect prostate cancer in regions of tissue as small as 0.03 cm2. Based on pathology results used as gold standard, we have acquired mean accuracy of 91%, mean sensitivity of 92% and mean specificity of 90% on seven human prostates.
NASA Technical Reports Server (NTRS)
Lenz, R. W.; Mckeever, B.
1976-01-01
The Air Force Flight Test Center (AFFTC) flight flutter facility is described. Concepts of using a minicomputer-based time series analyzer and a modal analysis software package for flight flutter testing are examined. The results of several evaluations of the software package are given. The reasons for employing a minimum phase concept in analyzing response only signals are discussed. The use of a Laplace algorithm is shown to be effective for the modal analysis of time histories in flutter testing. Sample results from models and flight tests are provided. The limitations inherent in time series analysis methods are discussed, and the need for effective noise reduction techniques is noted. The use of digital time series analysis techniques in flutter testing is shown to be fast, accurate, and cost effective.
GPS position time-series analysis based on asymptotic normality of M-estimation
NASA Astrophysics Data System (ADS)
Khodabandeh, A.; Amiri-Simkooei, A. R.; Sharifi, M. A.
2012-01-01
The efficacy of robust M-estimators is a well-known issue when dealing with observational blunders. When the number of observations is considerably large—long time series for instance—one can take advantage of the asymptotic normality of the M-estimation and compute reasonable estimates for the unknown parameters of interest. A few leading M-estimators have been employed to identify the most likely functional model for GPS coordinate time series. This includes the simultaneous detection of periodic patterns and offsets in the GPS time series. Estimates of white noise, flicker noise, and random walk noise components are also achieved using the robust M-estimators of (co)variance components, developed in the framework of the least-squares variance component estimation (LS-VCE) theory. The method allows one to compute confidence interval for the (co)variance components in asymptotic sense. Simulated time series using white noise plus flicker noise show that the estimates of random walk noise fluctuate more than those of flicker noise for different M-estimators. This is because random walk noise is not an appropriate noise structure for the series. The same phenomenon is observed using the results of real GPS time series, which implies that the combination of white plus flicker noise is well described for GPS time series. Some of the estimated noise components of LS-VCE differ significantly from those of other M- estimators. This reveals that there are a large number of outliers in the series. This conclusion is also affirmed by performing the statistical tests, which detect (large) parts of the outliers but can also leave parts to be undetected.
Performance consistency of international soccer teams in euro 2012: a time series analysis.
Shafizadeh, Mohsen; Taylor, Marc; Peñas, Carlos Lago
2013-01-01
The purpose of this study was to examine the consistency of performance in successive matches for international soccer teams from Europe which qualified for the quarter final stage of EURO 2012 in Poland and Ukraine. The eight teams that reached the quarter final stage and beyond were the sample teams for this time series analysis. The autocorrelation and cross-correlation functions were used to analyze the consistency of play and its association with the result of match in sixteen performance indicators of each team. The results of autocorrelation function showed that based on the number of consistent performance indicators, Spain and Italy demonstrated more consistency in successive matches in relation to other teams. This appears intuitive given that Spain played Italy in the final. However, it is arguable that other teams played at a higher performance levels at various parts of the competition, as opposed to performing consistently throughout the tournament. The results of the cross-correlation analysis showed that in relation to goal-related indicators, these had higher associations with the match results of Spain and France. In relation to the offensive-related indicators, France, England, Portugal, Greece, Czech Republic and Spain showed a positive correlation with the match result. In relation to the defensive-related indicators, France, England, Greece and Portugal showed a positive correlation with match results. In conclusion, in an international soccer tournament, the successful teams displayed a greater degree of performance consistency across all indicators in comparison to their competitors who occasionally would show higher levels of performance in individual games, yet not consistently across the overall tournament. The authors therefore conclude that performance consistency is more significant in international tournament soccer, versus occasionally excelling in some metrics and indicators in particular games.
Increase in dust storm related PM10 concentrations: A time series analysis of 2001-2015.
Krasnov, Helena; Katra, Itzhak; Friger, Michael
2016-06-01
Over the last decades, changes in dust storms characteristics have been observed in different parts of the world. The changing frequency of dust storms in the southeastern Mediterranean has led to growing concern regarding atmospheric PM10 levels. A classic time series additive model was used in order to describe and evaluate the changes in PM10 concentrations during dust storm days in different cities in Israel, which is located at the margins of the global dust belt. The analysis revealed variations in the number of dust events and PM10 concentrations during 2001-2015. A significant increase in PM10 concentrations was identified since 2009 in the arid city of Beer Sheva, southern Israel. Average PM10 concentrations during dust days before 2009 were 406, 312, and 364 μg m(-3) (median 337, 269,302) for Beer Sheva, Rehovot (central Israel) and Modi'in (eastern Israel), respectively. After 2009 the average concentrations in these cities during dust storms were 536, 466, and 428 μg m(-3) (median 382, 335, 338), respectively. Regression analysis revealed associations between PM10 variations and seasonality, wind speed, as well as relative humidity. The trends and periodicity are stronger in the southern part of Israel, where higher PM10 concentrations are found. Since 2009 dust events became more extreme with much higher daily and hourly levels. The findings demonstrate that in the arid area variations of dust storms can be quantified easier through PM10 levels over a relatively short time scale of several years.
Non-Linear Time Series Analysis of Dissolved Oxygen in Five Diverse Aquatic Environments
NASA Astrophysics Data System (ADS)
Simpson, K. E.; Barton, C. C.; Smigelski, J. R.; Tebbens, S. F.
2008-12-01
Temporal variations in the concentration of Dissolved oxygen (DO) can create catastrophic conditions for organisms that rely on aerobic metabolic processes for survival. Dissolved oxygen (DO) is an aquatic parameter whose concentration is controlled by physical, biological, and chemical processes. The concentration of DO in an aquatic system is important to organisms that rely on aerobic metabolic processes for survival. A power-spectral-density analysis of time series of DO concentration is used to quantify persistence (the degree of internal correlation) over durations of 3 months to 19 years. The interval between data points was either 15 minutes or one hour. The data are from ten different water bodies throughout the United States. Four of these sites are large, slow moving bodies of water including three estuaries: Chesapeake Bay (Virginia), Winyah Bay (North Carolina) and Elkhorn Slough (California); and one reservoir: the Cheney Reservoir in Kansas. The other six sites are small, fast moving water bodies. They included four rivers: Christina River (Delaware), St. Croix River (Maine), Ramapo River (New Jersey), and Passaic River, New Jersey; one stream: Green Pond Brook (New Jersey); and one man-made channel: Reynolds Channel (New York). The analysis quantifies persistence as the power scaling exponent (β), which for all ten water bodies β ranges between 1.2 and 1.6 meaning that the signal is persistent and non-stationary. Rivers and streams, exhibit higher β-values of 1.5 < β<1.6 (greater persistence) than estuaries and lakes, which have β-values of 1.2< β <1.4t.
NASA Astrophysics Data System (ADS)
Tirabassi, Giulio; Masoller, Cristina
2016-07-01
Many natural systems can be represented by complex networks of dynamical units with modular structure in the form of communities of densely interconnected nodes. Unraveling this community structure from observed data requires the development of appropriate tools, particularly when the nodes are embedded in a regular space grid and the datasets are short and noisy. Here we propose two methods to identify communities, and validate them with the analysis of climate datasets recorded at a regular grid of geographical locations covering the Earth surface. By identifying mutual lags among time-series recorded at different grid points, and by applying symbolic time-series analysis, we are able to extract meaningful regional communities, which can be interpreted in terms of large-scale climate phenomena. The methods proposed here are valuable tools for the study of other systems represented by networks of dynamical units, allowing the identification of communities, through time-series analysis of the observed output signals.
Tirabassi, Giulio; Masoller, Cristina
2016-01-01
Many natural systems can be represented by complex networks of dynamical units with modular structure in the form of communities of densely interconnected nodes. Unraveling this community structure from observed data requires the development of appropriate tools, particularly when the nodes are embedded in a regular space grid and the datasets are short and noisy. Here we propose two methods to identify communities, and validate them with the analysis of climate datasets recorded at a regular grid of geographical locations covering the Earth surface. By identifying mutual lags among time-series recorded at different grid points, and by applying symbolic time-series analysis, we are able to extract meaningful regional communities, which can be interpreted in terms of large-scale climate phenomena. The methods proposed here are valuable tools for the study of other systems represented by networks of dynamical units, allowing the identification of communities, through time-series analysis of the observed output signals. PMID:27406342
Time series analysis of Mexico City subsidence constrained by radar interferometry
NASA Astrophysics Data System (ADS)
Doin, Marie-Pierre; Lopez-Quiroz, Penelope; Yan, Yajing; Bascou, Pascale; Pinel, Virginie
2010-05-01
unwrapping errors for each pixel and show that they are strongly decreased by iterations in the unwrapping process. (3) 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. We also use the Gamma-PS software on the same data set. The phase differences are unwrapped within small patches with respect to a reference point chosen in each patch, whose phase is in turn unwrapped relatively to a reference point common for the whole area of interest. After removing the modelled contribution of the linear displacement rate and DEM error, some residual interferograms, presenting unwrapping errors because of strong residual orbital ramp or atmospheric phase screen, are spatially unwrapped by a minimum cost-flow algorithm. The next steps are to estimate and remove the residual orbital ramp and to apply temporal low-pass filter to remove atmospheric contributions. The step by step comparison of the SBAS and PS approaches shows both methods complementarity. The SBAS analysis provide subsidence rates with an accuracy of a mm/yr over the whole basin in a large area, together with the subsidence non linear behavior through time, however at the expense of some spatial regularization. The PS method provides locally accurate and punctual deformation rates, but fails in this case to yield a good large scale map and the non linear temporal behavior of the subsidence. We conclude that the relative contrast in subsidence between individual buildings and infrastructure must be relatively small, on average of the order of 5mm/yr.
TIME SERIES ANALYSIS OF REMOTELY-SENSED TIR EMISSION: linking anomalies to physical processes
NASA Astrophysics Data System (ADS)
Pavlidou, E.; van der Meijde, M.; Hecker, C.; van der Werff, H.; Ettema, J.
2013-12-01
In the last 15 years, remote sensing has been evaluated for detecting thermal anomalies as precursor to earthquakes. Important issues that need yet to be tackled include definition of: (a) thermal anomaly, taking into account weather conditions, observation settings and ';natural' variability caused by background sources (b) the length of observations required for this purpose; and (c) the location of detected anomalies, which should be physically related to the tectonic activity. To determine whether thermal anomalies are statistical noise, mere meteorological conditions, or actual earthquake-related phenomena, we apply a novel approach. We use brightness temperature (top-of-atmosphere) data from thermal infrared imagery acquired at a hypertemporal (sub-hourly) interval, from geostationary weather satellites over multiple years. The length of the time series allows for analysis of meteorological effects (diurnal, seasonal or annual trends) and background variability, through the application of a combined spatial and temporal filter to distinguish extreme occurrences from trends. The definition of potential anomalies is based on statistical techniques, taking into account published (geo)physical characteristics of earthquake related thermal anomalies. We use synthetic data to test the performance of the proposed detection method and track potential factors affecting the results. Subsequently, we apply the method on original data from Iran and Turkey, in quiescent and earthquake-struck periods alike. We present our findings with main focus to assess resulting anomalies in relation to physical processes thereby considering: (a) meteorological effects, (b) the geographical, geological and environmental settings, and (c) physically realistic distances and potential physical relations with the activity of causative faults.
Reduction of maternal mortality due to preeclampsia in Colombia-an interrupted time-series analysis
Herrera-Medina, Rodolfo; Herrera-Escobar, Juan Pablo; Nieto-Díaz, Aníbal
2014-01-01
Introduction: Preeclampsia is the most important cause of maternal mortality in developing countries. A comprehensive prenatal care program including bio-psychosocial components was developed and introduced at a national level in Colombia. We report on the trends in maternal mortality rates and their related causes before and after implementation of this program. Methods: General and specific maternal mortality rates were monitored for nine years (1998-2006). An interrupted time-series analysis was performed with monthly data on cases of maternal mortality that compared trends and changes in national mortality rates and the impact of these changes attributable to the introduction of a bio-psychosocial model. Multivariate analyses were performed to evaluate correlations between the interventions. Results: Five years after (2002 - 2006) its introduction the general maternal mortality rate was significantly reduced to 23% (OR=0.77, CI 95% 0.71-0.82).The implementation of BPSM also reduced the incidence of preeclampsia in 22% (OR= 0.78, CI 95% 0.67-0.88), as also the labor complications by hemorrhage in 25% (OR=0.75, CI 95% 0.59-0.90) associated with the implementation of red code. The other causes of maternal mortality did not reveal significant changes. Biomedical, nutritional, psychosocial assessments, and other individual interventions in prenatal care were not correlated to maternal mortality (p= 0.112); however, together as a model we observed a significant association (p= 0.042). Conclusions: General maternal mortality was reduced after the implementation of a comprehensive national prenatal care program. Is important the evaluation of this program in others populations. PMID:24970956
Ground Deformation Mapping of Houston-Galveston, Texas Using InSAR Time-Series Analysis
NASA Astrophysics Data System (ADS)
QU, F.; Lu, Z.; Bawden, G. W.; Kim, J. W.
2014-12-01
Houston-Galveston region in Texas has been subsiding due to the combined effects of groundwater withdrawal, hydrocarbon extraction, soil compaction, and active faulting. This human- and partially nature-induced ground deformation has gradually threatened the stability of urban infrastructure and caused the loss of wetland habitat along the Gulf of Mexico. Interferometric synthetic aperture radar (InSAR) exploiting multiple SAR images has the capability of obtaining ground motions in high spatial resolution over large coverage. In this study, ERS-1/2 (1993-2000), ENVISAT (2004-2010), and ALOS (2007-2011) datasets are used to unravel the characteristics of ground deformation from 1993 to 2011 over the Houston-Galveston area. The persistent scatterer InSAR (PSInSAR) time-series analysis technique is employed to estimate the spatial and temporal variations of ground motions during 20 years. The ERS-1/2 PSInSAR products have measured subsidence (up to 5 cm/yr) in the northwest Houston area as well as a slight uplift (1 cm/yr) in the southeast region from 1993 to 2000. The subsidence rate (up to 2 cm/yr) between 2004 and 2011 has been obtained from ENVISAT and ALOS data. Our results indicate that the pattern of ground deformation was nearly concentric around the location of intense groundwater withdrawal and the subsiding area has been shrinking and migrating toward the northeast after 2000. In addition, an approximately 2 cm of differential subsidence across faults are observed. Presence of faults can induce localized surface displacements, aggravate localized subsidence, discontinue the integrity of ground water flow, and limit the horizontal spread of subsidence funnels. Finally, our long-term measurement of ground deformation has also been validated by GPS observations in study area.
Efficient Transfer Entropy Analysis of Non-Stationary Neural Time Series
Vicente, Raul; Díaz-Pernas, Francisco J.; Wibral, Michael
2014-01-01
Information theory allows us to investigate information processing in neural systems in terms of information transfer, storage and modification. Especially the measure of information transfer, transfer entropy, has seen a dramatic surge of interest in neuroscience. Estimating transfer entropy from two processes requires the observation of multiple realizations of these processes to estimate associated probability density functions. To obtain these necessary observations, available estimators typically assume stationarity of processes to allow pooling of observations over time. This assumption however, is a major obstacle to the application of these estimators in neuroscience as observed processes are often non-stationary. As a solution, Gomez-Herrero and colleagues theoretically showed that the stationarity assumption may be avoided by estimating transfer entropy from an ensemble of realizations. Such an ensemble of realizations is often readily available in neuroscience experiments in the form of experimental trials. Thus, in this work we combine the ensemble method with a recently proposed transfer entropy estimator to make transfer entropy estimation applicable to non-stationary time series. We present an efficient implementation of the approach that is suitable for the increased computational demand of the ensemble method's practical application. In particular, we use a massively parallel implementation for a graphics processing unit to handle the computationally most heavy aspects of the ensemble method for transfer entropy estimation. We test the performance and robustness of our implementation on data from numerical simulations of stochastic processes. We also demonstrate the applicability of the ensemble method to magnetoencephalographic data. While we mainly evaluate the proposed method for neuroscience data, we expect it to be applicable in a variety of fields that are concerned with the analysis of information transfer in complex biological, social, and
Zapata-Fonseca, Leonardo; Dotov, Dobromir; Fossion, Ruben; Froese, Tom
2016-01-01
There is a growing consensus that a fuller understanding of social cognition depends on more systematic studies of real-time social interaction. Such studies require methods that can deal with the complex dynamics taking place at multiple interdependent temporal and spatial scales, spanning sub-personal, personal, and dyadic levels of analysis. We demonstrate the value of adopting an extended multi-scale approach by re-analyzing movement time-series generated in a study of embodied dyadic interaction in a minimal virtual reality environment (a perceptual crossing experiment). Reduced movement variability revealed an interdependence between social awareness and social coordination that cannot be accounted for by either subjective or objective factors alone: it picks out interactions in which subjective and objective conditions are convergent (i.e., elevated coordination is perceived as clearly social, and impaired coordination is perceived as socially ambiguous). This finding is consistent with the claim that interpersonal interaction can be partially constitutive of direct social perception. Clustering statistics (Allan Factor) of salient events revealed fractal scaling. Complexity matching defined as the similarity between these scaling laws was significantly more pronounced in pairs of participants as compared to surrogate dyads. This further highlights the multi-scale and distributed character of social interaction and extends previous complexity matching results from dyadic conversation to non-verbal social interaction dynamics. Trials with successful joint interaction were also associated with an increase in local coordination. Consequently, a local coordination pattern emerges on the background of complex dyadic interactions in the PCE task and makes joint successful performance possible. PMID:28018274
Multi Band Insar Analysis of Subsidence Development Based on the Long Period Time Series
NASA Astrophysics Data System (ADS)
Çomut, F. C.; Ustun, A.; Lazecky, M.; Aref, M. M.
2015-12-01
The SAR Interferometry (InSAR) application has shown great potential in monitoring of land terrain changes and in detection of land deformations such as subsidence. Longer time analysis can lead to understand longer trends and changes. Using different bands of SAR satellite (C- from ERS 1-2 and Envisat, L- from ALOS) over the study area, we achieve knowledge of movements in long-term and evaluation of its dynamic changes within observed period of time. Results from InSAR processing fit with the position changes in vertical direction based on GPS network established over the basin as an effective geodetic network. Time series (StaMPS PS+SB) of several points over Çumra County in eastern part of Konya City show a general trend of the deformation that is expected to be approximately between -13 to -17 mm/year. Northern part of Karaman is affected by faster subsidence, borders of the subsidence trough were identified from Envisat. Presenting InSAR results together with GIS information about locations and time of occurrence of sudden subsidence, urban/industrial growth in time and climate changes helps in better understanding of the situation. This way, the impact of natural and man-made changes will be shown for urban planning thanks to InSAR and GIS comparisons with hydrogeological modeling. In this study we present results of differential and multitemporal InSAR series using different bands and GIS conjunction associated with seasonal and temporal groundwater level changes in Konya Closed Basin.
Trend analysis of long-term temperature time series in the Greater Toronto Area (GTA)
NASA Astrophysics Data System (ADS)
Mohsin, Tanzina; Gough, William A.
2010-08-01
As the majority of the world’s population is living in urban environments, there is growing interest in studying local urban climates. In this paper, for the first time, the long-term trends (31-162 years) of temperature change have been analyzed for the Greater Toronto Area (GTA). Annual and seasonal time series for a number of urban, suburban, and rural weather stations are considered. Non-parametric statistical techniques such as Mann-Kendall test and Theil-Sen slope estimation are used primarily for the assessing of the significance and detection of trends, and the sequential Mann test is used to detect any abrupt climate change. Statistically significant trends for annual mean and minimum temperatures are detected for almost all stations in the GTA. Winter is found to be the most coherent season contributing substantially to the increase in annual minimum temperature. The analyses of the abrupt changes in temperature suggest that the beginning of the increasing trend in Toronto started after the 1920s and then continued to increase to the 1960s. For all stations, there is a significant increase of annual and seasonal (particularly winter) temperatures after the 1980s. In terms of the linkage between urbanization and spatiotemporal thermal patterns, significant linear trends in annual mean and minimum temperature are detected for the period of 1878-1978 for the urban station, Toronto, while for the rural counterparts, the trends are not significant. Also, for all stations in the GTA that are situated in all directions except south of Toronto, substantial temperature change is detected for the periods of 1970-2000 and 1989-2000. It is concluded that the urbanization in the GTA has significantly contributed to the increase of the annual mean temperatures during the past three decades. In addition to urbanization, the influence of local climate, topography, and larger scale warming are incorporated in the analysis of the trends.
Zapata-Fonseca, Leonardo; Dotov, Dobromir; Fossion, Ruben; Froese, Tom
2016-01-01
There is a growing consensus that a fuller understanding of social cognition depends on more systematic studies of real-time social interaction. Such studies require methods that can deal with the complex dynamics taking place at multiple interdependent temporal and spatial scales, spanning sub-personal, personal, and dyadic levels of analysis. We demonstrate the value of adopting an extended multi-scale approach by re-analyzing movement time-series generated in a study of embodied dyadic interaction in a minimal virtual reality environment (a perceptual crossing experiment). Reduced movement variability revealed an interdependence between social awareness and social coordination that cannot be accounted for by either subjective or objective factors alone: it picks out interactions in which subjective and objective conditions are convergent (i.e., elevated coordination is perceived as clearly social, and impaired coordination is perceived as socially ambiguous). This finding is consistent with the claim that interpersonal interaction can be partially constitutive of direct social perception. Clustering statistics (Allan Factor) of salient events revealed fractal scaling. Complexity matching defined as the similarity between these scaling laws was significantly more pronounced in pairs of participants as compared to surrogate dyads. This further highlights the multi-scale and distributed character of social interaction and extends previous complexity matching results from dyadic conversation to non-verbal social interaction dynamics. Trials with successful joint interaction were also associated with an increase in local coordination. Consequently, a local coordination pattern emerges on the background of complex dyadic interactions in the PCE task and makes joint successful performance possible.
Fractal analysis of the short time series in a visibility graph method
NASA Astrophysics Data System (ADS)
Li, Ruixue; Wang, Jiang; Yu, Haitao; Deng, Bin; Wei, Xile; Chen, Yingyuan
2016-05-01
The aim of this study is to evaluate the performance of the visibility graph (VG) method on short fractal time series. In this paper, the time series of Fractional Brownian motions (fBm), characterized by different Hurst exponent H, are simulated and then mapped into a scale-free visibility graph, of which the degree distributions show the power-law form. The maximum likelihood estimation (MLE) is applied to estimate power-law indexes of degree distribution, and in this progress, the Kolmogorov-Smirnov (KS) statistic is used to test the performance of estimation of power-law index, aiming to avoid the influence of droop head and heavy tail in degree distribution. As a result, we find that the MLE gives an optimal estimation of power-law index when KS statistic reaches its first local minimum. Based on the results from KS statistic, the relationship between the power-law index and the Hurst exponent is reexamined and then amended to meet short time series. Thus, a method combining VG, MLE and KS statistics is proposed to estimate Hurst exponents from short time series. Lastly, this paper also offers an exemplification to verify the effectiveness of the combined method. In addition, the corresponding results show that the VG can provide a reliable estimation of Hurst exponents.
Kolmogorov Complexity Spectrum for Use in Analysis of Uv-B Radiation Time Series
NASA Astrophysics Data System (ADS)
Mihailović, Dragutin T.; Malinović-Milićević, Slavica; Arsenić, Ilija; Drešković, Nusret; Bukosa, Beata
2013-10-01
In this paper, we have used the Kolmogorov complexity and sample entropy measures to estimate the complexity of the UV-B radiation time series in the Vojvodina region (Serbia) for the period 1990-2007. We have defined the Kolmogorov complexity spectrum and have introduced the Kolmogorov complexity spectrum highest value (KCH). We have established the UV-B radiation time series on the basis of their daily sum (dose) for seven representative places in this region using: (i) measured data, (ii) data calculated via a derived empirical formula and (iii) data obtained by a parametric UV radiation model. We have calculated the Kolmogorov complexity (KC) based on the Lempel-Ziv algorithm (LZA), KCH and sample entropy (SE) values for each time series. We have divided the period 1990-2007 into two subintervals: (i) 1990-1998 and (ii) 1999-2007 and calculated the KC, KCH and SE values for the various time series in these subintervals. It is found that during the period 1999-2007, there is a decrease in the KC, KCH and SE, compared to the period 1990-1998. This complexity loss may be attributed to (i) the increased human intervention in the post civil war period causing increase of the air pollution and (ii) the increased cloudiness due to climate changes.
Analysis of time series of the EOP and the ICRF source coordinates
NASA Astrophysics Data System (ADS)
Zharov, V.; Voronko, N. A.; Shmeleva, N. V.
2014-12-01
Software ARIADNA was used for estimation of the Earth orientation parameters (EOP) for period 1984{2012. Simultaneously the time series of the coordinates of the ICRF radio sources were calculated. The least-squares method with constraints is applied. It is shown that most radio sources (including defining sources) are characterized by significant apparent motions.
Hatch, C.E.; Fisher, A.T.; Revenaugh, J.S.; Constantz, J.; Ruehl, C.
2006-01-01
We present a method for determining streambed seepage rates using time series thermal data. The new method is based on quantifying changes in phase and amplitude of temperature variations between pairs of subsurface sensors. For a reasonable range of streambed thermal properties and sensor spacings the time series method should allow reliable estimation of seepage rates for a range of at least ??10 m d-1 (??1.2 ?? 10-2 m s-1), with amplitude variations being most sensitive at low flow rates and phase variations retaining sensitivity out to much higher rates. Compared to forward modeling, the new method requires less observational data and less setup and data handling and is faster, particularly when interpreting many long data sets. The time series method is insensitive to streambed scour and sedimentation, which allows for application under a wide range of flow conditions and allows time series estimation of variable streambed hydraulic conductivity. This new approach should facilitate wider use of thermal methods and improve understanding of the complex spatial and temporal dynamics of surface water-groundwater interactions. Copyright 2006 by the American Geophysical Union.
The application of artificial neural networks to magnetotelluric time-series analysis
NASA Astrophysics Data System (ADS)
Manoj, C.; Nagarajan, Nandini
2003-05-01
Magnetotelluric (MT) signals are often contaminated with noise from natural or man-made processes that may not fit a normal distribution or are highly correlated. This may lead to serious errors in computed MT transfer functions and result in erroneous interpretation. A substantial improvement is possible when the time-series are presented as clean as possible for further processing. Cleaning of MT time-series is often done by manual editing. Editing of magnetotelluric time-series is subjective in nature and time consuming. Automation of such a process is difficult to achieve by statistical methods. Artificial neural networks (ANNs) are widely used to automate processes that require human intelligence. The objective here is to automate MT long-period time-series editing using ANN. A three-layer feed-forward artificial neural network (FANN) was adopted for the problem. As ANN-based techniques are computationally intensive, a novel approach was made, which involves editing of five simultaneously measured MT time-series that have been subdivided into stacks (a stack=5 × 256 data points). Neural network training was done at two levels. Signal and noise patterns of individual channels were taught first. Five channel parameters along with interchannel correlation and amplitude ratios formed the input for a final network, which predicts the quality of a stack. A large database (5000 traces for pattern training and 900 vectors for interchannel training) was prepared to train the network. There were two error parameters to minimize while training: training error and testing error. Training was stopped when both errors were below an acceptable level. The sensitivity of the neural network to the signal-to-noise ratio and the relative significance of its inputs were tested to ensure that the training was correct. MT time-series from four stations with varying degrees of noise contamination were used to demonstrate the application of the network. The application brought out
InSAR Time Series Analysis of Interseismic Deformation in Eastern Iran
NASA Astrophysics Data System (ADS)
Mousavi, Z.; Pathier, E.; Walpersdorf, A.; Lassere, C.; Tavakli, F.; Nankali, H.
2012-04-01
orbits. The 400 by 400 km studied area that includes the eastern part of the Doruneh fault is covered by seven satellite tracks (Descending: 120, 392, 163, 435 and 206 and Ascending: 156 and 385). The raw radar images are processed with ROI_PAC to construct the interferograms and unwrap them. The resulting differential interferogram phase is related to the deformation signal, changes of tropospheric delay, orbital and DEM errors and noise. We correct for the stratified part of tropospheric delay correlated with elevation using the observed phase-elevation correlation and for a twisted plane to remove orbital errors. Large scale seasonal atmospheric corrections are also investigated using the ERA-Interim meteorological model and GPS data. To investigate the long wavelength tectonic signal due to interseismic strain accumulation, a time series analysis of the selected images based on the small base line method (SBAS) has been done on a pixel basis in order to enhance the signal to noise ratio affected by a remaining atmospheric signal. The selection and the weighting of the interferograms are based on a noise energy function that measures the quality of each interferogram. The resulting displacement time series and a mean velocity map can be compared to GPS data.
Pancheliuga, V A; Pancheliuga, M S
2013-01-01
In the present work a methodological background for the histogram method of time series analysis is developed. Connection between shapes of smoothed histograms constructed on the basis of short segments of time series of fluctuations and the fractal dimension of the segments is studied. It is shown that the fractal dimension possesses all main properties of the histogram method. Based on it a further development of fractal dimension determination algorithm is proposed. This algorithm allows more precision determination of the fractal dimension by using the "all possible combination" method. The application of the method to noise-like time series analysis leads to results, which could be obtained earlier only by means of the histogram method based on human expert comparisons of histograms shapes.
Comprehensive time series analysis of the transiting extrasolar planet WASP-33b
NASA Astrophysics Data System (ADS)
Kovács, G.; Kovács, T.; Hartman, J. D.; Bakos, G. Á.; Bieryla, A.; Latham, D.; Noyes, R. W.; Regály, Zs.; Esquerdo, G. A.
2013-05-01
://www.aanda.orgPhotometric time series and lightcurves are only available at the CDS via anonymous ftp to cdsarc.u-strasbg.fr (ftp://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/553/A44
Rivera, Diego; Lillo, Mario; Granda, Stalin
2014-12-01
The concept of time stability has been widely used in the design and assessment of monitoring networks of soil moisture, as well as in hydrological studies, because it is as a technique that allows identifying of particular locations having the property of representing mean values of soil moisture in the field. In this work, we assess the effect of time stability calculations as new information is added and how time stability calculations are affected at shorter periods, subsampled from the original time series, containing different amounts of precipitation. In doing so, we defined two experiments to explore the time stability behavior. The first experiment sequentially adds new data to the previous time series to investigate the long-term influence of new data in the results. The second experiment applies a windowing approach, taking sequential subsamples from the entire time series to investigate the influence of short-term changes associated with the precipitation in each window. Our results from an operating network (seven monitoring points equipped with four sensors each in a 2-ha blueberry field) show that as information is added to the time series, there are changes in the location of the most stable point (MSP), and that taking the moving 21-day windows, it is clear that most of the variability of soil water content changes is associated with both the amount and intensity of rainfall. The changes of the MSP over each window depend on the amount of water entering the soil and the previous state of the soil water content. For our case study, the upper strata are proxies for hourly to daily changes in soil water content, while the deeper strata are proxies for medium-range stored water. Thus, different locations and depths are representative of processes at different time scales. This situation must be taken into account when water management depends on soil water content values from fixed locations.
The study of coastal groundwater depth and salinity variation using time-series analysis
Tularam, G.A. . E-mail: a.tularam@griffith.edu.au; Keeler, H.P. . E-mail: p.keeler@ms.unimelb.edu.au
2006-10-15
A time-series approach is applied to study and model tidal intrusion into coastal aquifers. The authors examine the effect of tidal behaviour on groundwater level and salinity intrusion for the coastal Brisbane region using auto-correlation and spectral analyses. The results show a close relationship between tidal behaviour, groundwater depth and salinity levels for the Brisbane coast. The known effect can be quantified and incorporated into new models in order to more accurately map salinity intrusion into coastal groundwater table.
How cyanobacteria pose new problems to old methods: challenges in microarray time series analysis
2013-01-01
Background The transcriptomes of several cyanobacterial strains have been shown to exhibit diurnal oscillation patterns, reflecting the diurnal phototrophic lifestyle of the organisms. The analysis of such genome-wide transcriptional oscillations is often facilitated by the use of clustering algorithms in conjunction with a number of pre-processing steps. Biological interpretation is usually focussed on the time and phase of expression of the resulting groups of genes. However, the use of microarray technology in such studies requires the normalization of pre-processing data, with unclear impact on the qualitative and quantitative features of the derived information on the number of oscillating transcripts and their respective phases. Results A microarray based evaluation of diurnal expression in the cyanobacterium Synechocystis sp. PCC 6803 is presented. As expected, the temporal expression patterns reveal strong oscillations in transcript abundance. We compare the Fourier transformation-based expression phase before and after the application of quantile normalization, median polishing, cyclical LOESS, and least oscillating set (LOS) normalization. Whereas LOS normalization mostly preserves the phases of the raw data, the remaining methods introduce systematic biases. In particular, quantile-normalization is found to introduce a phase-shift of 180°, effectively changing night-expressed genes into day-expressed ones. Comparison of a large number of clustering results of differently normalized data shows that the normalization method determines the result. Subsequent steps, such as the choice of data transformation, similarity measure, and clustering algorithm, only play minor roles. We find that the standardization and the DTF transformation are favorable for the clustering of time series in contrast to the 12 m transformation. We use the cluster-wise functional enrichment of a clustering derived by LOS normalization, clustering using flowClust, and DFT
Effect of Environmental Factors on Low Weight in Non-Premature Births: A Time Series Analysis
Díaz, Julio; Arroyo, Virginia; Ortiz, Cristina; Carmona, Rocío; Linares, Cristina
2016-01-01
Objective Exposure to pollutants during pregnancy has been related to adverse birth outcomes. LBW can give rise to lifelong impairments. Prematurity is the leading cause of LBW, yet few studies have attempted to analyse how environmental factors can influence LBW in infants who are not premature. This study therefore sought to analyse the influence of air pollution, noise levels and temperature on LBW in non-premature births in Madrid during the period 2001–2009. Methods Ecological time-series study to assess the impact of PM2.5, NO2 and O3 concentrations, noise levels, and temperatures on LBW among non-premature infants across the period 2001–2009. Our analysis extended to infants having birth weights of 1,500 g to 2,500 g (VLBW) and less than 1,500 g (ELBW). Environmental variables were lagged until 37 weeks with respect to the date of birth, and cross-correlation functions were used to identify explaining lags. Results were quantified using Poisson regression models. Results Across the study period 298,705 births were registered in Madrid, 3,290 of which had LBW; of this latter total, 1,492 were non-premature. PM2.5 was the only pollutant to show an association with the three variables of LBW in non-premature births. This association occurred at around the third month of gestation for LBW and VLBW (LBW: lag 23 and VLBW: lag 25), and at around the eighth month of gestation for ELBW (lag 6). Leqd was linked to LBW at lag zero. The RR of PM2.5 on LBW was 1.01 (1.00 1.03). The RR of Leqd on LBW was 1.09 (0.99 1.19)(p<0.1). Conclusions The results obtained indicate that PM2.5 had influence on LBW. The adoption of measures aimed at reducing the number of vehicles would serve to lower pregnant women's exposure. In the case of noise should be limited the exposure to high levels during the final weeks of pregnancy. PMID:27788159
Nease, Brian R. Ueki, Taro
2009-12-10
A time series approach has been applied to the nuclear fission source distribution generated by Monte Carlo (MC) particle transport in order to calculate the non-fundamental mode eigenvalues of the system. The novel aspect is the combination of the general technical principle of projection pursuit for multivariate data with the neutron multiplication eigenvalue problem in the nuclear engineering discipline. Proof is thoroughly provided that the stationary MC process is linear to first order approximation and that it transforms into one-dimensional autoregressive processes of order one (AR(1)) via the automated choice of projection vectors. The autocorrelation coefficient of the resulting AR(1) process corresponds to the ratio of the desired mode eigenvalue to the fundamental mode eigenvalue. All modern MC codes for nuclear criticality calculate the fundamental mode eigenvalue, so the desired mode eigenvalue can be easily determined. This time series approach was tested for a variety of problems including multi-dimensional ones. Numerical results show that the time series approach has strong potential for three dimensional whole reactor core. The eigenvalue ratio can be updated in an on-the-fly manner without storing the nuclear fission source distributions at all previous iteration cycles for the mean subtraction. Lastly, the effects of degenerate eigenvalues are investigated and solutions are provided.
NASA Technical Reports Server (NTRS)
Menenti, M.; Azzali, S.; Verhoef, W.; Van Swol, R.
1993-01-01
Examples are presented of applications of a fast Fourier transform algorithm to analyze time series of images of Normalized Difference Vegetation Index values. The results obtained for a case study on Zambia indicated that differences in vegetation development among map units of an existing agroclimatic map were not significant, while reliable differences were observed among the map units obtained using the Fourier analysis.
NASA Astrophysics Data System (ADS)
Stanley, R. H. R.; Jenkins, W. J.; Doney, S. C.; Lott, D. E., III
2015-09-01
Significant rates of primary production occur in the oligotrophic ocean, without any measurable nutrients present in the mixed layer, fueling a scientific paradox that has lasted for decades. Here, we provide a new determination of the annual mean physical supply of nitrate to the euphotic zone in the western subtropical North Atlantic. We combine a 3-year time series of measurements of tritiugenic 3He from 2003 to 2006 in the surface ocean at the Bermuda Atlantic Time-series Study (BATS) site with a sophisticated noble gas calibrated air-sea gas exchange model to constrain the 3He flux across the sea-air interface, which must closely mirror the upward 3He flux into the euphotic zone. The product of the 3He flux and the observed subsurface nitrate-3He relationship provides an estimate of the minimum rate of new production in the BATS region. We also apply the gas model to an earlier time series of 3He measurements at BATS in order to recalculate new production fluxes for the 1985 to 1988 time period. The observations, despite an almost 3-fold difference in the nitrate-3He relationship, yield a roughly consistent estimate of nitrate flux. In particular, the nitrate flux from 2003 to 2006 is estimated to be 0.65 ± 0.14 mol m-2 yr-1, which is ~40 % smaller than the calculated flux for the period from 1985 to 1988. The difference in nitrate flux between the time periods may be signifying a real difference in new production resulting from changes in subtropical mode water formation. Overall, the nitrate flux is larger than most estimates of export fluxes or net community production fluxes made locally for the BATS site, which is likely a reflection of the larger spatial scale covered by the 3He technique and potentially also by the decoupling of 3He and nitrate during the obduction of water masses from the main thermocline into the upper ocean. The upward nitrate flux is certainly large enough to support observed rates of primary production at BATS and more generally
Nonlinear time-series analysis of current signal in cathodic contact glow discharge electrolysis
NASA Astrophysics Data System (ADS)
Allagui, Anis; Rojas, Andrea Espinel; Bonny, Talal; Elwakil, Ahmed S.; Abdelkareem, Mohammad Ali
2016-05-01
In the standard two-electrode configuration employed in electrolytic process, when the control dc voltage is brought to a critical value, the system undergoes a transition from conventional electrolysis to contact glow discharge electrolysis (CGDE), which has also been referred to as liquid-submerged micro-plasma, glow discharge plasma electrolysis, electrode effect, electrolytic plasma, etc. The light-emitting process is associated with the development of an irregular and erratic current time-series which has been arbitrarily labelled as "random," and thus dissuaded further research in this direction. Here, we examine the current time-series signals measured in cathodic CGDE configuration in a concentrated KOH solution at different dc bias voltages greater than the critical voltage. We show that the signals are, in fact, not random according to the NIST SP. 800-22 test suite definition. We also demonstrate that post-processing low-pass filtered sequences requires less time than the native as-measured sequences, suggesting a superposition of low frequency chaotic fluctuations and high frequency behaviors (which may be produced by more than one possible source of entropy). Using an array of nonlinear time-series analyses for dynamical systems, i.e., the computation of largest Lyapunov exponents and correlation dimensions, and re-construction of phase portraits, we found that low-pass filtered datasets undergo a transition from quasi-periodic to chaotic to quasi-hyper-chaotic behavior, and back again to chaos when the voltage controlling-parameter is increased. The high frequency part of the signals is discussed in terms of highly nonlinear turbulent motion developed around the working electrode.
NASA Astrophysics Data System (ADS)
Sawant, S. A.; Chakraborty, M.; Suradhaniwar, S.; Adinarayana, J.; Durbha, S. S.
2016-06-01
Satellite based earth observation (EO) platforms have proved capability to spatio-temporally monitor changes on the earth's surface. Long term satellite missions have provided huge repository of optical remote sensing datasets, and United States Geological Survey (USGS) Landsat program is one of the oldest sources of optical EO datasets. This historical and near real time EO archive is a rich source of information to understand the seasonal changes in the horticultural crops. Citrus (Mandarin / Nagpur Orange) is one of the major horticultural crops cultivated in central India. Erratic behaviour of rainfall and dependency on groundwater for irrigation has wide impact on the citrus crop yield. Also, wide variations are reported in temperature and relative humidity causing early fruit onset and increase in crop water requirement. Therefore, there is need to study the crop growth stages and crop evapotranspiration at spatio-temporal scale for managing the scarce resources. In this study, an attempt has been made to understand the citrus crop growth stages using Normalized Difference Time Series (NDVI) time series data obtained from Landsat archives (http://earthexplorer.usgs.gov/). Total 388 Landsat 4, 5, 7 and 8 scenes (from year 1990 to Aug. 2015) for Worldwide Reference System (WRS) 2, path 145 and row 45 were selected to understand seasonal variations in citrus crop growth. Considering Landsat 30 meter spatial resolution to obtain homogeneous pixels with crop cover orchards larger than 2 hectare area was selected. To consider change in wavelength bandwidth (radiometric resolution) with Landsat sensors (i.e. 4, 5, 7 and 8) NDVI has been selected to obtain continuous sensor independent time series. The obtained crop growth stage information has been used to estimate citrus basal crop coefficient information (Kcb). Satellite based Kcb estimates were used with proximal agrometeorological sensing system
Update on EMD and Hilbert-Spectra Analysis of Time Series
NASA Technical Reports Server (NTRS)
Huang, Norden E.
2003-01-01
This method is especially well suited for analyzing time-series data that represent nonstationary and nonlinear physical phenomena. The method is based principally on the concept of empirical mode decomposition (EMD), according to which any complicated signal (as represented by digital samples) can be decomposed into a finite number of functions, called "intrinsic mode functions" (IMFs), that admit well-behaved Hilbert transforms. The local energies and the instantaneous frequencies derived from the IMFs through Hilbert transforms can be used to construct an energy-frequency-time distribution, denoted a Hilbert spectrum.
Short-term pollution forecasts based on linear and nonlinear methods of time series analysis
NASA Astrophysics Data System (ADS)
Russo, A.; Trigo, R. M.
2012-04-01
Urban air pollution is a complex mixture of toxic components, which may induce acute and chronic responses from sensitive groups, such as children and people with previous heart and respiratory insufficiencies. However, air pollution, presents a highly chaotic and non-linear behavior. In this work we analyzed several pollutants time series recorded in the urban area of Lisbon (Portugal) for the 2002-2006 period. Linear and nonlinear methods were applied in order to assess NO2, PM10 and O3 main trends and fluctuations and finally, to produce daily forecasts of the referred pollutants. Here we evaluate the potential of linear and non-linear neural networks (NN) to produce short-term forecasts, and also the contribution of meteorological variables (daily mean temperature, radiation, wind speed and direction, boundary layer height, humidity) to pollutants dispersion. Additionally, we assess the role of large-scale circulation patterns, usually referred as Weather types (WT) (from the ERA40/ECMWF and ECMWF SLP database) towards the occurrence of critical pollution events identified previously. The presence and importance of trends and fluctuation is addressed by means of two modelling approaches: (1) raw data modelling; (2) residuals modelling (after the removal of the trends from the original data). The relative importance of two periodic components, the weekly and the monthly cycles, is addressed. For the three pollutants, the approach based on the removal of the weekly cycle presents the best results, comparatively to the removal of the monthly cycle or to the use of the raw data. The best predictors are chosen independently for each monitoring station and pollutant through an objective procedure (backward stepwise regression). The analysis reveals that the most significant variables in predicting NO2 concentration are several NO2 measures, wind direction and speed and global radiation, while for O3 correspond to several O3 measures, O3 precursors and WT
Beyond Fractals and 1/f Noise: Multifractal Analysis of Complex Physiological Time Series
NASA Astrophysics Data System (ADS)
Ivanov, Plamen Ch.; Amaral, Luis A. N.; Ashkenazy, Yosef; Stanley, H. Eugene; Goldberger, Ary L.; Hausdorff, Jeffrey M.; Yoneyama, Mitsuru; Arai, Kuniharu
2001-03-01
We investigate time series with 1/f-like spectra generated by two physiologic control systems --- the human heartbeat and human gait. We show that physiological fluctuations exhibit unexpected ``hidden'' structures often described by scaling laws. In particular, our studies indicate that when analyzed on different time scales the heartbeat fluctuations exhibit cascades of branching patterns with self-similar (fractal) properties, characterized by long-range power-law anticorrelations. We find that these scaling features change during sleep and wake phases, and with pathological perturbations. Further, by means of a new wavelet-based technique, we find evidence of multifractality in the healthy human heartbeat even under resting conditions, and show that the multifractal character and nonlinear properties of the healthy heart are encoded in the Fourier phases. We uncover a loss of multifractality for a life-threatening condition, congestive heart failure. In contrast to the heartbeat, we find that the interstride interval time series of healthy human gait, a voluntary process under neural regulation, is described by a single fractal dimension (such as classical 1/f noise) indicating monofractal behavior. Thus our approach can help distinguish physiological and physical signals with comparable frequency spectra and two-point correlations, and guide modeling of their control mechanisms.
TaiWan Ionospheric Model (TWIM) prediction based on time series autoregressive analysis
NASA Astrophysics Data System (ADS)
Tsai, L. C.; Macalalad, Ernest P.; Liu, C. H.
2014-10-01
As described in a previous paper, a three-dimensional ionospheric electron density (Ne) model has been constructed from vertical Ne profiles retrieved from the FormoSat3/Constellation Observing System for Meteorology, Ionosphere, and Climate GPS radio occultation measurements and worldwide ionosonde foF2 and foE data and named the TaiWan Ionospheric Model (TWIM). The TWIM exhibits vertically fitted α-Chapman-type layers with distinct F2, F1, E, and D layers, and surface spherical harmonic approaches for the fitted layer parameters including peak density, peak density height, and scale height. To improve the TWIM into a real-time model, we have developed a time series autoregressive model to forecast short-term TWIM coefficients. The time series of TWIM coefficients are considered as realizations of stationary stochastic processes within a processing window of 30 days. These autocorrelation coefficients are used to derive the autoregressive parameters and then forecast the TWIM coefficients, based on the least squares method and Lagrange multiplier technique. The forecast root-mean-square relative TWIM coefficient errors are generally <30% for 1 day predictions. The forecast TWIM values of foE and foF2 values are also compared and evaluated using worldwide ionosonde data.
[Improving data warehouse environments for efficient analysis of long time-series data].
Kataoka, Hiromi; Hatakeyama, Yutaka; Okuhara, Yoshiyasu; Sugiura, Tetsuro
2012-07-01
Medical records contain enormous amounts of data. It is important to extract useful evidence from such data and feedback to clinical medicine. Evidence-based medicine (EBM) was introduced in the 1990s and has been widely used for more than 20 years, however, hospital information system environments that take advantage of the ideas of EBM have not yet been established. Recently, the numbers of medical institutions with multilateral search systems for the medical records stored in data warehouses (DWHs) have been increasing, but these institutions' systems cannot deal fully with issues such as data reliability and high-dimensional, high-speed searches. DWHs can control long time-series data. Although, the measurement methods and analytical equipment used have been modified and improved with advances in testing techniques, this may have induced shifting and/or fragmentation of these types of data. Furthermore, database design has to be flexible to satisfy the various demands of information retrieval; systems must therefore have the structures to deal with such demands. We report here our new system infrastructure, which exchanges data in order to absorb the data shifting associated with changes in the testing methods. The system enables the preparation of DWH environments that can be used to seamlessly analyze long time-series data, record in knowledge databases the results of comprehensive analyses of institutions' characteristics of laboratory diagnoses, and use the data in education, research and clinical practice.
Brosig, B; Leweke, F; Milch, W; Eckhard, M; Reimer, C
2001-06-01
The term "brittle diabetes" denotes the unstable course of an insulin-dependent diabetes characterised by frequent hypo- or hyperglycaemic crises. The aim of this study is to demonstrate empirically how psychosocial parameters interact with metabolic instability in a paradigmatic case of juvenile brittle diabetes. By means of a structured diary study, blood sugar values, moods (SAM), body symptoms (GBB), the daily hustle and hassle, helping therapeutic alliance (HAQ) and the aspects of setting were registered. Resulting time series (112 days each) were ARIMA-analysed by a multivariate approach. It could be shown that the mean variance of daily blood sugar values as an indicator of brittleness was predicted by moods, body complaints and by a family session as setting factor (p < 0.05, for corresponding predictors). Feelings of dominance preceded an increase of blood sugar variance, whereas depressive moods, anger and body symptoms were associated with metabolic instability. A family therapy session also resulted in an increase of the mean blood sugar variance. The model accounted for almost 30% of the total variance of the dependent variable (R-square-adjusted, p < 0.0001). The potential of multivariate time-series as a means to demonstrate psychosomatic interrelations is discussed. We believe that the results may also contribute to an empirically rooted understanding of psychodynamic processes in psychosomatoses.
A Bias and Variance Analysis for Multistep-Ahead Time Series Forecasting.
Ben Taieb, Souhaib; Atiya, Amir F
2016-01-01
Multistep-ahead forecasts can either be produced recursively by iterating a one-step-ahead time series model or directly by estimating a separate model for each forecast horizon. In addition, there are other strategies; some of them combine aspects of both aforementioned concepts. In this paper, we present a comprehensive investigation into the bias and variance behavior of multistep-ahead forecasting strategies. We provide a detailed review of the different multistep-ahead strategies. Subsequently, we perform a theoretical study that derives the bias and variance for a number of forecasting strategies. Finally, we conduct a Monte Carlo experimental study that compares and evaluates the bias and variance performance of the different strategies. From the theoretical and the simulation studies, we analyze the effect of different factors, such as the forecast horizon and the time series length, on the bias and variance components, and on the different multistep-ahead strategies. Several lessons are learned, and recommendations are given concerning the advantages, disadvantages, and best conditions of use of each strategy.
Short-term prediction of solar irradiance using time-series analysis
Chowdhury, B.H. . Dept. of Electrical Engineering)
1990-01-01
A new statistical model for solar irradiance prediction is described. The method makes use of the atmospheric parameterizations as well as a time-series model to forecast a sequence of global irradiance in the 3--10 min time frame. A survey of some of the prominent research of the recent past reveals a definite lack of irradiance models that approach subhourly intervals, especially in the range mentioned. In this article, accurate parameterizations of atmospheric phenomena are used in a prewhitening process so that a time-series model may be used effectively to forecast irradiance components up to an hour in advance in the 3--10 min time intervals. The model requires only previous global horizontal irradiance measurement at a site. Results show that when compared with actual data on two locations in the southeaster United States, the forecasts are quite accurate, and the model is site-independent. Under some instances, forecasts may be inaccurate when there are sudden transitional changes in the cloud cover moving across the sun. In order for the proposed irradiance model to predict such transitional changes correctly, frequent forecast updates become necessary.
A Regularized Linear Dynamical System Framework for Multivariate Time Series Analysis
Liu, Zitao; Hauskrecht, Milos
2015-01-01
Linear Dynamical System (LDS) is an elegant mathematical framework for modeling and learning Multivariate Time Series (MTS). However, in general, it is difficult to set the dimension of an LDS’s hidden state space. A small number of hidden states may not be able to model the complexities of a MTS, while a large number of hidden states can lead to overfitting. In this paper, we study learning methods that impose various regularization penalties on the transition matrix of the LDS model and propose a regularized LDS learning framework (rLDS) which aims to (1) automatically shut down LDSs’ spurious and unnecessary dimensions, and consequently, address the problem of choosing the optimal number of hidden states; (2) prevent the overfitting problem given a small amount of MTS data; and (3) support accurate MTS forecasting. To learn the regularized LDS from data we incorporate a second order cone program and a generalized gradient descent method into the Maximum a Posteriori framework and use Expectation Maximization to obtain a low-rank transition matrix of the LDS model. We propose two priors for modeling the matrix which lead to two instances of our rLDS. We show that our rLDS is able to recover well the intrinsic dimensionality of the time series dynamics and it improves the predictive performance when compared to baselines on both synthetic and real-world MTS datasets. PMID:25905027
NASA Astrophysics Data System (ADS)
Alexander, Robert L.; O'Modhrain, Sile; Roberts, D. Aaron; Gilbert, Jason A.; Zurbuchen, Thomas H.
2014-07-01
The effective navigation, mining, and analysis of large time series data sets presents a recurring challenge throughout heliophysics. Audification, a specific form of auditory analysis commonly used in other fields of research (such as geoseismology), provides a promising technique for the evaluation of spectral features in long heliospheric time series data sets. Following a standard research methodology for the development of new analysis techniques, this paper presents a detailed case study in which audification was introduced into the working process of an experienced heliophysics research scientist and used for the identification and classification of features in high-resolution magnetometer data during a structured analysis task. Auditory evaluation successfully led to the detection of artificial, instrument-induced noise that was not previously observed by the scientist and also the identification of wave activity embedded within turbulent solar wind data. A follow-up interview indicated that the scientist continued using these auditory analysis methods in the assessment of every large data set during the 2 months after the study was completed. These findings indicate that audification can be valuable and enabling for researchers in forming a deeper understanding of both microstructures and macrostructures within large time series. Additionally, as both a standalone methodology and a supplement to visual analysis methods, audification can expedite certain stages of the data survey, analysis, and mining process and provide new qualitative insight into the spectral content of time-varying signals.
NASA Technical Reports Server (NTRS)
Scargle, J. D.
1982-01-01
Detection of a periodic signal hidden in noise is frequently a goal in astronomical data analysis. This paper does not introduce a new detection technique, but instead studies the reliability and efficiency of detection with the most commonly used technique, the periodogram, in the case where the observation times are unevenly spaced. This choice was made because, of the methods in current use, it appears to have the simplest statistical behavior. A modification of the classical definition of the periodogram is necessary in order to retain the simple statistical behavior of the evenly spaced case. With this modification, periodogram analysis and least-squares fitting of sine waves to the data are exactly equivalent. Certain difficulties with the use of the periodogram are less important than commonly believed in the case of detection of strictly periodic signals. In addition, the standard method for mitigating these difficulties (tapering) can be used just as well if the sampling is uneven. An analysis of the statistical significance of signal detections is presented, with examples
NASA Astrophysics Data System (ADS)
Scargle, J. D.
1982-12-01
Detection of a periodic signal hidden in noise is frequently a goal in astronomical data analysis. This paper does not introduce a new detection technique, but instead studies the reliability and efficiency of detection with the most commonly used technique, the periodogram, in the case where the observation times are unevenly spaced. This choice was made because, of the methods in current use, it appears to have the simplest statistical behavior. A modification of the classical definition of the periodogram is necessary in order to retain the simple statistical behavior of the evenly spaced case. With this modification, periodogram analysis and least-squares fitting of sine waves to the data are exactly equivalent. Certain difficulties with the use of the periodogram are less important than commonly believed in the case of detection of strictly periodic signals. In addition, the standard method for mitigating these difficulties (tapering) can be used just as well if the sampling is uneven. An analysis of the statistical significance of signal detections is presented, with examples
Lara, Juan A; Lizcano, David; Pérez, Aurora; Valente, Juan P
2014-10-01
There are now domains where information is recorded over a period of time, leading to sequences of data known as time series. In many domains, like medicine, time series analysis requires to focus on certain regions of interest, known as events, rather than analyzing the whole time series. In this paper, we propose a framework for knowledge discovery in both one-dimensional and multidimensional time series containing events. We show how our approach can be used to classify medical time series by means of a process that identifies events in time series, generates time series reference models of representative events and compares two time series by analyzing the events they have in common. We have applied our framework on time series generated in the areas of electroencephalography (EEG) and stabilometry. Framework performance was evaluated in terms of classification accuracy, and the results confirmed that the proposed schema has potential for classifying EEG and stabilometric signals. The proposed framework is useful for discovering knowledge from medical time series containing events, such as stabilometric and electroencephalographic time series. These results would be equally applicable to other medical domains generating iconographic time series, such as, for example, electrocardiography (ECG).
Mayaud, C.; Wagner, T.; Benischke, R.; Birk, S.
2014-01-01
Summary The Lurbach karst system (Styria, Austria) is drained by two major springs and replenished by both autogenic recharge from the karst massif itself and a sinking stream that originates in low permeable schists (allogenic recharge). Detailed data from two events recorded during a tracer experiment in 2008 demonstrate that an overflow from one of the sub-catchments to the other is activated if the discharge of the main spring exceeds a certain threshold. Time series analysis (autocorrelation and cross-correlation) was applied to examine to what extent the various available methods support the identification of the transient inter-catchment flow observed in this binary karst system. As inter-catchment flow is found to be intermittent, the evaluation was focused on single events. In order to support the interpretation of the results from the time series analysis a simplified groundwater flow model was built using MODFLOW. The groundwater model is based on the current conceptual understanding of the karst system and represents a synthetic karst aquifer for which the same methods were applied. Using the wetting capability package of MODFLOW, the model simulated an overflow similar to what has been observed during the tracer experiment. Various intensities of allogenic recharge were employed to generate synthetic discharge data for the time series analysis. In addition, geometric and hydraulic properties of the karst system were varied in several model scenarios. This approach helps to identify effects of allogenic recharge and aquifer properties in the results from the time series analysis. Comparing the results from the time series analysis of the observed data with those of the synthetic data a good agreement was found. For instance, the cross-correlograms show similar patterns with respect to time lags and maximum cross-correlation coefficients if appropriate hydraulic parameters are assigned to the groundwater model. The comparable behaviors of the real and
Mayaud, C; Wagner, T; Benischke, R; Birk, S
2014-04-16
The Lurbach karst system (Styria, Austria) is drained by two major springs and replenished by both autogenic recharge from the karst massif itself and a sinking stream that originates in low permeable schists (allogenic recharge). Detailed data from two events recorded during a tracer experiment in 2008 demonstrate that an overflow from one of the sub-catchments to the other is activated if the discharge of the main spring exceeds a certain threshold. Time series analysis (autocorrelation and cross-correlation) was applied to examine to what extent the various available methods support the identification of the transient inter-catchment flow observed in this binary karst system. As inter-catchment flow is found to be intermittent, the evaluation was focused on single events. In order to support the interpretation of the results from the time series analysis a simplified groundwater flow model was built using MODFLOW. The groundwater model is based on the current conceptual understanding of the karst system and represents a synthetic karst aquifer for which the same methods were applied. Using the wetting capability package of MODFLOW, the model simulated an overflow similar to what has been observed during the tracer experiment. Various intensities of allogenic recharge were employed to generate synthetic discharge data for the time series analysis. In addition, geometric and hydraulic properties of the karst system were varied in several model scenarios. This approach helps to identify effects of allogenic recharge and aquifer properties in the results from the time series analysis. Comparing the results from the time series analysis of the observed data with those of the synthetic data a good agreement was found. For instance, the cross-correlograms show similar patterns with respect to time lags and maximum cross-correlation coefficients if appropriate hydraulic parameters are assigned to the groundwater model. The comparable behaviors of the real and the
Assimakis, P D; Dillbeck, M C
1995-06-01
Two replication studies test in Canada a field theory of the effect of consciousness on social change. The exogenous variable is the number of participants in the largest North American group practice of the Transcendental Meditation and TM-Sidhi program, in Iowa. The first study indicated a significant reduction in violent deaths (homicide, suicide, and motor vehicle fatalities), using both time series intervention analysis and transfer function analysis methods, in weeks following change in the exogenous variable during the period 1983 to 1985. The second study, using time series intervention analysis, gave during and after intervention periods a significant improvement in quality of life on an index composed of the behavioral variables available on a monthly basis for Canada from 1972 to 1986-homicide, suicide, motor vehicle fatalities, cigarette consumption, and workers' days lost due to strikes. Implications of the findings for theory and social policy are noted briefly.
Estimating equation–based causality analysis with application to microarray time series data
Hu, Jianhua; Hu, Feifang
2009-01-01
Microarray time-course data can be used to explore interactions among genes and infer gene network. The crucial step in constructing gene network is to develop an appropriate causality test. In this regard, the expression profile of each gene can be treated as a time series. A typical existing method establishes the Granger causality based on Wald type of test, which relies on the homoscedastic normality assumption of the data distribution. However, this assumption can be seriously violated in real microarray experiments and thus may lead to inconsistent test results and false scientific conclusions. To overcome the drawback, we propose an estimating equation–based method which is robust to both heteroscedasticity and nonnormality of the gene expression data. In fact, it only requires the residuals to be uncorrelated. We will use simulation studies and a real-data example to demonstrate the applicability of the proposed method. PMID:19329818
NASA Technical Reports Server (NTRS)
Scargle, Jeffrey D.
1990-01-01
While chaos arises only in nonlinear systems, standard linear time series models are nevertheless useful for analyzing data from chaotic processes. This paper introduces such a model, the chaotic moving average. This time-domain model is based on the theorem that any chaotic process can be represented as the convolution of a linear filter with an uncorrelated process called the chaotic innovation. A technique, minimum phase-volume deconvolution, is introduced to estimate the filter and innovation. The algorithm measures the quality of a model using the volume covered by the phase-portrait of the innovation process. Experiments on synthetic data demonstrate that the algorithm accurately recovers the parameters of simple chaotic processes. Though tailored for chaos, the algorithm can detect both chaos and randomness, distinguish them from each other, and separate them if both are present. It can also recover nonminimum-delay pulse shapes in non-Gaussian processes, both random and chaotic.
Analysis of PV Advanced Inverter Functions and Setpoints under Time Series Simulation.
Seuss, John; Reno, Matthew J.; Broderick, Robert Joseph; Grijalva, Santiago
2016-05-01
Utilities are increasingly concerned about the potential negative impacts distributed PV may have on the operational integrity of their distribution feeders. Some have proposed novel methods for controlling a PV system's grid - tie inverter to mitigate poten tial PV - induced problems. This report investigates the effectiveness of several of these PV advanced inverter controls on improving distribution feeder operational metrics. The controls are simulated on a large PV system interconnected at several locations within two realistic distribution feeder models. Due to the time - domain nature of the advanced inverter controls, quasi - static time series simulations are performed under one week of representative variable irradiance and load data for each feeder. A para metric study is performed on each control type to determine how well certain measurable network metrics improve as a function of the control parameters. This methodology is used to determine appropriate advanced inverter settings for each location on the f eeder and overall for any interconnection location on the feeder.
PreAnalyseExtended: A graphical tool for (geophysical) time series analysis
NASA Astrophysics Data System (ADS)
Gebauer, André
2016-04-01
Time depending records of different geophysical and geodetic measurement systems require screening and post-processing, often combining the primary observable with additional measurement quantities from other external sensors or geophysical models. The ring laser 'G' located at the Geodetic Observatory Wettzell for example observes rotational ground motions depending on the sensor orientation. Hence tilt effects need to be corrected from the raw measurements of rotation. While the local tilt is taken from an independent time series of an auxiliary sensor, solid Earth tides and polar motion are corrected based on appropriate models. PreAnalyseExtended is a powerful software tool that combines the screening and processing of geophysical measurements of a variety of input sensors with a unique set of at least seven fully included models. This talk provides an introduction the important features of this open source tool.
Trend analysis of air temperature and precipitation time series over Greece: 1955-2010
NASA Astrophysics Data System (ADS)
Marougianni, G.; Melas, D.; Kioutsioukis, I.; Feidas, H.; Zanis, P.; Anandranistakis, E.
2012-04-01
In this study, a database of air temperature and precipitation time series from the network of Hellenic National Meteorological Service has been developed in the framework of the project GEOCLIMA, co-financed by the European Union and Greek national funds through the Operational Program "Competitiveness and Entrepreneurship" of the Research Funding Program COOPERATION 2009. Initially, a quality test was applied to the raw data and then missing observations have been imputed with a regularized, spatial-temporal expectation - maximization algorithm to complete the climatic record. Next, a quantile - matching algorithm was applied in order to verify the homogeneity of the data. The processed time series were used for the calculation of temporal annual and seasonal trends of air temperature and precipitation. Monthly maximum and minimum surface air temperature and precipitation means at all available stations in Greece were analyzed for temporal trends and spatial variation patterns for the longest common time period of homogenous data (1955 - 2010), applying the Mann-Kendall test. The majority of the examined stations showed a significant increase in the summer maximum and minimum temperatures; this could be possibly physically linked to the Etesian winds, because of the less frequent expansion of the low over the southeastern Mediterranean. Summer minimum temperatures have been increasing at a faster rate than that of summer maximum temperatures, reflecting an asymmetric change of extreme temperature distributions. Total annual precipitation has been significantly decreased at the stations located in western Greece, as well as in the southeast, while the remaining areas exhibit a non-significant negative trend. This reduction is very likely linked to the positive phase of the NAO that resulted in an increase in the frequency and persistence of anticyclones over the Mediterranean.
Improved efficiency of maximum likelihood analysis of time series with temporally correlated errors
NASA Astrophysics Data System (ADS)
Langbein, John
2017-02-01
Most time series of geophysical phenomena have temporally correlated errors. From these measurements, various parameters are estimated. For instance, from geodetic measurements of positions, the rates and changes in rates are often estimated and are used to model tectonic processes. Along with the estimates of the size of the parameters, the error in these parameters needs to be assessed. If temporal correlations are not taken into account, or each observation is assumed to be independent, it is likely that any estimate of the error of these parameters will be too low and the estimated value of the parameter will be biased. Inclusion of 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 for cases where 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/f^{α } with frequency, f. With missing data, standard spectral techniques involving FFTs are not appropriate. Instead, time domain techniques involving construction and inversion of large data covariance matrices are employed. Bos et al. (J Geod, 2013. doi: 10.1007/s00190-012-0605-0) demonstrate one technique that substantially increases the efficiency of the MLE methods, yet is only an approximate solution for power-law indices >1.0 since they require the data covariance matrix to be Toeplitz. 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 yet provides robust results for a wider range of power-law indices.
Grootswagers, Tijl; Wardle, Susan G; Carlson, Thomas A
2017-04-01
Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analyzing fMRI data. Although decoding methods have been extensively applied in brain-computer interfaces, these methods have only recently been applied to time series neuroimaging data such as MEG and EEG to address experimental questions in cognitive neuroscience. In a tutorial style review, we describe a broad set of options to inform future time series decoding studies from a cognitive neuroscience perspective. Using example MEG data, we illustrate the effects that different options in the decoding analysis pipeline can have on experimental results where the aim is to "decode" different perceptual stimuli or cognitive states over time from dynamic brain activation patterns. We show that decisions made at both preprocessing (e.g., dimensionality reduction, subsampling, trial averaging) and decoding (e.g., classifier selection, cross-validation design) stages of the analysis can significantly affect the results. In addition to standard decoding, we describe extensions to MVPA for time-varying neuroimaging data including representational similarity analysis, temporal generalization, and the interpretation of classifier weight maps. Finally, we outline important caveats in the design and interpretation of time series decoding experiments.
Time-series analysis to study the impact of an intersection on dispersion along a street canyon.
Richmond-Bryant, Jennifer; Eisner, Alfred D; Hahn, Intaek; Fortune, Christopher R; Drake-Richman, Zora E; Brixey, Laurie A; Talih, M; Wiener, Russell W; Ellenson, William D
2009-12-01
This paper presents data analysis from the Brooklyn Traffic Real-Time Ambient Pollutant Penetration and Environmental Dispersion (B-TRAPPED) study to assess the transport of ultrafine particulate matter (PM) across urban intersections. Experiments were performed in a street canyon perpendicular to a highway in Brooklyn, NY, USA. Real-time ultrafine PM samplers were positioned on either side of an intersection at multiple locations along a street to collect time-series number concentration data. Meteorology equipment was positioned within the street canyon and at an upstream background site to measure wind speed and direction. Time-series analysis was performed on the PM data to compute a transport velocity along the direction of the street for the cases where background winds were parallel and perpendicular to the street. The data were analyzed for sampler pairs located (1) on opposite sides of the intersection and (2) on the same block. The time-series analysis demonstrated along-street transport, including across the intersection when background winds were parallel to the street canyon and there was minimal transport and no communication across the intersection when background winds were perpendicular to the street canyon. Low but significant values of the cross-correlation function (CCF) underscore the turbulent nature of plume transport along the street canyon. The low correlations suggest that flow switching around corners or traffic-induced turbulence at the intersection may have aided dilution of the PM plume from the highway. This observation supports similar findings in the literature. Furthermore, the time-series analysis methodology applied in this study is introduced as a technique for studying spatiotemporal variation in the urban microscale environment.
NASA Astrophysics Data System (ADS)
Satoh, Y.; Yoshimura, K.; Pokhrel, Y. N.; KIM, H.; Oki, T.
2014-12-01
Human society have altered terrestrial hydrological cycles by water management infrastructure, such as reservoirs and weirs for irrigation, in order to enable stable water use against natural variability. On the other hand, anthropogenic climate change is projected to alter the hydro-meteorological cycles, and it is projected that drought frequency and/or intensity will increase in some regions. Thus reliable projection is a critical issue for our society in order to adapt for the change. However, only few studies have investigated the effect of anthropogenic intervention on drought under climate change. This study focuses on hydrological drought, particularly on stream flow, as stream flow is one of the most easy-to-access water resource. HiGW-MAT, a state of arts land surface model capable to reproduce energy and water cycle considering the anthropogenic water management, is used to simulate the historical and future terrestrial water cycles. The model includes reservoir operation, water withdrawal and irrigation process. Five CMIP5 GCM outputs with bias-correction provided by ISI-MIP for 1980-2099 are used to force a set of simulations. Time series data of global hydrological drought for 120 years, with and without human activity, is analyzed in order to estimate the impact of climate change and the adaptation capacity of anthropogenic water management. It is identified that Europe, Central and Eastern Asia, East and West part of USA, Chile, Amazon basin and Congo basin will have large increases of drought more than 90 days. According to uncertainty check particular increases in Central USA and Southern and Eastern South America have high robustness. Dividing global land into 26 regions, we characterized the variation of drought time series for each region. Drought does not show abrupt change and show almost linear increase in many regions. Also, it is found that human activity effectively reduces the increasing rate and suppresses the natural variability under
NASA Astrophysics Data System (ADS)
Cho, S.; Woo, N. C.; Lee, J. M.
2015-12-01
This study is aimed at developing process to analyze and predict groundwater drought potentials for Winter and Spring droughts in Korea using a long-term groundwater monitoring data. So far, most drought researches have been focused on precipitation and stream-flow data, although these data are considered to be non-linear. Subsequently, the prediction of drought events has been very difficult in practice. In this study, we targets to analyze the groundwater system as an intermediate stage between precipitation and stream-flow, but still has semi-linear characteristics. By the analysis of past trends of groundwater time-series compared with drought events, we will identify characteristics of fluctuation between groundwater-level and precipitation of the year before the droughts. Then, the characteristics will be tested with recent drought events in Korea. For this analysis, The updated ATGT (Analysis Tool for Groundwater Time-series data program version 1.0 based on JAVA), that was developed for analyzing and presenting groundwater time-series data, basically to identify abnormal changes in groundwater fluctuations, will be presented with additional functions including cross-correlation between groundwater and drought based on the PYTHON language.
NASA Astrophysics Data System (ADS)
Goela, Priscila Costa; Cordeiro, Clara; Danchenko, Sergei; Icely, John; Cristina, Sónia; Newton, Alice
2016-11-01
This study relates sea surface temperature (SST) to the upwelling conditions off the southwest coast of Portugal using statistical analyses of publically available data. Optimum Interpolation (OI) of daily SST data were extracted from the United States (US) National Oceanic and Atmospheric Administration (NOAA) and data for wind speed and direction were from the US National Climatic Data Center. Time series were extracted at a daily frequency for a time horizon of 26 years. Upwelling indices were estimated using westerly (Qx) and southerly (Qy) Ekman transport components. In the first part of the study, time series were inspected for trend and seasonality over the whole period. The seasonally adjusted time series revealed an increasing slope for SST (0.15 °C per decade) and decreasing slopes for Qx (- 84.01 m3 s- 1 km- 1 per decade) and Qy (- 25.20 m3 s- 1 km- 1 per decade), over the time horizon. Structural breaks analysis applied to the time series showed that a statistically significant incremental increase in SST was more pronounced during the last decade. Cross-correlation between upwelling indices and SST revealed a time delay of 5 and 2 days between Qx and SST, and between Qy and SST, respectively. A spectral analysis combined with the previous analysis enabled the identification of four oceanographic seasons. Those seasons were later recognised over a restricted time period of 4 years, between 2008 and 2012, when there was an extensive sampling programme for the validation of ocean colour remote sensing imagery. The seasons were defined as: summer, with intense and regular events of upwelling; autumn, indicating relaxation of upwelling conditions; and spring and winter, showing high interannual variability in terms of number and intensity of upwelling events.
Hogan, Alexandra B; Anderssen, Robert S; Davis, Stephanie; Moore, Hannah C; Lim, Faye J; Fathima, Parveen; Glass, Kathryn
2016-09-01
Respiratory syncytial virus (RSV) causes respiratory illness in young children and is most commonly associated with bronchiolitis. RSV typically occurs as annual or biennial winter epidemics in temperate regions, with less pronounced seasonality in the tropics. We sought to characterise and compare the seasonality of RSV and bronchiolitis in temperate and tropical Western Australia. We examined over 13 years of RSV laboratory identifications and bronchiolitis hospitalisations in children, using an extensive linked dataset from Western Australia. We applied mathematical time series analyses to identify the dominant seasonal cycle, and changes in epidemic size and timing over this period. Both the RSV and bronchiolitis data showed clear winter epidemic peaks in July or August in the southern Western Australia regions, but less identifiable seasonality in the northern regions. Use of complex demodulation proved very effective at comparing disease epidemics. The timing of RSV and bronchiolitis epidemics coincided well, but the size of the epidemics differed, with more consistent peak sizes for bronchiolitis than for RSV. Our results show that bronchiolitis hospitalisations are a reasonable proxy for the timing of RSV detections, but may not fully capture the magnitude of RSV epidemics.
Overland Flow Analysis Using Time Series of Suas-Derived Elevation Models
NASA Astrophysics Data System (ADS)
Jeziorska, J.; Mitasova, H.; Petrasova, A.; Petras, V.; Divakaran, D.; Zajkowski, T.
2016-06-01
With the advent of the innovative techniques for generating high temporal and spatial resolution terrain models from Unmanned Aerial Systems (UAS) imagery, it has become possible to precisely map overland flow patterns. Furthermore, the process has become more affordable and efficient through the coupling of small UAS (sUAS) that are easily deployed with Structure from Motion (SfM) algorithms that can efficiently derive 3D data from RGB imagery captured with consumer grade cameras. We propose applying the robust overland flow algorithm based on the path sampling technique for mapping flow paths in the arable land on a small test site in Raleigh, North Carolina. By comparing a time series of five flights in 2015 with the results of a simulation based on the most recent lidar derived DEM (2013), we show that the sUAS based data is suitable for overland flow predictions and has several advantages over the lidar data. The sUAS based data captures preferential flow along tillage and more accurately represents gullies. Furthermore the simulated water flow patterns over the sUAS based terrain models are consistent throughout the year. When terrain models are reconstructed only from sUAS captured RGB imagery, however, water flow modeling is only appropriate in areas with sparse or no vegetation cover.
Time-series Analysis of Broadband Photometry of Neptune from K2
NASA Astrophysics Data System (ADS)
Rowe, Jason F.; Gaulme, Patrick; Lissauer, Jack J.; Marley, Mark S.; Simon, Amy A.; Hammel, Heidi B.; Silva Aguirre, Víctor; Barclay, Thomas; Benomar, Othman; Boumier, Patrick; Caldwell, Douglas A.; Casewell, Sarah L.; Chaplin, William J.; Colón, Knicole D.; Corsaro, Enrico; Davies, G. R.; Fortney, Jonathan J.; Garcia, Rafael A.; Gizis, John E.; Haas, Michael R.; Mosser, Benoît; Schmider, François-Xavier
2017-04-01
We report here on our search for excess power in photometry of Neptune collected by the K2 mission that may be due to intrinsic global oscillations of the planet Neptune. To conduct this search, we developed new methods to correct for instrumental effects such as intrapixel variability and gain variations. We then extracted and analyzed the time-series photometry of Neptune from 49 days of nearly continuous broadband photometry of the planet. We find no evidence of global oscillations and place an upper limit of ∼5 ppm at 1000 μ {Hz} for the detection of a coherent signal. With an observed cadence of 1 minute and a point-to-point scatter of less than 0.01%, the photometric signal is dominated by reflected light from the Sun, which is in turn modulated by atmospheric variability of Neptune at the 2% level. A change in flux is also observed due to the increasing distance between Neptune and the K2 spacecraft and the solar variability with convection-driven solar p modes present.
Kim, Yongwook Bryce; O'Reilly, Una-May
2016-08-01
We apply the sublinear time, scalable locality-sensitive hashing (LSH) and majority discrimination to the problem of predicting critical events based on physiological waveform time series. Compared to using the linear exhaustive k-nearest neighbor search, our proposed method vastly speeds up prediction time up to 25 times while sacrificing only 1% of accuracy when demonstrated on an arterial blood pressure dataset extracted from the MIMIC2 database. We compare two widely used variants of LSH, the bit sampling based (L1LSH) and the random projection based (E2LSH) methods to measure their direct impact on retrieval and prediction accuracy. We experimentally show that the more sophisticated E2LSH performs worse than L1LSH in terms of accuracy, correlation, and the ability to detect false negatives. We attribute this to E2LSH's simultaneous integration of all dimensions when hashing the data, which actually makes it more impotent against common noise sources such as data misalignment. We also demonstrate that the deterioration of accuracy due to approximation at the retrieval step of LSH has a diminishing impact on the prediction accuracy as the speed up gain accelerates.
Time-series analysis of six whale-fall communities in Monterey Canyon, California, USA
NASA Astrophysics Data System (ADS)
Lundsten, Lonny; Schlining, Kyra L.; Frasier, Kaitlin; Johnson, Shannon B.; Kuhnz, Linda A.; Harvey, Julio B. J.; Clague, Gillian; Vrijenhoek, Robert C.
2010-12-01
Dead whale carcasses that sink to the deep seafloor introduce a massive pulse of energy capable of hosting dynamic communities of organisms in an otherwise food-limited environment. Through long-term observations of one natural and five implanted whale carcasses in Monterey Canyon, CA, this study suggests that: (1) depth and related physical conditions play a crucial role in species composition; (2) the majority of species in these communities are background deep-sea taxa; and (3) carcass degradation occurs sub-decadally. Remotely operated vehicles (ROVs) equipped with studio quality video cameras were used to survey whales during 0.8 to seven year periods, depending on the carcass. All organisms were identified to the lowest possible taxon. Community differences among whale-falls seemed to be most strongly related to depth and water temperature. The communities changed significantly from initial establishment shortly after a carcass' arrival at the seafloor through multiple years of steady degradation. The majority of species found at the whale-falls were background taxa commonly seen in Monterey Bay. While populations of species characterized as bone specialists, seep restricted, and of unknown habitat affinities were also observed, sometimes in great abundance, they contributed minimally to overall species richness. All whale carcasses, shallow and deep, exhibited sub-decadal degradation and a time-series of mosaic images at the deepest whale site illustrates the rapidity at which the carcasses degrade.
Effect of weather variability on the incidence of mumps in children: a time-series analysis.
Onozuka, D; Hashizume, M
2011-11-01
The increasing international interest in the potential health effects of climate change has emphasized the importance of investigations into the relationship between weather variability and infectious diseases. However, few studies have examined the impact of weather variability on mumps in children, despite the fact that children are considered particularly vulnerable to climate change. We acquired data about cases of mumps in children aged <15 years and weather variability in Fukuoka, Japan from 2000 to 2008, and then used time-series analyses to assess how weather variability affected mumps cases, adjusting for seasonal variations, inter-annual variations, and temporal variations of two large epidemics in 2001 and 2004-2005. The weekly number of mumps cases increased by 7·5% (95% CI 4·0-11·1) for every 1°C increase in average temperature and by 1·4% (95% CI 0·5-2·4) for every 1% increase in relative humidity. The percentage increase was greatest in the 0-4 years age group and tended to decrease with increasing age. The number of mumps cases in children increased significantly with increased average temperature and relative humidity.
New Ground Truth Capability from InSAR Time Series Analysis
Buckley, S; Vincent, P; Yang, D
2005-07-13
We demonstrate that next-generation interferometric synthetic aperture radar (InSAR) processing techniques applied to existing data provide rich InSAR ground truth content for exploitation in seismic source identification. InSAR time series analyses utilize tens of interferograms and can be implemented in different ways. In one such approach, conventional InSAR displacement maps are inverted in a final post-processing step. Alternatively, computationally intensive data reduction can be performed with specialized InSAR processing algorithms. The typical final result of these approaches is a synthesized set of cumulative displacement maps. Examples from our recent work demonstrate that these InSAR processing techniques can provide appealing new ground truth capabilities. We construct movies showing the areal and temporal evolution of deformation associated with previous nuclear tests. In other analyses, we extract time histories of centimeter-scale surface displacement associated with tunneling. The potential exists to identify millimeter per year surface movements when sufficient data exists for InSAR techniques to isolate and remove phase signatures associated with digital elevation model errors and the atmosphere.
On the embedding-dimension analysis of AE and AL time series
NASA Technical Reports Server (NTRS)
Shan, Lin-Hua; Goertz, Christoph; Smith, Robert A.
1991-01-01
Several authors have employed the embedding-dimension method to analyze time series of geomagnetic indices, with differing results for the value of the correlation dimension nu. It is argued that these differences may arise from corresponding differences in the length and construction of the various data sets used. Practical application of the method to sets of discretized data requires use of a delay time scale set by the autocorrelation time of the data set. It is found that a particular data set containing 35 days of AE exhibits an autocorrelation time tau(c) longer by an order of magnitude than that of a short-duration (less than 5 days) set, raising the possibility that extant analyses of long-duration sets may have employed delay times shorter than tau(c). In addition, the power spectrum of AE reveals modulation at a period of 24 hr. A numerical experiment on the logistic map shows that such modulation introduces an extra degree of freedom in the data, resulting in an augmented correlation dimension.
Reservoir computing and extreme learning machines for non-linear time-series data analysis.
Butcher, J B; Verstraeten, D; Schrauwen, B; Day, C R; Haycock, P W
2013-02-01
Random projection architectures such as Echo state networks (ESNs) and Extreme Learning Machines (ELMs) use a network containing a randomly connected hidden layer and train only the output weights, overcoming the problems associated with the complex and computationally demanding training algorithms traditionally used to train neural networks, particularly recurrent neural networks. In this study an ESN is shown to contain an antagonistic trade-off between the amount of non-linear mapping and short-term memory it can exhibit when applied to time-series data which are highly non-linear. To overcome this trade-off a new architecture, Reservoir with Random Static Projections (R(2)SP) is investigated, that is shown to offer a significant improvement in performance. A similar approach using an ELM whose input is presented through a time delay (TD-ELM) is shown to further enhance performance where it significantly outperformed the ESN and R(2)SP as well other architectures when applied to a novel task which allows the short-term memory and non-linearity to be varied. The hard-limiting memory of the TD-ELM appears to be best suited for the data investigated in this study, although ESN-based approaches may offer improved performance when processing data which require a longer fading memory.
Sun, Bruce Qiang; Zhang, Jie
2016-03-01
For the effects of social integration on suicides, there have been different and even contradictive conclusions. In this study, the selected economic and social risks of suicide for different age groups and genders in the United Kingdom were identified and the effects were estimated by the multilevel time series analyses. To our knowledge, there exist no previous studies that estimated a dynamic model of suicides on the time series data together with multilevel analysis and autoregressive distributed lags. The investigation indicated that unemployment rate, inflation rate, and divorce rate are all significantly and positively related to the national suicide rates in the United Kingdom from 1981 to 2011. Furthermore, the suicide rates of almost all groups above 40 years are significantly associated with the risk factors of unemployment and inflation rate, in comparison with the younger groups.
Time-Series Analysis of Remotely-Sensed SeaWiFS Chlorophyll in River-Influenced Coastal Regions
NASA Technical Reports Server (NTRS)
Acker, James G.; McMahon, Erin; Shen, Suhung; Hearty, Thomas; Casey, Nancy
2009-01-01
The availability of a nearly-continuous record of remotely-sensed chlorophyll a data (chl a) from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) mission, now longer than ten years, enables examination of time-series trends for multiple global locations. Innovative data analysis technology available on the World Wide Web facilitates such analyses. In coastal regions influenced by river outflows, chl a is not always indicative of actual trends in phytoplankton chlorophyll due to the interference of colored dissolved organic matter and suspended sediments; significant chl a timeseries trends for coastal regions influenced by river outflows may nonetheless be indicative of important alterations of the hydrologic and coastal environment. Chl a time-series analysis of nine marine regions influenced by river outflows demonstrates the simplicity and usefulness of this technique. The analyses indicate that coastal time-series are significantly influenced by unusual flood events. Major river systems in regions with relatively low human impact did not exhibit significant trends. Most river systems with demonstrated human impact exhibited significant negative trends, with the noteworthy exception of the Pearl River in China, which has a positive trend.
Toledo, B A; Chian, A C-L; Rempel, E L; Miranda, R A; Muñoz, P R; Valdivia, J A
2013-02-01
We study general multifractal properties of tidal gauge and long-wave time series which show a well defined transition between two states, as is the case of sea level when a tsunami arrives. We adopt a method based on discrete wavelets, called wavelet leaders, which has been successfully used in a wide range of applications from image analysis to biomedical signals. First, we analyze an empirical time series of tidal gauge from the tsunami event of 27 February 2010 in Chile. Then, we study a numerical solution of the driven-damped regularized long-wave equation (RLWE) which displays on-off intermittency. Both time series are characterized by a sudden change between two sharply distinct dynamical states. Our analysis suggests a correspondence between the pre- and post-tsunami states (ocean background) and the on state in the RLWE, and also between the tsunami state (disturbed ocean) and the off state in the RLWE. A qualitative similarity in their singularity spectra is observed, and since the RLWE is used to model shallow water dynamics, this result could imply an underlying dynamical similarity.
Daily Mean Temperature Affects Urolithiasis Presentation in Seoul: a Time-series Analysis.
Lee, SeoYeon; Kim, Min-Su; Kim, Jung Hoon; Kwon, Jong Kyou; Chi, Byung Hoon; Kim, Jin Wook; Chang, In Ho
2016-05-01
This study aimed to investigate the overall cumulative exposure-response and the lag response relationships between daily temperature and urolithiasis presentation in Seoul. Using a time-series design and distributing lag nonlinear methods, we estimated the relative risk (RR) of urolithiasis presentation associated with mean daily temperature, including the cumulative RR for a 20 days period, and RR for individual daily lag through 20 days. We analyzed data from 14,518 patients of 4 hospitals emergency department who sought medical evaluation or treatment of urolithiasis from 2005-2013 in Seoul. RR was estimated according to sex and age. Associations between mean daily temperature and urolithiasis presentation were not monotonic. Furthermore, there was variation in the exposure-response curve shapes and the strength of association at different temperatures, although in most cases RRs increased for temperatures above the 13°C reference value. The RRs for urolothiasis at 29°C vs. 13°C were 2.54 in all patients (95% confidence interval [CI]: 1.67-3.87), 2.59 in male (95% CI, 1.56-4.32), 2.42 in female (95% CI, 1.15-5.07), 3.83 in male less than 40 years old (95% CI, 1.78-8.26), and 2.47 in male between 40 and 60 years old (95% CI, 1.15-5.34). Consistent trends of increasing RR of urolithiasis presentation were observed within 5 days of high temperatures across all groups. Urolithiasis presentation increased with high temperature with higher daily mean temperatures, with the strongest associations estimated for lags of only a few days, in Seoul, a metropolitan city in Korea.
Risk assessment of dengue fever in Zhongshan, China: a time-series regression tree analysis.
Liu, K-K; Wang, T; Huang, X-D; Wang, G-L; Xia, Y; Zhang, Y-T; Jing, Q-L; Huang, J-W; Liu, X-X; Lu, J-H; Hu, W-B
2017-02-01
Dengue fever (DF) is the most prevalent and rapidly spreading mosquito-borne disease globally. Control of DF is limited by barriers to vector control and integrated management approaches. This study aimed to explore the potential risk factors for autochthonous DF transmission and to estimate the threshold effects of high-order interactions among risk factors. A time-series regression tree model was applied to estimate the hierarchical relationship between reported autochthonous DF cases and the potential risk factors including the timeliness of DF surveillance systems (median time interval between symptom onset date and diagnosis date, MTIOD), mosquito density, imported cases and meteorological factors in Zhongshan, China from 2001 to 2013. We found that MTIOD was the most influential factor in autochthonous DF transmission. Monthly autochthonous DF incidence rate increased by 36·02-fold [relative risk (RR) 36·02, 95% confidence interval (CI) 25·26-46·78, compared to the average DF incidence rate during the study period] when the 2-month lagged moving average of MTIOD was >4·15 days and the 3-month lagged moving average of the mean Breteau Index (BI) was ⩾16·57. If the 2-month lagged moving average MTIOD was between 1·11 and 4·15 days and the monthly maximum diurnal temperature range at a lag of 1 month was <9·6 °C, the monthly mean autochthonous DF incidence rate increased by 14·67-fold (RR 14·67, 95% CI 8·84-20·51, compared to the average DF incidence rate during the study period). This study demonstrates that the timeliness of DF surveillance systems, mosquito density and diurnal temperature range play critical roles in the autochthonous DF transmission in Zhongshan. Better assessment and prediction of the risk of DF transmission is beneficial for establishing scientific strategies for DF early warning surveillance and control.
Daily Mean Temperature Affects Urolithiasis Presentation in Seoul: a Time-series Analysis
2016-01-01
This study aimed to investigate the overall cumulative exposure-response and the lag response relationships between daily temperature and urolithiasis presentation in Seoul. Using a time-series design and distributing lag nonlinear methods, we estimated the relative risk (RR) of urolithiasis presentation associated with mean daily temperature, including the cumulative RR for a 20 days period, and RR for individual daily lag through 20 days. We analyzed data from 14,518 patients of 4 hospitals emergency department who sought medical evaluation or treatment of urolithiasis from 2005-2013 in Seoul. RR was estimated according to sex and age. Associations between mean daily temperature and urolithiasis presentation were not monotonic. Furthermore, there was variation in the exposure-response curve shapes and the strength of association at different temperatures, although in most cases RRs increased for temperatures above the 13°C reference value. The RRs for urolothiasis at 29°C vs. 13°C were 2.54 in all patients (95% confidence interval [CI]: 1.67-3.87), 2.59 in male (95% CI, 1.56-4.32), 2.42 in female (95% CI, 1.15-5.07), 3.83 in male less than 40 years old (95% CI, 1.78-8.26), and 2.47 in male between 40 and 60 years old (95% CI, 1.15-5.34). Consistent trends of increasing RR of urolithiasis presentation were observed within 5 days of high temperatures across all groups. Urolithiasis presentation increased with high temperature with higher daily mean temperatures, with the strongest associations estimated for lags of only a few days, in Seoul, a metropolitan city in Korea. PMID:27134497
Time-series analysis of weather and mortality patterns in Nairobi's informal settlements
Egondi, Thaddaeus; Kyobutungi, Catherine; Kovats, Sari; Muindi, Kanyiva; Ettarh, Remare; Rocklöv, Joacim
2012-01-01
Background Many studies have established a link between weather (primarily temperature) and daily mortality in developed countries. However, little is known about this relationship in urban populations in sub-Saharan Africa. Objectives The objective of this study was to describe the relationship between daily weather and mortality in Nairobi, Kenya, and to evaluate this relationship with regard to cause of death, age, and sex. Methods We utilized mortality data from the Nairobi Urban Health and Demographic Surveillance System and applied time-series models to study the relationship between daily weather and mortality for a population of approximately 60,000 during the period 2003–2008. We used a distributed lag approach to model the delayed effect of weather on mortality, stratified by cause of death, age, and sex. Results Increasing temperatures (above 75th percentile) were significantly associated with mortality in children and non-communicable disease (NCD) deaths. We found all-cause mortality of shorter lag of same day and previous day to increase by 3.0% for a 1 degree decrease from the 25th percentile of 18°C (not statistically significant). Mortality among people aged 50+ and children aged below 5 years appeared most susceptible to cold compared to other age groups. Rainfall, in the lag period of 0–29 days, increased all-cause mortality in general, but was found strongest related to mortality among females. Low temperatures were associated with deaths due to acute infections, whereas rainfall was associated with all-cause pneumonia and NCD deaths. Conclusions Increases in mortality were associated with both hot and cold weather as well as rainfall in Nairobi, but the relationship differed with regard to age, sex, and cause of death. Our findings indicate that weather-related mortality is a public health concern for the population in the informal settlements of Nairobi, Kenya, especially if current trends in climate change continue. PMID:23195509
Time Series Analysis of Meteorological Factors Influencing Malaria in South Eastern Iran
Ostovar, Afshin; Haghdoost, Ali Akbar; Rahimiforoushani, Abbas; Raeisi, Ahmad; Majdzadeh, Reza
2016-01-01
Background: The Malaria Early Warning System is defined as the use of prognostic variables for predicting the occurrence of malaria epidemics several months in advance. The principal objective of this study was to provide a malaria prediction model by using meteorological variables and historical malaria morbidity data for malaria-endemic areas in south eastern Iran. Methods: A total of 2002 locally transmitted microscopically confirmed malaria cases, which occurred in the Minab district of Hormozgan Province in Iran over a period of 6 years from March 2003 to March 2009, were analysed. Meteorological variables (the rainfall, temperature, and relative humidity in this district) were also assessed. Monthly and weekly autocorrelation functions, partial autocorrelation functions, and cross-correlation graphs were examined to explore the relationship between the historical morbidity data and meteorological variables and the number of cases of malaria. Having used univariate auto-regressive integrated moving average or transfer function models, significant predictors among the meteorological variables were selected to predict the number of monthly and weekly malaria cases. Ljung-Box statistics and stationary R-squared were used for model diagnosis and model fit, respectively. Results: The weekly model had a better fit (R2= 0.863) than the monthly model (R2= 0.424). However, the Ljung-Box statistic was significant for the weekly model. In addition to autocorrelations, meteorological variables were not significant, except for different orders of maximum and minimum temperatures in the monthly model. Conclusions: Time-series models can be used to predict malaria incidence with acceptable accuracy in a malaria early-warning system. The applicability of using routine meteorological data in statistical models is seriously limited. PMID:27308280
Changes in the use of broad-spectrum antibiotics after cefepime shortage: a time series analysis.
Plüss-Suard, C; Pannatier, A; Ruffieux, C; Kronenberg, A; Mühlemann, K; Zanetti, G
2012-02-01
The original cefepime product was withdrawn from the Swiss market in January 2007 and replaced by a generic 10 months later. The goals of the study were to assess the impact of this cefepime shortage on the use and costs of alternative broad-spectrum antibiotics, on antibiotic policy, and on resistance of Pseudomonas aeruginosa toward carbapenems, ceftazidime, and piperacillin-tazobactam. A generalized regression-based interrupted time series model assessed how much the shortage changed the monthly use and costs of cefepime and of selected alternative broad-spectrum antibiotics (ceftazidime, imipenem-cilastatin, meropenem, piperacillin-tazobactam) in 15 Swiss acute care hospitals from January 2005 to December 2008. Resistance of P. aeruginosa was compared before and after the cefepime shortage. There was a statistically significant increase in the consumption of piperacillin-tazobactam in hospitals with definitive interruption of cefepime supply and of meropenem in hospitals with transient interruption of cefepime supply. Consumption of each alternative antibiotic tended to increase during the cefepime shortage and to decrease when the cefepime generic was released. These shifts were associated with significantly higher overall costs. There was no significant change in hospitals with uninterrupted cefepime supply. The alternative antibiotics for which an increase in consumption showed the strongest association with a progression of resistance were the carbapenems. The use of alternative antibiotics after cefepime withdrawal was associated with a significant increase in piperacillin-tazobactam and meropenem use and in overall costs and with a decrease in susceptibility of P. aeruginosa in hospitals. This warrants caution with regard to shortages and withdrawals of antibiotics.
Zhou, Jiang; Ito, Kazuhiko; Lall, Ramona; Lippmann, Morton; Thurston, George
2011-01-01
Background Recent toxicological and epidemiological studies have shown associations between particulate matter (PM) and adverse health effects, but which PM components are most influential is less well known. Objectives In this study, we used time-series analyses to determine the associations between daily fine PM [PM ≤ 2.5 μm in aerodynamic diameter (PM2.5)] concentrations and daily mortality in two U.S. cities—Seattle, Washington, and Detroit, Michigan. Methods We obtained daily PM2.5 filters for the years of 2002–2004 and analyzed trace elements using X-ray fluorescence and black carbon using light reflectance as a surrogate measure of elemental carbon. We used Poisson regression and distributed lag models to estimate excess deaths for all causes and for cardiovascular and respiratory diseases adjusting for time-varying covariates. We computed the excess risks for interquartile range increases of each pollutant at lags of 0 through 3 days for both warm and cold seasons. Results The cardiovascular and respiratory mortality series exhibited different source and seasonal patterns in each city. The PM2.5 components and gaseous pollutants associated with mortality in Detroit were most associated with warm season secondary aerosols and traffic markers. In Seattle, the component species most closely associated with mortality included those for cold season traffic and other combustion sources, such as residual oil and wood burning. Conclusions The effects of PM2.5 on daily mortality vary with source, season, and locale, consistent with the hypothesis that PM composition has an appreciable influence on the health effects attributable to PM. PMID:21193387
NASA Astrophysics Data System (ADS)
Feigin, A. M.; Mukhin, D.; Volodin, E. M.; Gavrilov, A.; Loskutov, E. M.
2013-12-01
The new method of decomposition of the Earth's climate system into well separated spatial-temporal patterns ('climatic modes') is discussed. The method is based on: (i) generalization of the MSSA (Multichannel Singular Spectral Analysis) [1] for expanding vector (space-distributed) time series in basis of spatial-temporal empirical orthogonal functions (STEOF), which makes allowance delayed correlations of the processes recorded in spatially separated points; (ii) expanding both real SST data, and longer by several times SST data generated numerically, in STEOF basis; (iii) use of the numerically produced STEOF basis for exclusion of 'too slow' (and thus not represented correctly) processes from real data. The application of the method allows by means of vector time series generated numerically by the INM RAS Coupled Climate Model [2] to separate from real SST anomalies data [3] two climatic modes possessing by noticeably different time scales: 3-5 and 9-11 years. Relations of separated modes to ENSO and PDO are investigated. Possible applications of spatial-temporal climatic patterns concept to prognosis of climate system evolution is discussed. 1. Ghil, M., R. M. Allen, M. D. Dettinger, K. Ide, D. Kondrashov, et al. (2002) "Advanced spectral methods for climatic time series", Rev. Geophys. 40(1), 3.1-3.41. 2. http://83.149.207.89/GCM_DATA_PLOTTING/GCM_INM_DATA_XY_en.htm 3. http://iridl.ldeo.columbia.edu/SOURCES/.KAPLAN/.EXTENDED/.v2/.ssta/
NASA Astrophysics Data System (ADS)
Hackl, M.; Malservisi, R.; Hugentobler, U.; Wonnacott, R.
2011-11-01
We present a method to derive velocity uncertainties from GPS position time series that are affected by time-correlated noise. This method is based on the Allan variance, which is widely used in the estimation of oscillator stability and requires neither spectral analysis nor maximum likelihood estimation (MLE). The Allan variance of the rate (AVR) is calculated in the time domain and hence is not too sensitive to gaps in the time series. We derived analytical expressions of the AVR for different kinds of noises like power law noise, white noise, flicker noise, and random walk and found an expression for the variance produced by an annual signal. These functional relations form the basis of error models that have to be fitted to the AVR in order to estimate the velocity uncertainty. Finally, we applied the method to the South Africa GPS network TrigNet. Most time series show noise characteristics that can be modeled by a power law noise plus an annual signal. The method is computationally very cheap, and the results are in good agreement with the ones obtained by methods based on MLE.
NASA Astrophysics Data System (ADS)
Samsonov, S. V.; d'Oreye, N.; Gonzalez, P. J.; Tiampo, K. F.
2013-12-01
Modern Synthetic Aperture Radar (SAR) satellites and satellite constellations are capable of acquiring data at high spatial resolution and increasing temporal resolution allowing detection of ground deformation signals with a minimal delay. Advanced interferometric SAR (InSAR) processing techniques, such as Small Baseline Subset (SBAS) and Multidimensional Small Baseline Subset (MSBAS) are capable of producing time series of ground deformation with a very high sub-centimeter precision. Additionally MSBAS allows combination of various InSAR data into a single set of vertical and horizontal deformation time series further improving their temporal resolution and precision. Developed methodologies are ready for operational monitoring of natural and anthropogenic hazards, including landslides, volcanoes, earthquakes and tectonic motion and ground subsidence caused by mining and groundwater extraction. Here we present various case studies where an InSAR time series analysis was able to map ground deformation with superior resolution and precision, including mining subsidence in the Greater Luxembourg region and southern Saskatchewan, groundwater extraction related subsidence in the Greater Vancouver Region, volcanic deformation in the Virunga Volcanic Province, and tectonic deformation and landslide in northern California. Often, InSAR is the best cost-efficient solution with no restrictions on spatial coverage, weather or lighting condition and timing. It is anticipated that the use of SAR data for mapping hazards will increase in the future as data access improves.
Multiple Indicator Stationary Time Series Models.
ERIC Educational Resources Information Center
Sivo, Stephen A.
2001-01-01
Discusses the propriety and practical advantages of specifying multivariate time series models in the context of structural equation modeling for time series and longitudinal panel data. For time series data, the multiple indicator model specification improves on classical time series analysis. For panel data, the multiple indicator model…
NASA Astrophysics Data System (ADS)
Forootan, Ehsan; Kusche, Jürgen
2016-04-01
Geodetic/geophysical observations, such as the time series of global terrestrial water storage change or sea level and temperature change, represent samples of physical processes and therefore contain information about complex physical interactionswith many inherent time scales. Extracting relevant information from these samples, for example quantifying the seasonality of a physical process or its variability due to large-scale ocean-atmosphere interactions, is not possible by rendering simple time series approaches. In the last decades, decomposition techniques have found increasing interest for extracting patterns from geophysical observations. Traditionally, principal component analysis (PCA) and more recently independent component analysis (ICA) are common techniques to extract statistical orthogonal (uncorrelated) and independent modes that represent the maximum variance of observations, respectively. PCA and ICA can be classified as stationary signal decomposition techniques since they are based on decomposing the auto-covariance matrix or diagonalizing higher (than two)-order statistical tensors from centered time series. However, the stationary assumption is obviously not justifiable for many geophysical and climate variables even after removing cyclic components e.g., the seasonal cycles. In this paper, we present a new decomposition method, the complex independent component analysis (CICA, Forootan, PhD-2014), which can be applied to extract to non-stationary (changing in space and time) patterns from geophysical time series. Here, CICA is derived as an extension of real-valued ICA (Forootan and Kusche, JoG-2012), where we (i) define a new complex data set using a Hilbert transformation. The complex time series contain the observed values in their real part, and the temporal rate of variability in their imaginary part. (ii) An ICA algorithm based on diagonalization of fourth-order cumulants is then applied to decompose the new complex data set in (i
Trends in Statin Use in Seniors 1999 to 2013: Time Series Analysis
Corkum, Amber; Sketris, Ingrid; Fisher, Judith; Zhang, Ying; Saleh, Ahmed
2016-01-01
Purpose To examine HMG-CoA reductase inhibitor (statin) drug dispensing patterns to Nova Scotia Seniors' Pharmacare program (NSSPP) beneficiaries over a 14-year period in response to: 1) rosuvastatin market entry in 2003, 2) JUPITER trial publication in 2008, and 3) generic atorvastatin availability in 2010. Methods All NSSPP beneficiaries who redeemed at least one prescription for a statin from April 1, 1999 to March 31, 2013 were included. Aggregated, anonymous monthly prescription counts were extracted by the Nova Scotia Department of Health and Wellness (Nova Scotia, Canada) and changes in dispensing patterns of statins were measured. Data were analyzed using descriptive analyses and interrupted time series methods. Results The percentage of NSSPP beneficiaries dispensed any statin increased from 5.3% in April 1999 to 20.7% in March 2013. In 1999, most NSSPP beneficiaries were dispensed either simvastatin (29.5%) or atorvastatin (28.7%). When rosuvastatin was added to the NSSPP Formulary in August 2003, prescriptions dispensed for simvastatin, lovastatin, pravastatin, and fluvastatin declined significantly (slope change, -0.0027; 95% confidence interval (CI), (-0.0046, -0.0009)). This significant decline continued following the publication of JUPITER (level change, -0.1974; 95% CI, (-0.2991, -0.0957)) and the availability of generic atorvastatin (level change, -0.2436; 95% CI, (-0.3314, -0.1558)). Atorvastatin was not significantly affected by any of the three interventions, although it maintained an overall decreasing trend. Only upon the availability of generic atorvastatin did the upward trend in rosuvastatin use decrease significantly (slope change, -0.0010, 95% CI, (-0.0015, -0.0005)). Conclusions The type and rate of statins dispensed to NSSPP beneficiaries changed from 1999 to 2013 in response to the availability of new agents and publication of the JUPITER trial. The overall proportion of NSSPP beneficiaries dispensed a statin increased approximately 4
Analysis of time series of Cs-137 concentration in sewage sludge at Fukushima City
NASA Astrophysics Data System (ADS)
Fischer, Helmut W.; Mack, Majvor; Shikano, Yudai; Yokoo, Yoshiyuki
2015-04-01
Daily routine radioisotope measurements of sewage sludge at the sewage plant of Fukushima City starting in 2011 have provided a detailed data set for the isotopes Cs-137, Cs-134 and I-131. The long-term trend for the Cs isotopes is comparable to data sets from Central Europe caused by the Chernobyl emissions in 1986 - the average Cs-137 concentration decreases faster in the first year (T1/2 < 1 yr) and slower in later years (T1/2 > 1 yr). Absolute values at Fukushima City are comparably low (mostly below 1 kBq/kg dry mass), due to the existence of separate wastewater and rainwater sewer systems, with only a small portion of rainwater and erosion products reaching the purification plant. Cs-134 data decay faster due to the shorter radioactive half-life. I-131 appears even years after the NPP releases and is assumed to originate from the common medical usage of the isotope for thyroid treatment. Short-term Cs data show a clear dependence on rainfall: each significant rainfall event causes a concentration increase in sludge of up to a factor of ten. Therefore the time series exhibits high short-term variability. Here we attempt to numerically analyse the detailed Cs-137 data set, using two separate approaches: The first method tries to connect parameters like the local surface deposition density, surface types (sealed/unsealed), rainfall statistics, rainfall-induced erosion rate, leakage rate from rainwater to wastewater sewer, transport time in the sewer and residence time in the purification plant for a basically physical approach. As not all parameters are known, values have to be assumed or can be extracted in the course of the fitting process. The second approach is purely heuristic, based on a water surface runoff and transport model. Whilst there is no ad-hoc physical meaning in the extracted parameters, they can possibly be interpreted as such when compared with physical modeling results. The combination of both methods is expected to give a deeper insight
Training emergency services’ dispatchers to recognise stroke: an interrupted time-series analysis
2013-01-01
Background Stroke is a time-dependent medical emergency in which early presentation to specialist care reduces death and dependency. Up to 70% of all stroke patients obtain first medical contact from the Emergency Medical Services (EMS). Identifying ‘true stroke’ from an EMS call is challenging, with over 50% of strokes being misclassified. The aim of this study was to evaluate the impact of the training package on the recognition of stroke by Emergency Medical Dispatchers (EMDs). Methods This study took place in an ambulance service and a hospital in England using an interrupted time-series design. Suspected stroke patients were identified in one week blocks, every three weeks over an 18 month period, during which time the training was implemented. Patients were included if they had a diagnosis of stroke (EMS or hospital). The effect of the intervention on the accuracy of dispatch diagnosis was investigated using binomial (grouped) logistic regression. Results In the Pre-implementation period EMDs correctly identified 63% of stroke patients; this increased to 80% Post-implementation. This change was significant (p=0.003), reflecting an improvement in identifying stroke patients relative to the Pre-implementation period both the During-implementation (OR=4.10 [95% CI 1.58 to 10.66]) and Post-implementation (OR=2.30 [95% CI 1.07 to 4.92]) periods. For patients with a final diagnosis of stroke who had been dispatched as stroke there was a marginally non-significant 2.8 minutes (95% CI −0.2 to 5.9 minutes, p=0.068) reduction between Pre- and Post-implementation periods from call to arrival of the ambulance at scene. Conclusions This is the first study to develop, implement and evaluate the impact of a training package for EMDs with the aim of improving the recognition of stroke. Training led to a significant increase in the proportion of stroke patients dispatched as such by EMDs; a small reduction in time from call to arrival at scene by the ambulance also
Particulate air pollution and mortality in 38 of China's largest cities: time series analysis.
Yin, Peng; He, Guojun; Fan, Maoyong; Chiu, Kowk Yan; Fan, Maorong; Liu, Chang; Xue, An; Liu, Tong; Pan, Yuhang; Mu, Quan; Zhou, Maigeng
2017-03-14
Objectives To estimate the short term effect of particulate air pollution (particle diameter <10 μm, or PM10) on mortality and explore the heterogeneity of particulate air pollution effects in major cities in China.Design Generalised linear models with different lag structures using time series data.Setting 38 of the largest cities in 27 provinces of China (combined population >200 million).Participants 350 638 deaths (200 912 in males, 149 726 in females) recorded in 38 city districts by the Disease Surveillance Point System of the Chinese Center for Disease Control and Prevention from 1 January 2010 to 29 June 2013.Main outcome measure Daily numbers of deaths from all causes, cardiorespiratory diseases, and non-cardiorespiratory diseases and among different demographic groups were used to estimate the associations between particulate air pollution and mortality.Results A 10 µg/m(3) change in concurrent day PM10 concentrations was associated with a 0.44% (95% confidence interval 0.30% to 0.58%) increase in daily number of deaths. Previous day and two day lagged PM10 levels decreased in magnitude by one third and two thirds but remained statistically significantly associated with increased mortality. The estimate for the effect of PM10 on deaths from cardiorespiratory diseases was 0.62% (0.43% to 0.81%) per 10 µg/m(3) compared with 0.26% (0.09% to 0.42%) for other cause mortality. Exposure to PM10 had a greater impact on females than on males. Adults aged 60 and over were more vulnerable to particulate air pollution at high levels than those aged less than 60. The PM10 effect varied across different cities and marginally decreased in cities with higher PM10 concentrations.Conclusion Particulate air pollution has a greater impact on deaths from cardiorespiratory diseases than it does on other cause mortality. People aged 60 or more have a higher risk of death from particulate air pollution than people aged less than 60. The estimates of the effect
Onisko, Agnieszka; Druzdzel, Marek J.; Austin, R. Marshall
2016-01-01
Background: Classical statistics is a well-established approach in the analysis of medical data. While the medical community seems to be familiar with the concept of a statistical analysis and its interpretation, the Bayesian approach, argued by many of its proponents to be superior to the classical frequentist approach, is still not well-recognized in the analysis of medical data. Aim: The goal of this study is to encourage data analysts to use the Bayesian approach, such as modeling with graphical probabilistic networks, as an insightful alternative to classical statistical analysis of medical data. Materials and Methods: This paper offers a comparison of two approaches to analysis of medical time series data: (1) classical statistical approach, such as the Kaplan–Meier estimator and the Cox proportional hazards regression model, and (2) dynamic Bayesian network modeling. Our comparison is based on time series cervical cancer screening data collected at Magee-Womens Hospital, University of Pittsburgh Medical Center over 10 years. Results: The main outcomes of our comparison are cervical cancer risk assessments produced by the three approaches. However, our analysis discusses also several aspects of the comparison, such as modeling assumptions, model building, dealing with incomplete data, individualized risk assessment, results interpretation, and model validation. Conclusion: Our study shows that the Bayesian approach is (1) much more flexible in terms of modeling effort, and (2) it offers an individualized risk assessment, which is more cumbersome for classical statistical approaches. PMID:28163973
GPS Sensor Web Time Series Analysis Using SensorGrid Technology
NASA Astrophysics Data System (ADS)
Granat, R.; Pierce, M.; Aydin, G.; Qi, Z.
2006-12-01
We present a method for performing signal detection and classification on real-time streams of GPS sensor web data. Our approach has two parts. The first is a hidden Markov model fitting methodology that enables us to robustly describe the statistics of the data. The second is the SensorGrid technology which allows us to manage the data streams through a series of filters tied together with a publish/subscribe messaging system. In this framework, the HMM algorithm is viewed as a filter. The sensor web data we use in this work comes from the Southern California Integrated GPS Network (SCIGN), which produces a number of data products. In this work, we use the real-time (1Hz for most stations) three-dimensional position information. This data is collected from a system which is not only noisy but also poorly understood; driving forces on the system derive not only from the physical processes of the solid earth but also from external factors, including atmospheric effects and human activity. Fitting an HMM to time series allows us to describe the statistics of the data in a simple way that ascribes discrete modes of behavior to the system. By matching incoming data against the statistics of previously learned modes, we can perform classification according to the best match. In addition, we can perform signal detection across the entire sensor web by correlating mode changes in time; a significant number of mode changes across the network or within a certain sub-network is an indication of an event that is occurring over a wide geographical area. For most applications, reliable HMM fitting results are achieved by using a priori information to form constraints that reduce the number of free parameters. For GPS data, however, this information is not available as the underlying system is not well understood. As a result, we use the regularized deterministic annealing expectation-maximization (RDAEM) algorithm to perform the fit. This method provides high-quality, self
Langevin equations from time series.
Racca, E; Porporato, A
2005-02-01
We discuss the link between the approach to obtain the drift and diffusion of one-dimensional Langevin equations from time series, and Pope and Ching's relationship for stationary signals. The two approaches are based on different interpretations of conditional averages of the time derivatives of the time series at given levels. The analysis provides a useful indication for the correct application of Pope and Ching's relationship to obtain stochastic differential equations from time series and shows its validity, in a generalized sense, for nondifferentiable processes originating from Langevin equations.
NASA Astrophysics Data System (ADS)
Donges, J. F.; Donner, R. V.; Trauth, M. H.; Marwan, N.; Schellnhuber, H. J.; Kurths, J.
2012-04-01
The analysis of paleoclimate time series is usually affected by severe methodological problems, resulting primarily from non-equidistant sampling and uncertain age models. As an alternative to existing methods of time series analysis, the statistical properties of recurrence networks are promising candidates for characterizing a system's nonlinear dynamics and quantifying structural changes in its reconstructed phase space as time evolves. The results of recurrence network analysis are robust under changes in the age model and are not directly affected by non-equidistant sampling of the data. Specifically, we investigate three marine records of African climate variability during the Plio-Pleistocene. We detect several statistically significant dynamical transitions or tipping points and show that the obtained results are qualitatively robust under changes of the relevant parameters of our method, including detrending, size of the running window used for analysis, and embedding delay. Finally, relating the identified tipping points in paleoclimate-variability to speciation and extinction events in the available fossil record of human ancestors contributes to the understanding of climatic mechanisms driving human evolution in Africa during the past 5 million years.
NASA Astrophysics Data System (ADS)
Steinberg, D. K.; Madin, L. P.
2001-12-01
The structure of zooplankton communities has a significant impact on vertical transport and cycling of elements in the sea. Zooplankton play an integral role in the flux of material out of the euphotic zone at BATS via active transport by vertical migrators and by production of rapidly sinking fecal pellets. Zooplankton biomass in the upper 200 m at BATS is on average 1.7 times higher at night than day due to vertical migration. Migrating zooplankton actively transport a substantial amount of dissolved inorganic and organic carbon and nitrogen to deep water at BATS (via respiration and excretion), which can be significant relative to the passive flux of sinking particles. Active transport of C is equal to a mean of 8% (maximum 39%), and N equal to a mean of 13% (maximum 164%) of the gravitational vertical export of particulate organic C and N, respectively, measured with sediment traps at 150 m. Substantial excretion of dissolved organic material by migrators (mean of 24% of total C and 32% of total N metabolized) could be important to the microbial community at depth. Dissolved material exported by zooplankton is usually not at a Redfield C:N ratio of 6.6, contributing to non-Redfield remineralization patterns seen at depth. Changes in the zooplankton community can also dramatically affect the composition and sedimentation rate of fecal pellets, and thus the export of organic material. However, zooplankton biomass alone is not necessarily a good predictor of flux; the species composition of the resident community may at times more considerably affect export of organic material to the deep ocean. For example, there is a positive but weak relationship between monthly zooplankton biomass and organic C flux at BATS. Analysis of the bloom dynamics of salps (large gelatinous zooplankton) over the ten-year time series at BATS indicates salps graze on average 4% of the primary production, but fecal flux from salps can constitute on average 33% (maximum over 10-fold) of
NASA Astrophysics Data System (ADS)
Koeppen, W. C.; Wright, R.; Pilger, E.
2009-12-01
We developed and tested a new, automated algorithm, MODVOLC2, which analyzes thermal infrared satellite time series data to detect and quantify the excess energy radiated from thermal anomalies such as active volcanoes, fires, and gas flares. MODVOLC2 combines two previously developed algorithms, a simple point operation algorithm (MODVOLC) and a more complex time series analysis (Robust AVHRR Techniques, or RAT) to overcome the limitations of using each approach alone. MODVOLC2 has four main steps: (1) it uses the original MODVOLC algorithm to process the satellite data on a pixel-by-pixel basis and remove thermal outliers, (2) it uses the remaining data to calculate reference and variability images for each calendar month, (3) it compares the original satellite data and any newly acquired data to the reference images normalized by their variability, and it detects pixels that fall outside the envelope of normal thermal behavior, (4) it adds any pixels detected by MODVOLC to those detected in the time series analysis. Using test sites at Anatahan and Kilauea volcanoes, we show that MODVOLC2 was able to detect ~15% more thermal anomalies than using MODVOLC alone, with very few, if any, known false detections. Using gas flares from the Cantarell oil field in the Gulf of Mexico, we show that MODVOLC2 provided results that were unattainable using a time series-only approach. Some thermal anomalies (e.g., Cantarell oil field flares) are so persistent that an additional, semi-automated 12-µm correction must be applied in order to correctly estimate both the number of anomalies and the total excess radiance being emitted by them. Although all available data should be included to make the best possible reference and variability images necessary for the MODVOLC2, we estimate that at least 80 images per calendar month are required to generate relatively good statistics from which to run MODVOLC2, a condition now globally met by a decade of MODIS observations. We also found
NASA Astrophysics Data System (ADS)
van der Voort, Tessa Sophia; Hagedorn, Frank; Zell, Claudia; McIntyre, Cameron; Eglinton, Tim
2016-04-01
Understanding the interaction between soil organic matter (SOM) and climatic, geologic and ecological factors is essential for the understanding of potential susceptibility and vulnerability to climate and land use change. Radiocarbon constitutes a powerful tool for unraveling SOM dynamics and is increasingly used in studies of carbon turnover. The complex and inherently heterogeneous nature of SOM renders it challenging to assess the processes that govern SOM stability by solely looking at the bulk signature on a plot-scale level. This project combines bulk radiocarbon measurements on a regional-scale spanning wide climatic and geologic gradients with a more in-depth approach for a subset of locations. For this subset, time-series and carbon pool-specific radiocarbon data has been acquired for both topsoil and deeper soils. These well-studied sites are part of the Long-Term Forest Ecosystem Research (LWF) program of the Swiss Federal Institute for Forest, Snow and Landscape research (WSL). Statistical analysis was performed to examine relationships of radiocarbon signatures with variables such as temperature, precipitation and elevation. Bomb-curve modeling was applied determine carbon turnover using time-series data. Results indicate that (1) there is no significant correlation between Δ14C signature and environmental conditions except a weak positive correlation with mean annual temperature, (2) vertical gradients in Δ14C signatures in surface and deeper soils are highly similar despite covering disparate soil-types and climatic systems, and (3) radiocarbon signatures vary significantly between time-series samples and carbon pools. Overall, this study provides a uniquely comprehensive dataset that allows for a better understanding of links between carbon dynamics and environmental settings, as well as for pool-specific and long-term trends in carbon (de)stabilization.
NASA Astrophysics Data System (ADS)
Barreyre, T.; Sohn, R. A.; Crone, T. J.
2014-12-01
Time-series records of mid-ocean ridge hydrothermal fluid properties and flow rates have the potential to help constrain the hydrogeology, subsurface circulation patterns, heat, mass, and chemical fluxes, and habitat conditions within young oceanic crust. This potential has motivated a concerted international effort to acquire such records from a variety of geologically distinct vent fields at numerous locations along the mid-ocean ridge system. However up until now, the global database has not been systematically explored. These records have only been analyzed in a piecemeal fashion, which is problematic because hydrothermal time-series records from individual sites typically exhibit enigmatic modes of episodic and periodic variability that are difficult to interpret in isolation. In this study, we conduct a systematic analysis of the extant set of hydrothermal time-series records from several mid-ocean ridge sites where observatory-style experiments have been conducted (including, LSHF, TAG, EPR 9°50'N and MEF). We show that most temperature records, regardless of location or geological setting, display systematic tide-related variability, with the strongest signal at the principal semidiurnal tidal periods (M2, S2, N2 and K2). Cross-spectral multi-taper methods applied to the temperature and bottom pressure records reveal robust phase relationships, particularly for the high-temperature, black-smoker records, as predicted by poroelastic theory. These results suggest that tidal pressures diffusely propagate through the formation, perturbing fluid velocities and temperatures, resulting in phase lags between the seafloor loading and the exit-fluid temperatures. Here, we use multi-layer analytical and numerical models to constrain the subseafloor permeability, skin depth, and Darcy velocities required to explain the phase lag observations.
Dynamical properties of a ferroelectric capacitor observed through nonlinear time series analysis.
Hegger, Rainer; Kantz, Holger; Schmuser, Frank; Diestelhorst, Martin; Kapsch, Ralf-Peter; Beige, Horst
1998-09-01
By data analysis the ordinary differential equation for the description of an experimental electric resonance circuit with nonlinear capacitor is derived. Triglycine sulfate (TGS) was used as nonlinear dielectric material. This is the most thoroughly investigated ferroelectric with a second order phase transition. Its static dielectric small signal behavior is well described in the framework of the Landau theory, yielding a Duffing-type ordinary differential equation as a model equation of the circuit. Data analysis allows us to check carefully the validity of this model and to determine required corrections of this simplified equation. (c) 1998 American Institute of Physics.
Statistics for Time-Series Spatial Data: Applying Survival Analysis to Study Land-Use Change
ERIC Educational Resources Information Center
Wang, Ninghua Nathan
2013-01-01
Traditional spatial analysis and data mining methods fall short of extracting temporal information from data. This inability makes their use difficult to study changes and the associated mechanisms of many geographic phenomena of interest, for example, land-use. On the other hand, the growing availability of land-change data over multiple time…
A time-series analysis framework for the flood-wave method to estimate groundwater model parameters
NASA Astrophysics Data System (ADS)
Obergfell, Christophe; Bakker, Mark; Maas, Kees
2016-11-01
The flood-wave method is implemented within the framework of time-series analysis to estimate aquifer parameters for use in a groundwater model. The resulting extended flood-wave method is applicable to situations where groundwater fluctuations are affected significantly by time-varying precipitation and evaporation. Response functions for time-series analysis are generated with an analytic groundwater model describing stream-aquifer interaction. Analytical response functions play the same role as the well function in a pumping test, which is to translate observed head variations into groundwater model parameters by means of a parsimonious model equation. An important difference as compared to the traditional flood-wave method and pumping tests is that aquifer parameters are inferred from the combined effects of precipitation, evaporation, and stream stage fluctuations. Naturally occurring fluctuations are separated in contributions from different stresses. The proposed method is illustrated with data collected near a lowland river in the Netherlands. Special emphasis is put on the interpretation of the streambed resistance. The resistance of the streambed is the result of stream-line contraction instead of a semi-pervious streambed, which is concluded through comparison with the head loss calculated with an analytical two-dimensional cross-section model.
NASA Astrophysics Data System (ADS)
Kravets, O. Ja; Abramov, G. V.; Beletskaja, S. Ju
2017-02-01
The article describes a generalization of the mechanisms of cross-correlation analysis in the case of a multivariate time series and how this allows the optimal lags to be identified for each of the independent variables (IV) using a number of algorithms. The use of generalized mechanisms will allow variables to be analysed and predicted based on the retrospective analysis of multidimensional data. In the available literature, cross-correlation has been defined only for pairs of time series. However, the study of dependent variable (DV) dependencies on multidimensional independent variables that takes into account the vector of specially selected time lags will significantly improve the quality of models based on multiple regression. The idea of multiple cross-correlation lies in the sequential forward shift of each IV row with respect to DV (it transpires that DV is delayed relative to IV) until we obtain a minimum error or the best test of multiple regression. After the completion of all stages of multiple cross-correlation, the synthesis of the model is not a difficult process.
Time-series analysis of chemical trends in a dated ice core from Antarctica
Keskin, S.S.; Olmez, I.; Langway, C.C. Jr.
1994-12-31
Polar ice sheets contain valuable information about past atmospheric conditions. Atmospherically produced or transported substances from natural and anthropogenic sources are preserved stratigraphically within the ice layers as a result of both wet and dry deposition mechanisms. Substances deposited include aerosols and gaseous compounds. The analysis of trace elements contained in dated annual snow layers provides a measure of the elemental chemistry content of the atmosphere during the same time interval. The aerosol content of the atmosphere and ice sheets is one of the most important parameters for cloud/radiation interaction processes. Ice cores were obtained from the Byrd Station, West Antarctica, in November, 1989. This study presents results obtained from instrumental neutron activation analysis and ion chromatography on 30 samples over a 20 year period.
NASA Astrophysics Data System (ADS)
Wang, H. Y.; Li, Z. Y.; Gao, Z. H.; Wu, J. J.; Sun, B.; Li, C. L.
2014-03-01
Land condition assessment is a basic prerequisite for finding the degradation of a territory, which might lead to desertification under climatic and human pressures. The temporal change in vegetation productivity is a key indicator of land degradation. In this paper, taking the Otindag Sandy Land as a case, the mean normalized difference vegetation index (NDVI_a), net primary production (NPP) and vegetation rain use efficiency (RUE) dynamic trends during 2001-2010 were analysed. The Mann-Kendall test and the Correlation Analysis method were used and their sensitivities to land degradation were evaluated. The results showed that the three vegetation indicators (NDVI_a, NPP and RUE) showed a downward trend with the two methods in the past 10 years and the land was degraded. For the analysis of the three vegetation indicators (NDVI_a, NPP and RUE), it indicated a decreasing trend in 62.57%, 74.16% and 88.56% of the study area according to the Mann-Kendall test and in 57.85%, 68.38% and 85.29% according to the correlation analysis method. However, the change trends were not significant, the significant trends at the 95% confidence level only accounted for a small proportion. Analysis of NDVI_a, NPP and RUE series showed a significant decreasing trend in 9.21%, 4.81% and 6.51% with the Mann-Kendall test. The NPP change trends showed obvious positive link with the precipitation in the study area. While the effect of the inter-annual variation of the precipitation for RUE was small, the vegetation RUE can provide valuable insights into the status of land condition and had best sensitivity to land degradation.
Higher Order Residual Analysis for Nonlinear Time Series with Autoregressive Correlation Structures.
1984-09-25
ANALYSIS FOR NONLINEAR TIMEF SERIES WITH AI.OREGRESSIVE CORRELATION STRUVIURES BY P.A.W. Lewis & A. J. Lawrance September 1984 Approved for public release...of all or part of this report is authorized. P.A.W. Lewis A. J. Lawrance Professor of Operations Research University of Birmingham, England ’ Naval...J. Lawrance P. A. W. Lewis 9 PERFORMING ORGANIZATION NAME AND ADDRESS 10. PROGRAM ELEMENT. PROJECT, TASK AREA G WORK UNIT NUMBERS Naval Postgraduate
2009-04-01
unfavorable outcome. Any given prediction of a linear regression model is the mean and has an associated standard deviation. Using Monte Carlo ...and standard deviation parameters for a Monte Carlo simulation to determine the quantitative uncertainty or risk in the models predictions is...Statistics and Data Analysis Using JMP® and JMP IN® Software." SAS Institute Inc., 2001. Schwartz, Lawrence, Isabela Castaneda , Ronald L. Straight
Time Series Modeling of Army Mission Command Communication Networks: An Event-Driven Analysis
2013-06-01
critical events. In a detailed analysis of the email corpus of the Enron Corporation, Diesner and Carley (2005; see also Murshed et al. 2007) found that...established contacts and formal roles. The Enron crisis is instructive as a network with a critical period of failure. Other researchers have also found...Diesner, J., Frantz, T. L., & Carley, K. M. (2005). Communication networks from the Enron email corpus “It’s always about the people. Enron is no
Hutchinson, J M S; Jacquin, A; Hutchinson, S L; Verbesselt, J
2015-03-01
Given the significant land holdings of the U.S. Department of Defense, and the importance of those lands to support a variety of inherently damaging activities, application of sound natural resource conservation principles and proactive monitoring practices are necessary to manage military training lands in a sustainable manner. This study explores a method for, and the utility of, analyzing vegetation condition and trends as sustainability indicators for use by military commanders and land managers, at both the national and local levels, in identifying when and where vegetation-related environmental impacts might exist. The BFAST time series decomposition method was applied to a ten-year MODIS NDVI time series dataset for the Fort Riley military installation and Konza Prairie Biological Station (KPBS) in northeastern Kansas. Imagery selected for time-series analysis were 16-day MODIS NDVI (MOD13Q1 Collection 5) composites capable of characterizing vegetation change induced by human activities and climate variability. Three indicators related to gradual interannual or abrupt intraannual vegetation change for each pixel were calculated from the trend component resulting from the BFAST decomposition. Assessment of gradual interannual NDVI trends showed the majority of Fort Riley experienced browning between 2001 and 2010. This result is supported by validation using high spatial resolution imagery. The observed versus expected frequency of linear trends detected at Fort Riley and KPBS were significantly different and suggest a causal link between military training activities and/or land management practices. While both sites were similar with regards to overall disturbance frequency and the relative spatial extents of monotonic or interrupted trends, vegetation trajectories after disturbance were significantly different. This suggests that the type and magnitude of disturbances characteristic of each location result in distinct post-disturbance vegetation responses
NASA Technical Reports Server (NTRS)
Mourad, A. G. (Principal Investigator); Fubara, D. M. J.
1973-01-01
The author has identified the following significant results. The analysis was based on a time series intrinsic relationship between the satellite ephemeris, altimeter measured ranges, and the corresponding a priori values of subsatellite geoidal heights. Using sequential least squares processing with parameter weighting, the objective was to recover (1) the absolute geoidal heights of the subsatellite points, and (2) the associated altimeter calibration constant(s). Preliminary results from Skylab altimetry are given, using various combinations of orbit ephemeris and altimeter ranges as computed differently by NASA/JSC and NASA/Wallops. The influences of orbit accuracy, weighting functions, and a priori ground truth are described, based on the various combination solutions. It is shown that to deduce geoidal height by merely subtracting the height of the satellite from the altimeter range is inadmissible. The results of such direct subtraction can be very misleading if the orbit used is computed from data that included altimeter data used as height constraints. In view of the current state of knowledge, the use of geodetic ground truth samples as control benchmarks appears indispensable for the recovery of absolute geoidal heights with correct scale.
NASA Astrophysics Data System (ADS)
Olofsson, Pontus; Holden, Christopher E.; Bullock, Eric L.; Woodcock, Curtis E.
2016-06-01
Land cover and land change were monitored continuously between 1985 and 2011 at 30 m resolution across New England in the Northeastern United States in support of modeling the terrestrial carbon budget. It was found that the forest area has been decreasing throughout the study period in each state of the region since the 1980s. A total of 386 657 ± 98 137 ha (95% confidence interval) of forest has been converted to other land covers since 1985. Mainly driven by low density residential development, the deforestation accelerated in the mid-1990s until 2007 when it plateaued as a result of declining new residential construction and in turn, the financial crisis of 2007-08. The area of forest harvest, estimated at 226 519 ± 66 682 ha, was mapped separately and excluded from the deforestation estimate, while the area of forest expansion on non-forested lands was found to not be significantly different from zero. New England is often held as a principal example of a forest transition with historical widespread deforestation followed by recovery of forestlands as farming activities diminished, but the results of this study support the notion of a reversal of the forest transition as the region again is experiencing widespread deforestation. All available Landsat imagery acquired after 1985 for the study area were collected and used in the analysis. Areas of land cover and land change were estimated from a random sample of reference observations stratified by a twelve-class land change map encompassing the entire study area and period. The statistical analysis revealed that the net change in forest area and the associated modeled impact on the terrestrial carbon balance would have been considerably different if the results of the map were used without inferring the area of forest change by analysis of a reference sample.
The GPS Analysis Package for Exploration and Understanding of Geodetic Sensor Web Time Series Data
NASA Astrophysics Data System (ADS)
Granat, R. A.; Moghaddam, B.; Donnellan, A.
2012-12-01
We introduce the GPS Analysis Package (GAP), a Matlab toolbox for GPS data exploration and understanding. The toolbox is designed to support scientists and engineers studying the motion of the solid Earth both in an academic environment and in the course of NASA missions such as UAVSAR and future InSAR satellite missions. It includes an ensemble of low-level routines to perform basic signal processing operations, such as removal of secular motion, de-noising, and removal of seasonal signals. It also includes a suite of more sophisticated statistical pattern recognition techniques, including hidden Markov models and Bayes nets, to detect changes, identify transient signals, understand regional motion, and uncover relationships between geographically removed nodes in the GPS network. Finally, it provides an assortment of methods for estimating missing observations in the network. We provide usage examples of the package applied to particular scenarios, including the 2010 El Mayor-Cucapah earthquake, the 2011 Tohoku-Oki earthquake, and ongoing slow slip events in the Cascadia region. We also demonstrate the utility of the package within a web portal and web services environment by showcasing its use in the QuakeSim web portal. The QuakeSim portal allows easy access to GPS data sources provided by multiple institutions as well as a map and plotting interface to quickly assess analysis results. Finally, we show the extensibility of the package to other problem domains and sensor network data sources, demonstrating the analysis tools as applied to seismic network data, autonomous robotic navigation, and fault detection in engineering data streams from the International Space Station.
Time Series Analysis of Monte Carlo Fission Sources - I: Dominance Ratio Computation
Ueki, Taro; Brown, Forrest B.; Parsons, D. Kent; Warsa, James S.
2004-11-15
In the nuclear engineering community, the error propagation of the Monte Carlo fission source distribution through cycles is known to be a linear Markov process when the number of histories per cycle is sufficiently large. In the statistics community, linear Markov processes with linear observation functions are known to have an autoregressive moving average (ARMA) representation of orders p and p - 1. Therefore, one can perform ARMA fitting of the binned Monte Carlo fission source in order to compute physical and statistical quantities relevant to nuclear criticality analysis. In this work, the ARMA fitting of a binary Monte Carlo fission source has been successfully developed as a method to compute the dominance ratio, i.e., the ratio of the second-largest to the largest eigenvalues. The method is free of binning mesh refinement and does not require the alteration of the basic source iteration cycle algorithm. Numerical results are presented for problems with one-group isotropic, two-group linearly anisotropic, and continuous-energy cross sections. Also, a strategy for the analysis of eigenmodes higher than the second-largest eigenvalue is demonstrated numerically.
Oomens, Wouter; Maes, Joseph H. R.; Hasselman, Fred; Egger, Jos I. M.
2015-01-01
The concept of executive functions plays a prominent role in contemporary experimental and clinical studies on cognition. One paradigm used in this framework is the random number generation (RNG) task, the execution of which demands aspects of executive functioning, specifically inhibition and working memory. Data from the RNG task are best seen as a series of successive events. However, traditional RNG measures that are used to quantify executive functioning are mostly summary statistics referring to deviations from mathematical randomness. In the current study, we explore the utility of recurrence quantification analysis (RQA), a non-linear method that keeps the entire sequence intact, as a better way to describe executive functioning compared to traditional measures. To this aim, 242 first- and second-year students completed a non-paced RNG task. Principal component analysis of their data showed that traditional and RQA measures convey more or less the same information. However, RQA measures do so more parsimoniously and have a better interpretation. PMID:26097449
Extensive mapping of coastal change in Alaska by Landsat time-series analysis, 1972-2013 (Invited)
NASA Astrophysics Data System (ADS)
Macander, M. J.; Swingley, C. S.; Reynolds, J.
2013-12-01
The landscape-scale effects of coastal storms on Alaska's Bering Sea and Gulf of Alaska coasts includes coastal erosion, migration of spits and barrier islands, breaching of coastal lakes and lagoons, and inundation and salt-kill of vegetation. Large changes in coastal storm frequency and intensity are expected due to climate change and reduced sea-ice extent. Storms have a wide range of impacts on carbon fluxes and on fish and wildlife resources, infrastructure siting and operation, and emergency response planning. In areas experiencing moderate to large effects, changes can be mapped by analyzing trends in time series of Landsat imagery from Landsat 1 through Landsat 8. ABR, Inc.--Environmental Research & Services and the Western Alaska Landscape Conservation Cooperative are performing a time-series trend analysis for over 22,000 kilometers of coastline along the Bering Sea and Gulf of Alaska. The archive of Landsat imagery covers the time period 1972-present. For a pilot study area in Kotzebue Sound, we conducted a regression analysis of changes in near-infrared reflectance to identify areas with significant changes in coastal features, 1972-2011. Suitable ice- and cloud-free Landsat imagery was obtained for 28 of the 40 years during the period. The approach captured several coastal changes over the 40-year study period, including coastal erosion exceeding the 60-m pixel resolution of the Multispectral Scanner (MSS) data and migrations of coastal spits and estuarine channels. In addition several lake drainage events were identified, mostly inland from the coastal zone. Analysis of shorter, decadal time periods produced noisier results that were generally consistent with the long-term trend analysis. Unusual conditions at the start or end of the time-series can strongly influence decadal results. Based on these results the study is being scaled up to map coastal change for over 22,000 kilometers of coastline along the Bering Sea and Gulf of Alaska coast. The
Intensive time series data exploitation: the Multi-sensor Evolution Analysis (MEA) platform
NASA Astrophysics Data System (ADS)
Mantovani, Simone; Natali, Stefano; Folegani, Marco; Scremin, Alessandro
2014-05-01
The monitoring of the temporal evolution of natural phenomena must be performed in order to ensure their correct description and to allow improvements in modelling and forecast capabilities. This assumption, that is obvious for ground-based measurements, has not always been true for data collected through space-based platforms: except for geostationary satellites and sensors, that allow providing a very effective monitoring of phenomena with geometric scale from regional to global; smaller phenomena (with characteristic dimension lower than few kilometres) have been monitored with instruments that could collect data only with a time interval in the order of several days; bi-temporal techniques have been the most used ones for years, in order to characterise temporal changes and try identifying specific phenomena. The more the number of flying sensor has grown and their performance improved, the more their capability of monitoring natural phenomena at a smaller geographic scale has grown: we can now count on tenth of years of remotely sensed data, collected by hundreds of sensors that are now accessible from a wide users' community, and the techniques for data processing have to be adapted to move toward a data intensive exploitation. Starting from 2008, the European Space Agency has initiated the development of the Multi-sensor Evolution Analysis (MEA) platform (https://mea.eo.esa.int), whose first aim was to permit the access and exploitation of long term remotely sensed satellite data from different platforms: 15 years of global (A)ATSR data together with 5 years of regional AVNIR-2 data were loaded into the system and were used, through a web-based graphic user interface, for land cover change analysis. The MEA data availability has grown during years integrating multi-disciplinary data that feature spatial and temporal dimensions: so far tenths of Terabytes of data in the land and atmosphere domains are available and can be visualized and exploited, keeping the
Program for the analysis of time series. [by means of fast Fourier transform algorithm
NASA Technical Reports Server (NTRS)
Brown, T. J.; Brown, C. G.; Hardin, J. C.
1974-01-01
A digital computer program for the Fourier analysis of discrete time data is described. The program was designed to handle multiple channels of digitized data on general purpose computer systems. It is written, primarily, in a version of FORTRAN 2 currently in use on CDC 6000 series computers. Some small portions are written in CDC COMPASS, an assembler level code. However, functional descriptions of these portions are provided so that the program may be adapted for use on any facility possessing a FORTRAN compiler and random-access capability. Properly formatted digital data are windowed and analyzed by means of a fast Fourier transform algorithm to generate the following functions: (1) auto and/or cross power spectra, (2) autocorrelations and/or cross correlations, (3) Fourier coefficients, (4) coherence functions, (5) transfer functions, and (6) histograms.
Enhancing dominant modes in nonstationary time series by means of the symbolic resonance analysis
NASA Astrophysics Data System (ADS)
beim Graben, Peter; Drenhaus, Heiner; Brehm, Eva; Rhode, Bela; Saddy, Douglas; Frisch, Stefan
2007-12-01
We present the symbolic resonance analysis (SRA) as a viable method for addressing the problem of enhancing a weakly dominant mode in a mixture of impulse responses obtained from a nonlinear dynamical system. We demonstrate this using results from a numerical simulation with Duffing oscillators in different domains of their parameter space, and by analyzing event-related brain potentials (ERPs) from a language processing experiment in German as a representative application. In this paradigm, the averaged ERPs exhibit an N400 followed by a sentence final negativity. Contemporary sentence processing models predict a late positivity (P600) as well. We show that the SRA is able to unveil the P600 evoked by the critical stimuli as a weakly dominant mode from the covering sentence final negativity.
Manifold Learning With Contracting Observers for Data-Driven Time-Series Analysis
NASA Astrophysics Data System (ADS)
Shnitzer, Tal; Talmon, Ronen; Slotine, Jean-Jacques
2017-02-01
Analyzing signals arising from dynamical systems typically requires many modeling assumptions and parameter estimation. In high dimensions, this modeling is particularly difficult due to the "curse of dimensionality". In this paper, we propose a method for building an intrinsic representation of such signals in a purely data-driven manner. First, we apply a manifold learning technique, diffusion maps, to learn the intrinsic model of the latent variables of the dynamical system, solely from the measurements. Second, we use concepts and tools from control theory and build a linear contracting observer to estimate the latent variables in a sequential manner from new incoming measurements. The effectiveness of the presented framework is demonstrated by applying it to a toy problem and to a music analysis application. In these examples we show that our method reveals the intrinsic variables of the analyzed dynamical systems.
Time Series Analysis of Hemorrhagic Fever with Renal Syndrome: A Case Study in Jiaonan County, China
Li, Shujuan; Cao, Wei; Ren, Hongyan; Lu, Liang; Zhuang, Dafang; Liu, Qiyong
2016-01-01
Exact prediction of Hemorrhagic fever with renal syndrome (HFRS) epidemics must improve to establish effective preventive measures in China. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was applied to establish a highly predictive model of HFRS. Meteorological factors were considered external variables through a cross correlation analysis. Then, these factors were included in the SARIMA model to determine if they could improve the predictive ability of HFRS epidemics in the region. The optimal univariate SARIMA model was identified as (0,0,2)(1,1,1)12. The R2 of the prediction of HFRS cases from January 2014 to December 2014 was 0.857, and the Root mean square error (RMSE) was 2.708. However, the inclusion of meteorological variables as external regressors did not significantly improve the SARIMA model. This result is likely because seasonal variations in meteorological variables were included in the seasonal characteristics of the HFRS itself. PMID:27706256
Trend analysis of rainfall time series for Sindh river basin in India
NASA Astrophysics Data System (ADS)
Gajbhiye, Sarita; Meshram, Chandrashekhar; Mirabbasi, Rasoul; Sharma, S. K.
2016-08-01
The study of precipitation trends is critically important for a country like India whose food security and economy are dependent on the timely availability of water such as 83 % water used for agriculture sector, 12 % for industry sector and only 5 % for domestic sector. In this study, the historical rainfall data for the periods 1901-2002 and 1942-2002 of the Sindh river basin, India, were analysed for monthly, seasonal and annual trends. The conventional Mann-Kendall test (MK) and Mann-Kendall test (MMK), after the removal of the effect of all significant autocorrelation coefficients, and Sen's slope estimator were used to identify the trends. Kriging technique was used for interpolating the spatial pattern using Arc GIS 9.3. The analysis suggested significant increase in the trend of rainfall for seasonal and annual series in the Sindh basin during 1901-2002.
Time-series analysis for rapid event-related skin conductance responses
Bach, Dominik R.; Flandin, Guillaume; Friston, Karl J.; Dolan, Raymond J.
2009-01-01
Event-related skin conductance responses (SCRs) are traditionally analysed by comparing the amplitude of individual peaks against a pre-stimulus baseline. Many experimental manipulations in cognitive neuroscience dictate paradigms with short inter trial intervals, precluding accurate baseline estimation for SCR measurements. Here, we present a novel and general approach to SCR analysis, derived from methods used in neuroimaging that estimate responses using a linear convolution model. In effect, the method obviates peak-scoring and makes use of the full SCR. We demonstrate, across three experiments, that the method has face validity in analysing reactions to a loud white noise and emotional pictures, can be generalised to paradigms where the shape of the response function is unknown and can account for parametric trial-by-trial effects. We suggest our approach provides greater flexibility in analysing SCRs than existing methods. PMID:19686778
NASA Astrophysics Data System (ADS)
Mingwei, Zhang; Qingbo, Zhou; Zhongxin, Chen; Jia, Liu; Yong, Zhou; Chongfa, Cai
2008-12-01
Crop identification is the basis of crop monitoring using remote sensing. Remote sensing the extent and distribution of individual crop types has proven useful to a wide range of users, including policy-makers, farmers, and scientists. Northern China is not merely the political, economic, and cultural centre of China, but also an important base for grain production. Its main grains are wheat, maize, and cotton. By employing the Fourier analysis method, we studied crop planting patterns in the Northern China plain. Then, using time-series EOS-MODIS NDVI data, we extracted the key parameters to discriminate crop types. The results showed that the estimated area and the statistics were correlated well at the county-level. Furthermore, there was little difference between the crop area estimated by the MODIS data and the statistics at province-level. Our study shows that the method we designed is promising for use in regional spatial scale crop mapping in Northern China using the MODIS NDVI time-series.
Schulz, Marcus; Krone, Roland; Dederer, Gabriele; Wätjen, Kai; Matthies, Michael
2015-05-01
The comparative analysis of marine litter in different marine compartments has rarely been attempted. In this study, long-term time series of marine litter abundance on the seafloor and on the coast, both from the southeastern North Sea, were analyzed for temporal trends and correlations. On four beach sections of 100 m length, mean abundances of total beach litter collected four times a year from 2002 to 2008 varied between 105 and 435 items. Mean densities of total inorganic litter on the seafloor amounted to 10.6 ± 9.7 kg km(-2) in the offshore region (2001-2010) and 13.7 ± 12.6 kg km(-2) in the Wadden Sea (1998-2007), respectively. In the offshore region, there was no significant long-term trend, while in the Wadden Sea, densities of marine litter declined significantly. Correlations between time series were weak, indicating different sources and transport processes responsible for compositions of beach litter and litter on the seafloor. Decreases in inputs from fisheries and substantial export due to resuspension are discussed as reasons for the decrease in litter on the seafloor in the Wadden Sea.
NASA Astrophysics Data System (ADS)
Jeannet, P.; Stübi, R.; Levrat, G.; Viatte, P.; Staehelin, J.
2007-06-01
This study documents the history of the Payerne (Switzerland) ozone series obtained with the Brewer-Mast sonde from the end of 1966 until the change to the electrochemical concentration cell (ECC) sonde in autumn 2002, as well as the reevaluation of the original data. Several corrections were made in order to improve the homogeneity and the quality of the time series. We furthermore derived long-term trends for the reevaluated time series using atmospheric variables in a stepwise regression model. In the stratosphere, trends over the 1970-2002 period remain nearly the same as over periods ending a few years earlier. For tropospheric ozone trends, a hockey stick model allowing for a change in trend in 1990 was used and a sensitivity analysis with different data sets was carried out. Besides the standard World Meteorological Organization (WMO) data evaluation procedure, we used alternative data sets (1) accounting for the preflight laboratory calibrations, or (2) ignoring the total ozone normalization, (3) as well as correcting for chemical interference with SO2. With all data sets, tropospheric trends were strongly positive in all seasons over the 1967-1989 period. In the 1990-2002 period, winter trends remained positive over the whole troposphere with all data sets, whereas in the other seasons, trends were generally negative near the ground and shifted to zero or positive values with increasing altitude in the troposphere. The alternative evaluation procedures strongly affect the derived tropospheric trends in the 1990-2002 period and their uncertainties.
Impact of telephone triage on emergency after hours GP Medicare usage: a time-series analysis
Dunt, David; Wilson, Robert; Day, Susan E; Kelaher, Margaret; Gurrin, Lyle
2007-01-01
Background The Australian government sponsored trials aimed at addressing problems in after hours primary medical care service use in five different parts of the country with different after hours care problems. The study's objective was to determine in four of the five trials where telephone triage was the sole innovation, if there was a reduction in emergency GP after hours service utilization (GP first call-out) as measured in Medicare Benefits Schedule claim data. Monthly MBS claim data in both the pre-trial and trial periods was monitored over a 3-year period in each trial area as well as in a national sample outside the trial areas (National comparator). Poisson regression analysis was used in analysis. Results There was significant reduction in first call out MBS claims in three of the four study areas where stand-alone call centre services existed. These were the Statewide Call Centre in both its Metropolitan and Non-metropolitan areas in which it operated – Relative Risk (RR) = 0.87 (95% Confidence interval: 0.86 – 0.88) and 0.60 (95% CI: 0.54 – 0.68) respectively. There was also a reduction in the Regional Call Centre in the non-Metropolitan area in which it operated (RR = 0.46 (95% CI: 0.35 – 0.61) though a small increase in its Metropolitan area (RR = 1.11 (95% CI: 1.06 – 1.17). For the two telephone triage services embedded in existing organisations, there was also a significant reduction for the Deputising Service – RR = 0.62 (95% CI: 0.61 – 0.64) but no change in the Local Triage centre area. Conclusion The four telephone triage services were associated with reduced GP MBS claims for first callout after hours care in most study areas. It is possible that other factors could be responsible for some of this reduction, for example, MBS submitted claims for after hours GP services being reclassified from 'after hours' to 'in hours'. The goals of stand-alone call centres which are aimed principally at meeting population needs rather than
Impact of temperature on mortality in Hubei, China: a multi-county time series analysis
NASA Astrophysics Data System (ADS)
Zhang, Yunquan; Yu, Chuanhua; Bao, Junzhe; Li, Xudong
2017-03-01
We examined the impact of extreme temperatures on mortality in 12 counties across Hubei Province, central China, during 2009–2012. Quasi-Poisson generalized linear regression combined with distributed lag non-linear model was first applied to estimate county-specific relationship between temperature and mortality. A multivariable meta-analysis was then used to pool the estimates of county-specific mortality effects of extreme cold temperature (1st percentile) and hot temperature (99th percentile). An inverse J-shaped relationship was observed between temperature and mortality at the provincial level. Heat effect occurred immediately and persisted for 2–3 days, whereas cold effect was 1–2 days delayed and much longer lasting. Higher mortality risks were observed among females, the elderly aged over 75 years, persons dying outside the hospital and those with high education attainment, especially for cold effects. Our data revealed some slight differences in heat- and cold- related mortality effects on urban and rural residents. These findings may have important implications for developing locally-based preventive and intervention strategies to reduce temperature-related mortality, especially for those susceptible subpopulations. Also, urbanization should be considered as a potential influence factor when evaluating temperature-mortality association in future researches.
Impact of temperature on mortality in Hubei, China: a multi-county time series analysis
Zhang, Yunquan; Yu, Chuanhua; Bao, Junzhe; Li, Xudong
2017-01-01
We examined the impact of extreme temperatures on mortality in 12 counties across Hubei Province, central China, during 2009–2012. Quasi-Poisson generalized linear regression combined with distributed lag non-linear model was first applied to estimate county-specific relationship between temperature and mortality. A multivariable meta-analysis was then used to pool the estimates of county-specific mortality effects of extreme cold temperature (1st percentile) and hot temperature (99th percentile). An inverse J-shaped relationship was observed between temperature and mortality at the provincial level. Heat effect occurred immediately and persisted for 2–3 days, whereas cold effect was 1–2 days delayed and much longer lasting. Higher mortality risks were observed among females, the elderly aged over 75 years, persons dying outside the hospital and those with high education attainment, especially for cold effects. Our data revealed some slight differences in heat- and cold- related mortality effects on urban and rural residents. These findings may have important implications for developing locally-based preventive and intervention strategies to reduce temperature-related mortality, especially for those susceptible subpopulations. Also, urbanization should be considered as a potential influence factor when evaluating temperature-mortality association in future researches. PMID:28327609
Impact of temperature on mortality in Hubei, China: a multi-county time series analysis.
Zhang, Yunquan; Yu, Chuanhua; Bao, Junzhe; Li, Xudong
2017-03-22
We examined the impact of extreme temperatures on mortality in 12 counties across Hubei Province, central China, during 2009-2012. Quasi-Poisson generalized linear regression combined with distributed lag non-linear model was first applied to estimate county-specific relationship between temperature and mortality. A multivariable meta-analysis was then used to pool the estimates of county-specific mortality effects of extreme cold temperature (1st percentile) and hot temperature (99th percentile). An inverse J-shaped relationship was observed between temperature and mortality at the provincial level. Heat effect occurred immediately and persisted for 2-3 days, whereas cold effect was 1-2 days delayed and much longer lasting. Higher mortality risks were observed among females, the elderly aged over 75 years, persons dying outside the hospital and those with high education attainment, especially for cold effects. Our data revealed some slight differences in heat- and cold- related mortality effects on urban and rural residents. These findings may have important implications for developing locally-based preventive and intervention strategies to reduce temperature-related mortality, especially for those susceptible subpopulations. Also, urbanization should be considered as a potential influence factor when evaluating temperature-mortality association in future researches.
Outdoor air temperature and mortality in The Netherlands: a time-series analysis.
Kunst, A E; Looman, C W; Mackenbach, J P
1993-02-01
Death rates become progressively higher when outdoor air temperature rises above or falls below 20-25 degrees C. This study addresses the question of whether this relation is largely attributable to the direct effects of exposure to heat and cold on the human body in general, and on the circulatory system in particular. The association between daily mortality and daily temperatures in the Netherlands in the period 1979-1987 was examined by controlling for influenza incidence, air pollution, and "season"; distinguishing lag periods; examining effect modification by wind speed and relative humidity; and distinguishing causes of death. Important direct effects of exposure to cold and heat on mortality were suggested by the following findings: 1) control for influenza incidence reduced cold-related mortality by only 34% and reduced heat-related mortality by 23% (the role of air pollution and "season" was negligible); 2) 62% of the "unexplained" cold-related mortality, and all heat-related mortality, occurred within 1 week; and 3) effect modification by wind speed was in the expected direction. The finding that 57% of "unexplained" cold-related mortality and 26% of the "unexplained" heat-related mortality was attributable to cardiovascular diseases suggests that direct effects are only in part the result of increased stress on the circulatory system. For heat-related mortality, direct effects on the respiratory system are probably more important. For cold-related mortality, the analysis yielded evidence of an important indirect effect involving increased incidence of influenza and other respiratory infections.
Analysis of the mass balance time series of glaciers in the Italian Alps
NASA Astrophysics Data System (ADS)
Carturan, Luca; Baroni, Carlo; Brunetti, Michele; Carton, Alberto; Dalla Fontana, Giancarlo; Salvatore, Maria Cristina; Zanoner, Thomas; Zuecco, Giulia
2016-03-01
This work presents an analysis of the mass balance series of nine Italian glaciers, which were selected based on the length, continuity and reliability of observations. All glaciers experienced mass loss in the observation period, which is variable for the different glaciers and ranges between 10 and 47 years. The longest series display increasing mass loss rates, which were mainly due to increased ablation during longer and warmer ablation seasons. The mean annual mass balance (Ba) in the decade from 2004 to 2013 ranged from -1788 to -763 mm w.e. yr-1. Low-altitude glaciers with low range of elevation are more out of balance than the higher, larger and steeper glaciers, which maintain residual accumulation areas in their upper reaches. The response of glaciers is mainly controlled by the combination of October-May precipitations and June-September temperatures, but rapid geometric adjustments and atmospheric changes lead to modifications in their response to climatic variations. In particular, a decreasing correlation of Ba with the June-September temperatures and an increasing correlation with October-May precipitations are observed for some glaciers. In addition, the October-May temperatures tend to become significantly correlated with Ba, possibly indicating a decrease in the fraction of solid precipitation, and/or increased ablation, during the accumulation season. Because most of the monitored glaciers have no more accumulation area, their observations series are at risk due to their impending extinction, thus requiring a replacement soon.
Analysis of the mass balance time series of glaciers in the Italian Alps
NASA Astrophysics Data System (ADS)
Carturan, L.; Baroni, C.; Brunetti, M.; Carton, A.; Dalla Fontana, G.; Salvatore, M. C.; Zanoner, T.; Zuecco, G.
2015-10-01
This work presents an analysis of the mass balance series of nine Italian glaciers, which were selected based on the length, continuity and reliability of observations. All glaciers experienced mass loss in the observation period, which is variable for the different glaciers and ranges between 10 and 47 years. The longest series display increasing mass loss rates, that were mainly due to increased ablation during longer and warmer ablation seasons. The mean annual mass balance (Ba) in the decade from 2004 to 2013 ranged from -1788 mm to -763 mm w.e. yr-1. Low-altitude glaciers with low elevation ranges are more out of balance than the higher, larger and steeper glaciers, which maintain residual accumulation areas in their upper reaches. The response of glaciers is mainly controlled by the combination of October-May precipitation and June-September temperature, but rapid geometric adjustments and atmospheric changes lead to modifications in their response to climatic variations. In particular, a decreasing correlation of Ba with the June-September temperature and an increasing correlation with October-May precipitation are observed for some glaciers. In addition, the October-May temperature tends to become significantly correlated with Ba, possibly indicating a decrease in the fraction of solid precipitation, and/or increased ablation, during the accumulation season. Because most of the monitored glaciers have no more accumulation area, their observations series are at risk due to their impending extinction, thus requiring a soon replacement.
The Tracking Meteogram, an AWIPS II Tool for Time-Series Analysis
NASA Technical Reports Server (NTRS)
Burks, Jason Eric; Sperow, Ken
2015-01-01
A new tool has been developed for the National Weather Service (NWS) Advanced Weather Interactive Processing System (AWIPS) II through collaboration between NASA's Short-term Prediction Research and Transition (SPoRT) and the NWS Meteorological Development Laboratory (MDL). Referred to as the "Tracking Meteogram", the tool aids NWS forecasters in assessing meteorological parameters associated with moving phenomena. The tool aids forecasters in severe weather situations by providing valuable satellite and radar derived trends such as cloud top cooling rates, radial velocity couplets, reflectivity, and information from ground-based lightning networks. The Tracking Meteogram tool also aids in synoptic and mesoscale analysis by tracking parameters such as the deepening of surface low pressure systems, changes in surface or upper air temperature, and other properties. The tool provides a valuable new functionality and demonstrates the flexibility and extensibility of the NWS AWIPS II architecture. In 2014, the operational impact of the tool was formally evaluated through participation in the NOAA/NWS Operations Proving Ground (OPG), a risk reduction activity to assess performance and operational impact of new forecasting concepts, tools, and applications. Performance of the Tracking Meteogram Tool during the OPG assessment confirmed that it will be a valuable asset to the operational forecasters. This presentation reviews development of the Tracking Meteogram tool, performance and feedback acquired during the OPG activity, and future goals for continued support and extension to other application areas.
Time series analysis of V 1794 Cygni long-term photometry
NASA Astrophysics Data System (ADS)
Jetsu, L.; Pelt, J.; Tuominen, I.
1999-11-01
Standard Johnson UBVRI photometry of V 1794 Cyg (HD199178) between 1975 and 1995 is analysed. Instead of the traditional constant period ephemeris, we determine the seasonal periodicities (Pphot) and the primary and secondary minima epochs (t_{min,1}, t_{min,2}) of the normalized UBVRI magnitudes using the three stage period analysis (TSPA) and complementary methods. Our t_{min,1} and t_{min,2} estimates with variable Pphot can adapt easily to both differential rotation and longitudinal activity migration. The seasonal Pphot are utilized in modelling the mean (M) and total amplitude (A) of contemporary light curves in UBVRI. TSPA reveals that the long-term M and A changes of V 1794 Cyg are unpredictable. We search for active longitudes from the t_{min,1} and t_{min,2} series of time points with nonparametric methods. The critical level of 0.0029 for the best 3.d3175 period detected with the Kuiper method is high, but exceeds the 0.001 significance for rejecting the hypothesis that the phases of t_{min,1} and t_{min,2} are randomly distributed. The activity centres in V 1794 Cyg are rarely disrupted, and most probably undergo continuous longitudinal migration, because only one abrupt disruption is observed during 20 years. As for differential rotation, the irregular changes of seasonal Pphot are 7.5%. The surprisingly regular 3.3% changes of yearly Pphot may provide a stellar analogy of the solar ``butterfly'' diagram.
Trend analysis of time-series phenology of North America derived from satellite data
Reed, B.C.
2006-01-01
Remote sensing information has been used in studies of the seasonal dynamics (phenology) of the land surface since the 1980s. While our understanding of remote sensing phenology is still in development, it is regarded as a key to understanding land-surface processes over large areas. Phenologic metrics, including start of season, end of season, duration of season, and seasonally integrated greenness, were derived from 8 km advanced very high resolution radiometer (AVHRR) data over North America spanning the years 1982-2003. Trend analysis was performed on annual summaries of the metrics to determine areas with increasing or decreasing growing season trends for the time period under study. Results show a trend toward earlier starts of season in limited areas of the mixed boreal forest, and a trend toward later end of season in well-defined areas of New England and southeastern Canada. Results in Saskatchewan, Canada, include a trend toward longer duration of season over a well-defined area, principally as a result of regional changes in land use practices. Changing seasonality appears to be an integrated response to a complex of factors, including climate change, but also, in many places, changes in land use practices. Copyright ?? 2006 by V. H. Winston & Son, Inc. All rights reserved.
Time series analysis of cholera in Matlab, Bangladesh, during 1988-2001.
Ali, Mohammad; Kim, Deok Ryun; Yunus, Mohammad; Emch, Michael
2013-03-01
The study examined the impact of in-situ climatic and marine environmental variability on cholera incidence in an endemic area of Bangladesh and developed a forecasting model for understanding the magnitude of incidence. Diarrhoea surveillance data collected between 1988 and 2001 were obtained from a field research site in Matlab, Bangladesh. Cholera cases were defined as Vibrio cholerae O1 isolated from faecal specimens of patients who sought care at treatment centres serving the Matlab population. Cholera incidence for 168 months was correlated with remotely-sensed sea-surface temperature (SST) and in-situ environmental data, including rainfall and ambient temperature. A seasonal autoregressive integrated moving average (SARIMA) model was used for determining the impact of climatic and environmental variability on cholera incidence and evaluating the ability of the model to forecast the magnitude of cholera. There were 4,157 cholera cases during the study period, with an average of 1.4 cases per 1,000 people. Since monthly cholera cases varied significantly by month, it was necessary to stabilize the variance of cholera incidence by computing the natural logarithm to conduct the analysis. The SARIMA model shows temporal clustering of cholera at one- and 12-month lags. There was a 6% increase in cholera incidence with a minimum temperature increase of one degree celsius in the current month. For increase of SST by one degree celsius, there was a 25% increase in the cholera incidence at currrent month and 18% increase in the cholera incidence at two months. Rainfall did not influenc to cause variation in cholera incidence during the study period. The model forecast the fluctuation of cholera incidence in Matlab reasonably well (Root mean square error, RMSE: 0.108). Thus, the ambient and sea-surface temperature-based model could be used in forecasting cholera outbreaks in Matlab.
Distributed analysis of simultaneous EEG-fMRI time-series: modeling and interpretation issues.
Esposito, Fabrizio; Aragri, Adriana; Piccoli, Tommaso; Tedeschi, Gioacchino; Goebel, Rainer; Di Salle, Francesco
2009-10-01
Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) represent brain activity in terms of a reliable anatomical localization and a detailed temporal evolution of neural signals. Simultaneous EEG-fMRI recordings offer the possibility to greatly enrich the significance and the interpretation of the single modality results because the same neural processes are observed from the same brain at the same time. Nonetheless, the different physical nature of the measured signals by the two techniques renders the coupling not always straightforward, especially in cognitive experiments where spatially localized and distributed effects coexist and evolve temporally at different temporal scales. The purpose of this article is to illustrate the combination of simultaneously recorded EEG and fMRI signals exploiting the principles of EEG distributed source modeling. We define a common source space for fMRI and EEG signal projection and gather a conceptually unique framework for the spatial and temporal comparative analysis. We illustrate this framework in a graded-load working-memory simultaneous EEG-fMRI experiment based on the n-back task where sustained load-dependent changes in the blood-oxygenation-level-dependent (BOLD) signals during continuous item memorization co-occur with parametric changes in the EEG theta power induced at each single item. In line with previous studies, we demonstrate on two single-subject cases how the presented approach is capable of colocalizing in midline frontal regions two phenomena simultaneously observed at different temporal scales, such as the sustained negative changes in BOLD activity and the parametric EEG theta synchronization. We discuss the presented approach in relation to modeling and interpretation issues typically arising in simultaneous EEG-fMRI studies.
Asymmetric multiscale detrended cross-correlation analysis of financial time series
NASA Astrophysics Data System (ADS)
Yin, Yi; Shang, Pengjian
2014-09-01
We propose the asymmetric multiscale detrended cross-correlation analysis (MS-ADCCA) method and apply MS-ADCCA method to explore the existence of asymmetric cross-correlation for daily price returns in US and Chinese stock markets and to assess the properties of these asymmetric cross-correlations. The results all show the existences of asymmetric cross-correlations, while small asymmetries at small scales and larger asymmetries at larger scales are also displayed. There is a strong similarity between S&P500 and DJI, and we reveal that the asymmetries depend more on the cross-correlations of S&P500 vs. DJI, S&P500 vs. NQCI, DJI vs. NQCI, and ShangZheng vs. ShenCheng when the market is falling than rising, respectively. By comparing the spectra of S&P500 vs. NQCI and DJI vs. NQCI with uptrends and downtrends, we detect some new characteristics which lead to some new conclusions. Likewise, some new conclusions also can be drawn by the new characteristics displayed through the comparison between the spectra of ShangZheng vs. HSI and ShenCheng vs. HSI. Obviously, we conclude that although the overall spectra are similar and one market has the same effect when it is rising and falling in the study of asymmetric cross-correlations between it and different markets, the cross-correlations and asymmetries on the trends of the different markets are all different. MS-ADCCA method can detect the differences on the asymmetric cross-correlations by different trends of markets. Moreover, the uniqueness of cross-correlation between NQCI and HSI can be detected in the study of the asymmetric cross-correlations, which confirms that HSI is unique in the Chinese stock markets and NQCI is unique in the US stock markets further.
Monitoring the Urban Growth on Vitosha Northeast Slope by Time Series Analysis
NASA Astrophysics Data System (ADS)
Nikolov, Hristo
2015-04-01
In last decades satellites are routinely used in solving large amount of Earth observation (EO) tasks. One of the phenomena that can be easily noted from EO images is the urban sprawl caused by urbanization process and formation of megacities. Two concurrent processes are observed in urban area enlargement - the loss of vegetation cover by soil sealing and the increase of impervious surfaces. The area for this specific study was selected due to its economic attractiveness and closeness to one of the biggest national parks - mountain Vitosha. Better identification of the ongoing changes in this particular area is considered to be of public interest. The basic task of this research was to trace the city growth by means of multispectral data and spectral indices and list possible reasons for the changes occurred. Important advantage in case instruments onboard satellites are used in such scenarios are the global coverage, repeatability, provision of historical data sets, and data consistency from one instrument to its successor. Other goal set in this study is establish method for better delineation of built/nobuilt areas as trade off between widely used spectral indices used for change detection in urban areas and the density of the buildings in the selected area derived by means of subpixel mixture analysis. These tasks were achieved by creating several new vector layers corresponding to shape and area of the land use change in the studied area. In the framework of this study used are the freely provided by USGS multispectral data from the series of TM/ETM+ instruments onboard Landsat satellites. The product used for tasks aforementioned is level L1T(G) radiometrically corrected and orthotransformed images that has been verified in large number of experiments and cited in numerous publications. For ground truthing several sources have been used - orthophoto images for visual inspection and CLC vector layers for years 1990, 2000, 2006 and 2012.
Gao, Ce; Weisman, David; Lan, Jiaqi; Gou, Na; Gu, April Z
2015-04-07
The advance in high-throughput "toxicogenomics" technologies, which allows for concurrent monitoring of cellular responses globally upon exposure to chemical toxicants, presents promises for next-generation toxicity assessment. It is recognized that cellular responses to toxicants have a highly dynamic nature, and exhibit both temporal complexity and dose-response shifts. Most current gene enrichment or pathway analysis lack the recognition of the inherent correlation within time series data, and may potentially miss important pathways or yield biased and inconsistent results that ignore dynamic patterns and time-sensitivity. In this study, we investigated the application of two score metrics for GSEA (gene set enrichment analysis) to rank the genes that consider the temporal gene expression profile. One applies a novel time series CPCA (common principal components analysis) to generate scores for genes based on their contributions to the common temporal variation among treatments for a given chemical at different concentrations. Another one employs an integrated altered gene expression quantifier-TELI (transcriptional effect level index) that integrates altered gene expression magnitude over the exposure time. By comparing the GSEA results using two different ranking metrics for examining the dynamic responses of reporter cells treated with various dose levels of three model toxicants, mitomycin C, hydrogen peroxide, and lead nitrate, the analysis identified and revealed different toxicity mechanisms of these chemicals that exhibit chemical-specific, as well as time-aware and dose-sensitive nature. The ability, advantages, and disadvantages of varying ranking metrics were discussed. These findings support the notion that toxicity bioassays should account for the cells' complex dynamic responses, thereby implying that both data acquisition and data analysis should look beyond simple traditional end point responses.
Ma, Xiaoyue; Blanton, Jesse D; Rathbun, Stephen L; Recuenco, Sergio; Rupprecht, Charles E
2010-10-01
To assess the potential impact of oral rabies vaccination (ORV) on the occurrence of raccoon rabies in the mid-Atlantic region, temporal and seasonal trends of raccoon rabies cases reported in West Virginia from 1990 to 2007 were identified with both descriptive statistical analysis and exploratory time series analysis. Raccoon rabies cases in the non-ORV region maintain an enzootic pattern and increase over time; a bimodal seasonal pattern is observed with a large peak in April and a smaller peak in August. The results of the model indicate that the effect of the ORV intervention to control raccoon rabies was statistically significant. ORV should be attempted in other enzootic raccoon rabies areas.
NASA Astrophysics Data System (ADS)
Dutton, Steven James
Particulate air pollution has demonstrated significant health effects ranging from worsening of asthma to increased rates of respiratory and cardiopulmonary mortality. These results have prompted the US-EPA to include particulate matter (PM) as one of the six criteria air pollutants regulated under the Clean Air Act. The diverse chemical make-up and physical characteristics of PM make it a challenging pollutant to characterize and regulate. Particulate matter less than 2.5 microns in diameter (PM2.5) has the ability to travel deep into the lungs and therefore has been linked with some of the more significant health effects. The toxicity of any given particle is likely dependent on its chemical composition. The goal of this project has been to chemically characterize a long time series of PM 2.5 measurements collected at a receptor site in Denver to a level of detail that has not been done before on this size data set. This has involved characterization of inorganic ions using ion chromatography, total elemental and organic carbon using thermal optical transmission, and organic molecular marker species using gas chromatography-mass spectrometry. Methods have been developed to allow for daily measurement and speciation for these compounds over a six year period. Measurement methods, novel approaches to uncertainty estimation, time series analysis, spectral and pattern analyses and source apportionment using two multivariate factor analysis models are presented. Analysis results reveal several natural and anthropogenic sources contributing to PM2.5 in Denver. The most distinguishable sources are motor vehicles and biomass combustion. This information will be used in a health effect analysis as part of a larger study called the Denver Aerosol Sources and Health (DASH) study. Such results will inform regulatory decisions and may help create a better understanding of the underlying mechanisms for the observed adverse health effects associated with PM2.5.
NASA Astrophysics Data System (ADS)
Hausdorff, Jeffrey M.; Balash, Y.; Giladi, Nir
2003-04-01
We quantitatively characterized the fluctuations in the forces under the feet during freezing of gait (FOG) in 11 patients with advanced Parkinson's disease. FOG, a leading cause of falls and impaired functional independence, is a poorly understood debilitating phenomenon that is common among persons with advanced Parkinson's disease. During freezing, the feet are “glued” to the ground and the subject is unable to move forward despite effort in what has been described as an absence of movement. Using time series and fractal analysis methods, we found that FOG is not a frozen akinetic state, nor is freezing random, uncorrelated attempts at overcoming motor blockade. Instead, the forces under the feet oscillate in a fairly organized pattern. However, in contrast to walking and resting leg tremor, the spectrum was broadband and more complex. This complex movement pattern may reflect the activation of multiple networks during FOG.
NASA Astrophysics Data System (ADS)
Shevyrnogov, A.; Vysotskaya, G.; Sukhinin, A.; Frolikova, O.; Tchernetsky, M.
The paper shows the efficiency of an application of the vegetation index image time series to determine long-term vegetation dynamics. The influence of large industrial centers of Siberia on the near-by vegetation is demonstrated. The analysis of the data shows that the influence of industrial waste is stronger in the Siberian North. These regions are characterized by critical conditions for vegetation existence. In the south of the Krasnoyarsk region, human impact is also important, but the possibility of vegetation self-rehabilitation is higher. The present-day economic situation in Russia is unique, with a temporary abrupt fall of industrial production and its following increase. Thus, we managed to analyze the degree of human impact on the environment within a relatively short-time interval.
NASA Astrophysics Data System (ADS)
Klarenberg, G.
2015-12-01
Infrastructure projects such as road paving have proven to bring a variety of (mainly) socio-economic advantages to countries and populations. However, many studies have also highlighted the negative socio-economic and biophysical effects that these developments have at local, regional and even larger scales. The "MAP" area (Madre de Dios in Peru, Acre in Brazil, and Pando in Bolivia) is a biodiversity hotspot in the southwestern Amazon where sections of South America's Inter-Oceanic Highway were paved between 2006 and 2010. We are interested in vegetation dynamics in the area since it plays an important role in ecosystem functions and ecosystem services in socio-ecological systems: it provides information on productivity and structure of the forest. In preparation of more complex and mechanistic simulation of vegetation, non-linear time series analysis and Dynamic Factor Analysis (DFA) was conducted on Enhanced Vegetation Index (EVI) time series - which is a remote sensing product and provides information on vegetation dynamics as it detects chlorophyll (productivity) and structural change. Time series of 30 years for EVI2 (from MODIS and AVHRR) were obtained for 100 communities in the area. Through specific time series cluster analysis of the vegetation data, communities were clustered to facilitate data analysis and pattern recognition. The clustering is spatially consistent, and appears to be driven by median road paving progress - which is different for each cluster. Non-linear time series analysis (multivariate singular spectrum analysis, MSSA) separates common signals (or low-dimensional attractors) across clusters. Despite the presence of this deterministic structure though, time series behavior is mostly stochastic. Granger causality analysis between EVI2 and possible response variables indicates which variables (and with what lags) are to be included in DFA, resulting in unique Dynamic Factor Models for each cluster.
A novel robust index to assess beat-to-beat variability in heart rate time-series analysis.
García-González, M A; Pallàs-Areny, R
2001-06-01
A new index is proposed to estimate the variance of the differentiated heart rate (RR) time series from its truncated histogram. The index is more robust to artifacts than the standard deviation of the differentiated RR time series (rMSDD) and, unlike the pNN50, does not saturate for very high or very low heart rate variability.
NASA Astrophysics Data System (ADS)
Wortham, C.; Zebker, H. A.
2011-12-01
Our analysis focuses on the June 2007 eruption along the East Rift Zone (ERZ) of Kilauea Volcano, Hawaii. The event began with an intrusion at the ERZ and culminated in a small eruption. GPS shows uplift at the ERZ, followed by relaxation, where average north/south velocities are on the order of 19 cm/yr for dates spanning the event and 4 cm/yr following the eruption. Similarly, we see deflation at the Kilauea caldera on the order of 7 cm/yr. Depending on the temporal baseline and spatial location, the expected deformation signal may fall easily within the range of 10 cm or less. We use multiple aperture InSAR (MAI) to estimate of the along-track deformation component missing from traditional satellite-based InSAR. This approach uses split-beam processing to form forward and backward apertures, yielding multiple look vectors with opposing along-track components. Repeat pass measurements are then used to form forward and backward interferograms, where the phase difference between these images is proportional to the deformation in the azimuthal direction. Relative to other along-track methods, such as azimuth offsets, MAI interferograms are computationally inexpensive and offer lower measurement uncertainty. However, compared to InSAR, MAI deformation estimates are highly sensitive to phase errors and can only be used in areas with large signals. This limitation is due to the fundamental tradeoff between sensitivity and SNR in partial aperture processing. Most areas of the Hawaii data set have a deformation signal below the theoretical MAI error of ~10 cm. Thus, a large subset of the available data is unusable when considering only single MAI interferograms. We present the extension of MAI to time-series and quantify the reduction in error for the case where large sets of data are used to jointly estimate deformation over the span of several years. We show that by using time-series analysis, MAI can be used in regions were the deformation signal is below that of the
NASA Astrophysics Data System (ADS)
Stanley, R. H. R.; Jenkins, W. J.; Doney, S. C.; Lott, D. E., III
2015-03-01
We provide a new determination of the annual mean physical supply of nitrate to the euphotic zone in the western subtropical North Atlantic based on a three year time-series of measurements of tritiugenic 3He from 2003 to 2006 in the surface ocean at the Bermuda Atlantic Time-series Study (BATS) site. We combine the 3He data with a sophisticated noble gas calibrated air-sea gas exchange model to constrain the 3He flux across the sea-air interface, which must closely balance the upward 3He flux into the euphotic zone. The product of the 3He flux and the observed subsurface nitrate-3He relationship provides an estimate of the minimum rate of new production in the BATS region. We also applied the gas model to an earlier time series of 3He measurements at BATS in order to recalculate new production fluxes for the 1985 to 1988 time period. The observations, despite an almost three-fold difference in the nitrate-3He relationship, yield a roughly consistent estimate of nitrate flux. In particular, the nitrate flux from 2003-2006 is estimated to be 0.65 ± 0.3 mol m-2 y-1, which is ~ 40% smaller than the calculated flux for the period from 1985 to 1988. The difference between the time periods, which is barely significant, may be due to a real difference in new production resulting from changes in subtropical mode water formation. Overall, the nitrate flux is larger than most estimates of export fluxes or net community production fluxes made locally for BATS site, which is likely a reflection of the larger spatial scale covered by the 3He technique and potentially also by decoupling of 3He and nitrate during obduction of water masses from the main thermocline into the upper ocean.
HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain
Huppert, Theodore J.; Diamond, Solomon G.; Franceschini, Maria A.; Boas, David A.
2009-01-01
Near-infrared spectroscopy (NIRS) is a noninvasive neuroimaging tool for studying evoked hemodynamic changes within the brain. By this technique, changes in the optical absorption of light are recorded over time and are used to estimate the functionally evoked changes in cerebral oxyhemoglobin and deoxyhemoglobin concentrations that result from local cerebral vascular and oxygen metabolic effects during brain activity. Over the past three decades this technology has continued to grow, and today NIRS studies have found many niche applications in the fields of psychology, physiology, and cerebral pathology. The growing popularity of this technique is in part associated with a lower cost and increased portability of NIRS equipment when compared with other imaging modalities, such as functional magnetic resonance imaging and positron emission tomography. With this increasing number of applications, new techniques for the processing, analysis, and interpretation of NIRS data are continually being developed. We review some of the time-series and functional analysis techniques that are currently used in NIRS studies, we describe the practical implementation of various signal processing techniques for removing physiological, instrumental, and motion-artifact noise from optical data, and we discuss the unique aspects of NIRS analysis in comparison with other brain imaging modalities. These methods are described within the context of the MATLAB-based graphical user interface program, HomER, which we have developed and distributed to facilitate the processing of optical functional brain data. PMID:19340120
NASA Astrophysics Data System (ADS)
Sarvan, Darko; Stratimirović, Djordje; Blesić, Suzana; Miljković, Vladimir
2014-12-01
In this paper we have analyzed scaling properties of time series of stock market indices (SMIs) of developing economies of Western Balkans, and have compared the results we have obtained with the results from more developed economies. We have used three different techniques of data analysis to obtain and verify our findings: detrended fluctuation analysis (DFA) method, detrended moving average (DMA) method, and wavelet transformation (WT) analysis. We have found scaling behavior in all SMI data sets that we have analyzed. The scaling of our SMI series changes from long-range correlated to slightly anti-correlated behavior with the change in growth or maturity of the economy the stock market is embedded in. We also report the presence of effects of potential periodic-like influences on the SMI data that we have analyzed. One such influence is visible in all our SMI series, and appears at a period Tp ≈ 90 days. We propose that the existence of various periodic-like influences on SMI data may partially explain the observed difference in types of correlated behavior of corresponding scaling functions.
Hauk, O; Keil, A; Elbert, T; Müller, M M
2002-01-30
We describe a methodology to apply current source density (CSD) and minimum norm (MN) estimation as pre-processing tools for time-series analysis of single trial EEG data. The performance of these methods is compared for the case of wavelet time-frequency analysis of simulated gamma-band activity. A reasonable comparison of CSD and MN on the single trial level requires regularization such that the corresponding transformed data sets have similar signal-to-noise ratios (SNRs). For region-of-interest approaches, it should be possible to optimize the SNR for single estimates rather than for the whole distributed solution. An effective implementation of the MN method is described. Simulated data sets were created by modulating the strengths of a radial and a tangential test dipole with wavelets in the frequency range of the gamma band, superimposed with simulated spatially uncorrelated noise. The MN and CSD transformed data sets as well as the average reference (AR) representation were subjected to wavelet frequency-domain analysis, and power spectra were mapped for relevant frequency bands. For both CSD and MN, the influence of noise can be sufficiently suppressed by regularization to yield meaningful information, but only MN represents both radial and tangential dipole sources appropriately as single peaks. Therefore, when relating wavelet power spectrum topographies to their neuronal generators, MN should be preferred.
NASA Astrophysics Data System (ADS)
Stewart, C.; Schiavon, G.; Lasaponara, R.
2012-04-01
A study is being carried out to analyse the potential of high resolution polarimetric SAR data to detect seasonal and yearly changes for archaeological applications. The area under study includes the city of Rome and the area to the south east of the city, towards the Alban Hills. The data comprises Radarsat 2 Fine Quad data of various beams, from FQ2 to FQ19, acquired throughout 2008 and 2011, and provided within the Science and Operational Application Research for RADARSAT-2 program SOAR Project 1488 and SOAR-EU Project 6795. Two different analyses are performed: One is a seasonal analysis, investigating changes taking place on a monthly basis in 2008; the other is an analysis of changes taking place between 2008 and 2011. The processing chain involves the following: multilooking, extraction of the T3 matrix, speckle filtering, geocoding, and finally the application of a range of coherent and incoherent polarimetric decompositions. The software NEST and PolSARPro (both ESA OS software) are used for this processing chain. Different change detection techniques are then discussed for analysis of the dataset to detect potential changes in the time series.
NASA Astrophysics Data System (ADS)
Alakent, Burak; Camurdan, Mehmet C.; Doruker, Pemra
2005-10-01
Time series analysis tools are employed on the principal modes obtained from the Cα trajectories from two independent molecular-dynamics simulations of α-amylase inhibitor (tendamistat). Fluctuations inside an energy minimum (intraminimum motions), transitions between minima (interminimum motions), and relaxations in different hierarchical energy levels are investigated and compared with those encountered in vacuum by using different sampling window sizes and intervals. The low-frequency low-indexed mode relationship, established in vacuum, is also encountered in water, which shows the reliability of the important dynamics information offered by principal components analysis in water. It has been shown that examining a short data collection period (100ps) may result in a high population of overdamped modes, while some of the low-frequency oscillations (<10cm-1) can be captured in water by using a longer data collection period (1200ps). Simultaneous analysis of short and long sampling window sizes gives the following picture of the effect of water on protein dynamics. Water makes the protein lose its memory: future conformations are less dependent on previous conformations due to the lowering of energy barriers in hierarchical levels of the energy landscape. In short-time dynamics (<10ps), damping factors extracted from time series model parameters are lowered. For tendamistat, the friction coefficient in the Langevin equation is found to be around 40-60cm-1 for the low-indexed modes, compatible with literature. The fact that water has increased the friction and that on the other hand has lubrication effect at first sight contradicts. However, this comes about because water enhances the transitions between minima and forces the protein to reduce its already inherent inability to maintain oscillations observed in vacuum. Some of the frequencies lower than 10cm-1 are found to be overdamped, while those higher than 20cm-1 are slightly increased. As for the long
Regenerating time series from ordinal networks.
McCullough, Michael; Sakellariou, Konstantinos; Stemler, Thomas; Small, Michael
2017-03-01
Recently proposed ordinal networks not only afford novel methods of nonlinear time series analysis but also constitute stochastic approximations of the deterministic flow time series from which the network models are constructed. In this paper, we construct ordinal networks from discrete sampled continuous chaotic time series and then regenerate new time series by taking random walks on the ordinal network. We then investigate the extent to which the dynamics of the original time series are encoded in the ordinal networks and retained through the process of regenerating new time series by using several distinct quantitative approaches. First, we use recurrence quantification analysis on traditional recurrence plots and order recurrence plots to compare the temporal structure of the original time series with random walk surrogate time series. Second, we estimate the largest Lyapunov exponent from the original time series and investigate the extent to which this invariant measure can be estimated from the surrogate time series. Finally, estimates of correlation dimension are computed to compare the topological properties of the original and surrogate time series dynamics. Our findings show that ordinal networks constructed from univariate time series data constitute stochastic models which approximate important dynamical properties of the original systems.
Regenerating time series from ordinal networks
NASA Astrophysics Data System (ADS)
McCullough, Michael; Sakellariou, Konstantinos; Stemler, Thomas; Small, Michael
2017-03-01
Recently proposed ordinal networks not only afford novel methods of nonlinear time series analysis but also constitute stochastic approximations of the deterministic flow time series from which the network models are constructed. In this paper, we construct ordinal networks from discrete sampled continuous chaotic time series and then regenerate new time series by taking random walks on the ordinal network. We then investigate the extent to which the dynamics of the original time series are encoded in the ordinal networks and retained through the process of regenerating new time series by using several distinct quantitative approaches. First, we use recurrence quantification analysis on traditional recurrence plots and order recurrence plots to compare the temporal structure of the original time series with random walk surrogate time series. Second, we estimate the largest Lyapunov exponent from the original time series and investigate the extent to which this invariant measure can be estimated from the surrogate time series. Finally, estimates of correlation dimension are computed to compare the topological properties of the original and surrogate time series dynamics. Our findings show that ordinal networks constructed from univariate time series data constitute stochastic models which approximate important dynamical properties of the original systems.
Escalera-Antezana, Juan Pablo; Dadvand, Payam; Llatje, Òscar; Barrera-Gómez, Jose; Cunillera, Jordi; Medina-Ramón, Mercedes; Pérez, Katherine
2015-01-01
Background Experimental studies have shown a decrease in driving performance at high temperatures. The epidemiological evidence for the relationship between heat and motor vehicle crashes is not consistent. Objectives We estimated the impact of high ambient temperatures on the daily number of motor vehicle crashes and, in particular, on crashes involving driver performance factors (namely distractions, driver error, fatigue, or sleepiness). Methods We performed a time-series analysis linking daily counts of motor vehicle crashes and daily temperature or occurrence of heat waves while controlling for temporal trends. All motor vehicle crashes with victims that occurred during the warm period of the years 2000–2011 in Catalonia (Spain) were included. Temperature data were obtained from 66 weather stations covering the region. Poisson regression models adjusted for precipitation, day of the week, month, year, and holiday periods were fitted to quantify the associations. Results The study included 118,489 motor vehicle crashes (an average of 64.1 per day). The estimated risk of crashes significantly increased by 2.9% [95% confidence interval (CI): 0.7%, 5.1%] during heat wave days, and this association was stronger (7.7%, 95% CI: 1.2%, 14.6%) when restricted to crashes with driver performance–associated factors. The estimated risk of crashes with driver performance factors significantly increased by 1.1% (95% CI: 0.1%, 2.1%) for each 1°C increase in maximum temperature. Conclusions Motor vehicle crashes involving driver performance–associated factors were increased in association with heat waves and increasing temperature. These findings are relevant for designing preventive plans in a context of global warming. Citation Basagaña X, Escalera-Antezana JP, Dadvand P, Llatje Ò, Barrera-Gómez J, Cunillera J, Medina-Ramón M, Pérez K. 2015. High ambient temperatures and risk of motor vehicle crashes in Catalonia, Spain (2000–2011): a time-series analysis
Evaluating the impact of PCV-10 on invasive pneumococcal disease in Brazil: A time-series analysis
Andrade, Ana Lucia; Minamisava, Ruth; Policena, Gabriela; Cristo, Elier B; Domingues, Carla Magda S; de Cunto Brandileone, Maria Cristina; Almeida, Samanta Cristine Grassi; Toscano, Cristiana Maria; Bierrenbach, Ana Luiza
2016-01-01
Routine infant immunization with 10-valent pneumococcal conjugate vaccine (PCV-10) began in Brazil in 2010. The impact of the PCV-10 on rates of invasive pneumococcal disease (IPD) at the population level was not yet evaluated. Serotype-specific IPD changes after PCV-10 introduction is still to be determined. Data from national surveillance system for notifiable diseases (SINAN) and national reference laboratory for S. pneumoniae in Brazil (IAL) were linked to enhance case ascertainment of IPD. An interrupted time-series analysis was conducted to predict trends in the postvaccination IPD rates in the absence of PCV-10 vaccination, taking into consideration seasonality and secular trends. PCVs serotype-specific distribution were assessed before (2008–2009) and after (2011–2013) the introduction of PCV-10 in the immunization program. A total of 9,827 IPD cases were identified from 2008–2013 when combining SINAN and IAL databases. Overall, PCV-10 types decreased by 41.3% after PCV-10 vaccination period, mostly in children aged 2–23 months, while additional PCV-13 serotypes increased by 62.8% mainly in children under 5-year of age. For children aged 2–23 months, targeted by the immunization program, we observed a 44.2% (95%CI, 15.8–72.5%) reduction in IPD rates. In contrast, significant increase in IPD rates were observed for adults aged 18–39 y (18.9%, 95%CI 1.1–36.7%), 40–64 y (52.5%, 95%CI 24.8–80.3%), and elderly ≥ 65 y (79.3%, 95%CI 62.1–96.5%). This is the first report of a time-series analysis for PCV impact in IPD conducted at national level data in a developing country. We were able to show significant impact of PCV-10 on IPD for age groups targeted by vaccination in Brazil, 3 y after its introduction. No impact on other age groups was demonstrated. PMID:26905679
Evaluating the impact of PCV-10 on invasive pneumococcal disease in Brazil: A time-series analysis.
Andrade, Ana Lucia; Minamisava, Ruth; Policena, Gabriela; Cristo, Elier B; Domingues, Carla Magda S; de Cunto Brandileone, Maria Cristina; Almeida, Samanta Cristine Grassi; Toscano, Cristiana Maria; Bierrenbach, Ana Luiza
2016-01-01
Routine infant immunization with 10-valent pneumococcal conjugate vaccine (PCV-10) began in Brazil in 2010. The impact of the PCV-10 on rates of invasive pneumococcal disease (IPD) at the population level was not yet evaluated. Serotype-specific IPD changes after PCV-10 introduction is still to be determined. Data from national surveillance system for notifiable diseases (SINAN) and national reference laboratory for S. pneumoniae in Brazil (IAL) were linked to enhance case ascertainment of IPD. An interrupted time-series analysis was conducted to predict trends in the postvaccination IPD rates in the absence of PCV-10 vaccination, taking into consideration seasonality and secular trends. PCVs serotype-specific distribution were assessed before (2008-2009) and after (2011-2013) the introduction of PCV-10 in the immunization program. A total of 9,827 IPD cases were identified from 2008-2013 when combining SINAN and IAL databases. Overall, PCV-10 types