The Experts below are selected from a list of 300 Experts worldwide ranked by ideXlab platform
Holger Kantz - One of the best experts on this subject based on the ideXlab platform.
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Nonlinear time-Series Analysis revisited
Chaos, 2015Co-Authors: Elizabeth Bradley, Holger KantzAbstract:In 1980 and 1981, two pioneering papers laid the foundation for what became known as nonlinear time-Series Analysis: the Analysis of observed data—typically univariate—via dynamical systems theory. Based on the concept of state-space reconstruction, this set of methods allows us to compute characteristic quantities such as Lyapunov exponents and fractal dimensions, to predict the future course of the time Series, and even to reconstruct the equations of motion in some cases. In practice, however, there are a number of issues that restrict the power of this approach: whether the signal accurately and thoroughly samples the dynamics, for instance, and whether it contains noise. Moreover, the numerical algorithms that we use to instantiate these ideas are not perfect; they involve approximations, scale parameters, and finite-precision arithmetic, among other things. Even so, nonlinear time-Series Analysis has been used to great advantage on thousands of real and synthetic data sets from a wide variety of sys...
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Nonlinear time-Series Analysis revisited
Chaos, 2015Co-Authors: Elizabeth Bradley, Holger KantzAbstract:In 1980 and 1981, two pioneering papers laid the foundation for what became known as nonlinear time-Series Analysis: the Analysis of observed data---typically univariate---via dynamical systems theory. Based on the concept of state-space reconstruction, this set of methods allows us to compute characteristic quantities such as Lyapunov exponents and fractal dimensions, to predict the future course of the time Series, and even to reconstruct the equations of motion in some cases. In practice, however, there are a number of issues that restrict the power of this approach: whether the signal accurately and thoroughly samples the dynamics, for instance, and whether it contains noise. Moreover, the numerical algorithms that we use to instantiate these ideas are not perfect; they involve approximations, scale parameters, and finite-precision arithmetic, among other things. Even so, nonlinear time-Series Analysis has been used to great advantage on thousands of real and synthetic data sets from a wide variety of systems ranging from roulette wheels to lasers to the human heart. Even in cases where the data do not meet the mathematical or algorithmic requirements to assure full topological conjugacy, the results of nonlinear time-Series Analysis can be helpful in understanding, characterizing, and predicting dynamical systems.
Elizabeth Bradley - One of the best experts on this subject based on the ideXlab platform.
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Nonlinear time-Series Analysis revisited
Chaos, 2015Co-Authors: Elizabeth Bradley, Holger KantzAbstract:In 1980 and 1981, two pioneering papers laid the foundation for what became known as nonlinear time-Series Analysis: the Analysis of observed data—typically univariate—via dynamical systems theory. Based on the concept of state-space reconstruction, this set of methods allows us to compute characteristic quantities such as Lyapunov exponents and fractal dimensions, to predict the future course of the time Series, and even to reconstruct the equations of motion in some cases. In practice, however, there are a number of issues that restrict the power of this approach: whether the signal accurately and thoroughly samples the dynamics, for instance, and whether it contains noise. Moreover, the numerical algorithms that we use to instantiate these ideas are not perfect; they involve approximations, scale parameters, and finite-precision arithmetic, among other things. Even so, nonlinear time-Series Analysis has been used to great advantage on thousands of real and synthetic data sets from a wide variety of sys...
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Nonlinear time-Series Analysis revisited
Chaos, 2015Co-Authors: Elizabeth Bradley, Holger KantzAbstract:In 1980 and 1981, two pioneering papers laid the foundation for what became known as nonlinear time-Series Analysis: the Analysis of observed data---typically univariate---via dynamical systems theory. Based on the concept of state-space reconstruction, this set of methods allows us to compute characteristic quantities such as Lyapunov exponents and fractal dimensions, to predict the future course of the time Series, and even to reconstruct the equations of motion in some cases. In practice, however, there are a number of issues that restrict the power of this approach: whether the signal accurately and thoroughly samples the dynamics, for instance, and whether it contains noise. Moreover, the numerical algorithms that we use to instantiate these ideas are not perfect; they involve approximations, scale parameters, and finite-precision arithmetic, among other things. Even so, nonlinear time-Series Analysis has been used to great advantage on thousands of real and synthetic data sets from a wide variety of systems ranging from roulette wheels to lasers to the human heart. Even in cases where the data do not meet the mathematical or algorithmic requirements to assure full topological conjugacy, the results of nonlinear time-Series Analysis can be helpful in understanding, characterizing, and predicting dynamical systems.
Christoph Bandt - One of the best experts on this subject based on the ideXlab platform.
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Ordinal time Series Analysis
Ecological Modelling, 2005Co-Authors: Christoph BandtAbstract:Abstract We discuss robust methods of time Series Analysis which use only comparisons of values and not their actual size. Local and global order structure are defined as matrices or by rank numbers. Local ranks, autocorrelation by Kendall’s tau, and permutation entropy as complexity measure are introduced in such a way that they contain a scale parameter which allows to study time Series on different scales.
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Ordinal time Series Analysis
Ecological Modelling, 2005Co-Authors: Christoph BandtAbstract:We discuss robust methods of time Series Analysis which use only comparisons of values and not their actual size. Local and global order structure are defined as matrices or by rank numbers. Local ranks, autocorrelation by Kendall's tau, and permutation entropy as complexity measure are introduced in such a way that they contain a scale parameter which allows to study time Series on different scales. ?? 2004 Published by Elsevier B.V.
Youseop Shin - One of the best experts on this subject based on the ideXlab platform.
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Time Series Analysis in the Social Sciences
2017Co-Authors: Youseop ShinAbstract:This book focuses on fundamental elements of time Series Analysis that social scientists need to understand to employ time Series Analysis for their research and practice. Avoiding extraordinary mathematical materials, this book explains univariate time-Series Analysis step by step from the preliminary visual Analysis through the modeling of seasonality, trends, and residuals to the prediction and the evaluation of estimated models. Then, this book explains smoothing, multiple time-Series Analysis, and interrupted time-Series Analysis. At the end of each step, this book coherently provides an Analysis of the monthly violent crime rates as an example.
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Time Series Analysis in the Social Sciences
Time Series Analysis in the Social Sciences, 2017Co-Authors: Youseop ShinAbstract:This chapter explains how time Series Analysis has been applied in the social sciences.
Juan Gabriel Brida - One of the best experts on this subject based on the ideXlab platform.
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Symbolic time Series Analysis and dynamic regimes
Structural Change and Economic Dynamics, 2003Co-Authors: Juan Gabriel Brida, Lionello F. PunzoAbstract:In this paper I describe and apply the methods of Symbolic Time Series Analysis (STSA) to an experimental framework. The idea behind Symbolic Time Series Analysis is simple: the values of a given time Series data are transformed into a finite set of symbols obtaining a finite string. Then, we can process the symbolic sequence using tools from information theory and symbolic dynamics. I discuss data symbolization as a tool for identifying temporal patterns in experimental data and use symbol sequence statistics in a model strategy. In this application the data symbolization is based on economic criteria using the notion of economic regime.
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Symbolic Time Series Analysis in Economics
2000Co-Authors: Juan Gabriel BridaAbstract:In this paper I describe and apply the methods of Symbolic Time Series Analysis (STSA) to an experimental framework. The idea behind Symbolic Time Series Analysis is simple: the values of a given time Series data are transformed into a finite set of symbols obtaining a finite string. Then, we can process the symbolic sequence using tools from information theory and symbolic dynamics. I discuss data symbolization as a tool for identifying temporal patterns in experimental data and use symbol sequence statistics in a model strategy. To explain these applications, I describe methods to select the symbolization of the data (Section 2), I introduce the symbolic sequence histograms and some tools to characterize and compare these histograms (Section 3). I show that the methods of symbolic time Series Analysis can be a good tool to describe and recognize time patterns in complex dynamical processes and to extract dynamical information about this kind of system. In particular, the method gives us a language in which to express and analyze these time patterns. In section 4 I report some applications of STSA to study the evolution of ifferent economies. In these applications data symbolization is based on economic criteria using the notion of economic regime introduced earlier in this thesis. I use STSA methods to describe the dynamical behavior of these economies and to do comparative Analysis of their regime dynamics. In section 5 I use STSA to reconstruct a model of a dynamical system from measured time Series data. In particular, I will show how the observed symbolic sequence statistics can be used as a target for measuring the goodness of fit of proposed models.
