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Author: Ray G. Huffaker Publisher: Oxford University Press ISBN: 0198782934 Category : Computers Languages : en Pages : 371
Book Description
Nonlinear Time Series Analysis with R provides a practical guide to emerging empirical techniques allowing practitioners to diagnose whether highly fluctuating and random appearing data are most likely driven by random or deterministic dynamic forces. Practitioners become 'data detectives' accumulating hard empirical evidence supporting their choice of a modelling approach corresponding to reality. The book is targeted to non-mathematicians with limitedknowledge of nonlinear dynamics; in particular, professionals and graduate students in engineering and the biophysical and social sciences. The book makes readers active learners with hands-on computerexperiments in R code directing them through Nonlinear Time Series Analysis (NLTS). The computer code is explained in detail so that readers can adjust it for use in their own work. The book also provides readers with an explicit framework--condensed from sound empirical practices recommended in the literature--that details a step-by-step procedure for applying NLTS in real-world data diagnostics.
Author: Ray G. Huffaker Publisher: Oxford University Press ISBN: 0198782934 Category : Computers Languages : en Pages : 371
Book Description
Nonlinear Time Series Analysis with R provides a practical guide to emerging empirical techniques allowing practitioners to diagnose whether highly fluctuating and random appearing data are most likely driven by random or deterministic dynamic forces. Practitioners become 'data detectives' accumulating hard empirical evidence supporting their choice of a modelling approach corresponding to reality. The book is targeted to non-mathematicians with limitedknowledge of nonlinear dynamics; in particular, professionals and graduate students in engineering and the biophysical and social sciences. The book makes readers active learners with hands-on computerexperiments in R code directing them through Nonlinear Time Series Analysis (NLTS). The computer code is explained in detail so that readers can adjust it for use in their own work. The book also provides readers with an explicit framework--condensed from sound empirical practices recommended in the literature--that details a step-by-step procedure for applying NLTS in real-world data diagnostics.
Author: Ruey S. Tsay Publisher: John Wiley & Sons ISBN: 1119264065 Category : Mathematics Languages : en Pages : 516
Book Description
A comprehensive resource that draws a balance between theory and applications of nonlinear time series analysis Nonlinear Time Series Analysis offers an important guide to both parametric and nonparametric methods, nonlinear state-space models, and Bayesian as well as classical approaches to nonlinear time series analysis. The authors—noted experts in the field—explore the advantages and limitations of the nonlinear models and methods and review the improvements upon linear time series models. The need for this book is based on the recent developments in nonlinear time series analysis, statistical learning, dynamic systems and advanced computational methods. Parametric and nonparametric methods and nonlinear and non-Gaussian state space models provide a much wider range of tools for time series analysis. In addition, advances in computing and data collection have made available large data sets and high-frequency data. These new data make it not only feasible, but also necessary to take into consideration the nonlinearity embedded in most real-world time series. This vital guide: • Offers research developed by leading scholars of time series analysis • Presents R commands making it possible to reproduce all the analyses included in the text • Contains real-world examples throughout the book • Recommends exercises to test understanding of material presented • Includes an instructor solutions manual and companion website Written for students, researchers, and practitioners who are interested in exploring nonlinearity in time series, Nonlinear Time Series Analysis offers a comprehensive text that explores the advantages and limitations of the nonlinear models and methods and demonstrates the improvements upon linear time series models.
Author: Holger Kantz Publisher: Cambridge University Press ISBN: 9780521529020 Category : Mathematics Languages : en Pages : 390
Book Description
The paradigm of deterministic chaos has influenced thinking in many fields of science. Chaotic systems show rich and surprising mathematical structures. In the applied sciences, deterministic chaos provides a striking explanation for irregular behaviour and anomalies in systems which do not seem to be inherently stochastic. The most direct link between chaos theory and the real world is the analysis of time series from real systems in terms of nonlinear dynamics. Experimental technique and data analysis have seen such dramatic progress that, by now, most fundamental properties of nonlinear dynamical systems have been observed in the laboratory. Great efforts are being made to exploit ideas from chaos theory wherever the data displays more structure than can be captured by traditional methods. Problems of this kind are typical in biology and physiology but also in geophysics, economics, and many other sciences.
Author: Randal Douc Publisher: CRC Press ISBN: 1466502347 Category : Mathematics Languages : en Pages : 548
Book Description
This text emphasizes nonlinear models for a course in time series analysis. After introducing stochastic processes, Markov chains, Poisson processes, and ARMA models, the authors cover functional autoregressive, ARCH, threshold AR, and discrete time series models as well as several complementary approaches. They discuss the main limit theorems for Markov chains, useful inequalities, statistical techniques to infer model parameters, and GLMs. Moving on to HMM models, the book examines filtering and smoothing, parametric and nonparametric inference, advanced particle filtering, and numerical methods for inference.
Author: Jan G. De Gooijer Publisher: Springer ISBN: 3319432524 Category : Mathematics Languages : en Pages : 626
Book Description
This book provides an overview of the current state-of-the-art of nonlinear time series analysis, richly illustrated with examples, pseudocode algorithms and real-world applications. Avoiding a “theorem-proof” format, it shows concrete applications on a variety of empirical time series. The book can be used in graduate courses in nonlinear time series and at the same time also includes interesting material for more advanced readers. Though it is largely self-contained, readers require an understanding of basic linear time series concepts, Markov chains and Monte Carlo simulation methods. The book covers time-domain and frequency-domain methods for the analysis of both univariate and multivariate (vector) time series. It makes a clear distinction between parametric models on the one hand, and semi- and nonparametric models/methods on the other. This offers the reader the option of concentrating exclusively on one of these nonlinear time series analysis methods. To make the book as user friendly as possible, major supporting concepts and specialized tables are appended at the end of every chapter. In addition, each chapter concludes with a set of key terms and concepts, as well as a summary of the main findings. Lastly, the book offers numerous theoretical and empirical exercises, with answers provided by the author in an extensive solutions manual.
Author: Andreas Galka Publisher: World Scientific ISBN: 9814493929 Category : Science Languages : en Pages : 360
Book Description
This book provides a thorough review of a class of powerful algorithms for the numerical analysis of complex time series data which were obtained from dynamical systems. These algorithms are based on the concept of state space representations of the underlying dynamics, as introduced by nonlinear dynamics. In particular, current algorithms for state space reconstruction, correlation dimension estimation, testing for determinism and surrogate data testing are presented — algorithms which have been playing a central role in the investigation of deterministic chaos and related phenomena since 1980. Special emphasis is given to the much-disputed issue whether these algorithms can be successfully employed for the analysis of the human electroencephalogram.
Author: Philip Rothman Publisher: Springer Science & Business Media ISBN: 0792383796 Category : Business & Economics Languages : en Pages : 394
Book Description
Nonlinear Time Series Analysis of Economic and Financial Data provides an examination of the flourishing interest that has developed in this area over the past decade. The constant theme throughout this work is that standard linear time series tools leave unexamined and unexploited economically significant features in frequently used data sets. The book comprises original contributions written by specialists in the field, and offers a combination of both applied and methodological papers. It will be useful to both seasoned veterans of nonlinear time series analysis and those searching for an informative panoramic look at front-line developments in the area.
Author: Jonathan D. Cryer Publisher: Springer Science & Business Media ISBN: 0387759581 Category : Business & Economics Languages : en Pages : 501
Book Description
This book presents an accessible approach to understanding time series models and their applications. The ideas and methods are illustrated with both real and simulated data sets. A unique feature of this edition is its integration with the R computing environment.
Author: Ray G. Huffaker
Author: Ray G. Huffaker
Author: Ruey S. Tsay
Author: Holger Kantz
Author: Randal Douc
Author: Jan G. De Gooijer
Author: Andreas Galka
Author: Christian Ritz
Author: Philip Rothman
Author: Jonathan D. Cryer
Author: Robert H. Shumway