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| Author | : Michael Small |
| Publisher | : World Scientific |
| Total Pages | : 262 |
| Release | : 2005 |
| Genre | : Mathematics |
| ISBN | : 981256117X |
A collection of photographs focusing on the fading traditions, heritage and culture in County Cork Ireland.
| Author | : Holger Kantz |
| Publisher | : Cambridge University Press |
| Total Pages | : 390 |
| Release | : 2004 |
| Genre | : Mathematics |
| ISBN | : 9780521529020 |
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 | : Ruey S. Tsay |
| Publisher | : John Wiley & Sons |
| Total Pages | : 512 |
| Release | : 2018-09-14 |
| Genre | : Mathematics |
| ISBN | : 1119264073 |
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 | : Randal Douc |
| Publisher | : CRC Press |
| Total Pages | : 548 |
| Release | : 2014-01-06 |
| Genre | : Mathematics |
| ISBN | : 1466502347 |
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 | : Ray Huffaker |
| Publisher | : Oxford University Press |
| Total Pages | : 312 |
| Release | : 2017-10-20 |
| Genre | : Mathematics |
| ISBN | : 0191085790 |
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. It joins the chorus of voices recommending 'getting to know your data' as an essential preliminary evidentiary step in modelling. Time series are often highly fluctuating with a random appearance. Observed volatility is commonly attributed to exogenous random shocks to stable real-world systems. However, breakthroughs in nonlinear dynamics raise another possibility: highly complex dynamics can emerge endogenously from astoundingly parsimonious deterministic nonlinear models. Nonlinear Time Series Analysis (NLTS) is a collection of empirical tools designed to aid practitioners detect whether stochastic or deterministic dynamics most likely drive observed complexity. Practitioners become 'data detectives' accumulating hard empirical evidence supporting their modelling approach. This book is targeted to professionals and graduate students in engineering and the biophysical and social sciences. Its major objectives are to help non-mathematicians — with limited knowledge of nonlinear dynamics — to become operational in NLTS; and in this way to pave the way for NLTS to be adopted in the conventional empirical toolbox and core coursework of the targeted disciplines. Consistent with modern trends in university instruction, the book makes readers active learners with hands-on computer experiments in R code directing them through NLTS methods and helping them understand the underlying logic (please see www.marco.bittelli.com). 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 | : Jan G. De Gooijer |
| Publisher | : Springer |
| Total Pages | : 618 |
| Release | : 2017-03-30 |
| Genre | : Mathematics |
| ISBN | : 3319432524 |
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 | : Jiti Gao |
| Publisher | : CRC Press |
| Total Pages | : 249 |
| Release | : 2007-03-22 |
| Genre | : Mathematics |
| ISBN | : 1420011219 |
Useful in the theoretical and empirical analysis of nonlinear time series data, semiparametric methods have received extensive attention in the economics and statistics communities over the past twenty years. Recent studies show that semiparametric methods and models may be applied to solve dimensionality reduction problems arising from using fully
| Author | : Michael Small |
| Publisher | : World Scientific |
| Total Pages | : 261 |
| Release | : 2005-03-28 |
| Genre | : Science |
| ISBN | : 981448122X |
Nonlinear time series methods have developed rapidly over a quarter of a century and have reached an advanced state of maturity during the last decade. Implementations of these methods for experimental data are now widely accepted and fairly routine; however, genuinely useful applications remain rare. This book focuses on the practice of applying these methods to solve real problems.To illustrate the usefulness of these methods, a wide variety of physical and physiological systems are considered. The technical tools utilized in this book fall into three distinct, but interconnected areas: quantitative measures of nonlinear dynamics, Monte-Carlo statistical hypothesis testing, and nonlinear modeling. Ten highly detailed applications serve as case studies of fruitful applications and illustrate the mathematical techniques described in the text.
| Author | : Andreas Galka |
| Publisher | : World Scientific |
| Total Pages | : 360 |
| Release | : 2000-02-18 |
| Genre | : Science |
| ISBN | : 9814493929 |
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. Contents:Dynamical Systems, Time Series and AttractorsLinear MethodsState Space Reconstruction: Theoretical FoundationsState Space Reconstruction: Practical ApplicationDimensions: Basic DefinitionsLyapunov Exponents and EntropiesNumerical Estimation of the Correlation DimensionSources of Error and Data Set Size RequirementsMonte Carlo Analysis of Dimension EstimationSurrogate Data TestsDimension Analysis of the Human EEGTesting for Determinism in Time Series Readership: Graduates and scientists in physics, applied mathematics, neurology, theoretical biology, economics, meteorology and neuroinformatics. Keywords:Time Series Analysis;Nonlinear Dynamics;Fractal Dimension;Correlation Dimension;Chaos;Electroencephalogram;EEG;Determinism;Strange Attractor;Embedding;Attractor Reconstruction;Surrogate DataReviews: “The book is pleasantly written and makes for easy reading. It is informative for anyone with a sufficiently deep knowledge of nonlinear dynamics.” Mathematical Reviews
| Author | : Achintya Mukhopadhyay |
| Publisher | : Springer Nature |
| Total Pages | : 526 |
| Release | : 2019-10-14 |
| Genre | : Technology & Engineering |
| ISBN | : 9811505365 |
This book presents recent advances in dynamics and control of different types of energy systems. It covers research on dynamics and control in energy systems from different aspects, namely, combustion, multiphase flow, nuclear, chemical and thermal. The chapters start from the basic concepts so that this book can be useful even for researchers with very little background in the area. A dedicated chapter provides an overview on the fundamental aspects of the dynamical systems approach. The book will be of use to researchers and professionals alike.
