Threshold Models in Non-linear Time Series Analysis
Threshold Models in Non-linear Time Series Analysis

Author: H. Tong

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 333

ISBN-13: 1468478885

DOWNLOAD EBOOK

In the last two years or so, I was most fortunate in being given opportunities of lecturing on a new methodology to a variety of audiences in Britain, China, Finland, France and Spain. Despite my almost Confucian attitude of preferring talking (i.e. a transient record) to writing (i.e. a permanent record), the warm encouragement of friends has led to the ensuing notes. I am also only too conscious of the infancy of the methodology introduced in these notes. However, it is my sincere hope that exposure to a wider audience will accelerate its maturity. Readers are assumed to be familiar with the basic theory of time series analysis. The book by Professor M.B. Priestley (1981) may be used as a general reference. Chapter One is addressed to the general question: "why do we need non-linear time series models?" After describing some significant advantages of linear models, it singles out several major limitations of linearity. Of course, the selection reflects my personal view on the subject, which is only at its very beginning, although there does seem to be a general agreement in the literature that time irr'eversibility and limit cycles are among the most obvious.

Threshold Models in Non-linear Time Series Analysis
Threshold Models in Non-linear Time Series Analysis

Author: H. Tong

Publisher: Springer

Published: 1983-11

Total Pages: 348

ISBN-13:

DOWNLOAD EBOOK

In the last two years or so, I was most fortunate in being given opportunities of lecturing on a new methodology to a variety of audiences in Britain, China, Finland, France and Spain. Despite my almost Confucian attitude of preferring talking (i.e. a transient record) to writing (i.e. a permanent record), the warm encouragement of friends has led to the ensuing notes. I am also only too conscious of the infancy of the methodology introduced in these notes. However, it is my sincere hope that exposure to a wider audience will accelerate its maturity. Readers are assumed to be familiar with the basic theory of time series analysis. The book by Professor M.B. Priestley (1981) may be used as a general reference. Chapter One is addressed to the general question: "why do we need non-linear time series models?" After describing some significant advantages of linear models, it singles out several major limitations of linearity. Of course, the selection reflects my personal view on the subject, which is only at its very beginning, although there does seem to be a general agreement in the literature that time irr'eversibility and limit cycles are among the most obvious.

Nonlinear Time Series Analysis
Nonlinear Time Series Analysis

Author: Ruey S. Tsay

Publisher: John Wiley & Sons

Published: 2018-09-13

Total Pages: 516

ISBN-13: 1119264065

DOWNLOAD EBOOK

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.

Nonlinear Time Series
Nonlinear Time Series

Author: Randal Douc

Publisher: CRC Press

Published: 2014-01-06

Total Pages: 548

ISBN-13: 1466502347

DOWNLOAD EBOOK

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.

Non-linear Time Series
Non-linear Time Series

Author: Howell Tong

Publisher: Oxford University Press, USA

Published: 1990

Total Pages: 592

ISBN-13:

DOWNLOAD EBOOK

Written by an internationally recognized expert in the field, this book provides a valuable introduction to the rapidly growing area of non-linear time series. Because developments in the study of dynamical systems have motivated many of the advances discussed here, the author's coverage includes such fundamental concepts of dynamical systems theory as limit cycles, Lyapunov functions, thresholds, and stability, with detailed descriptions of their role in the analysis of non-linear time series data. As the first accessible and comprehensive account of these exciting new developments, this unique volume bridges the gap between linear and chaotic time series analysis. Both statisticians and dynamical systems theorists will value its survey of recent developments and the present state of research, as well as the discussion of a number of unsolved problems in the field.

Nonlinear Time Series Analysis
Nonlinear Time Series Analysis

Author: Holger Kantz

Publisher: Cambridge University Press

Published: 2004

Total Pages: 390

ISBN-13: 9780521529020

DOWNLOAD EBOOK

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.

Nonlinear Time Series Analysis with R
Nonlinear Time Series Analysis with R

Author: Ray Huffaker

Publisher: Oxford University Press

Published: 2017-10-20

Total Pages: 312

ISBN-13: 0191085790

DOWNLOAD EBOOK

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.

Time Series Analysis
Time Series Analysis

Author: Jonathan D. Cryer

Publisher: Springer Science & Business Media

Published: 2008-04-04

Total Pages: 501

ISBN-13: 0387759581

DOWNLOAD EBOOK

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.