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Thursday, August 6, 2020 | History

5 edition of Nonlinear Time Series found in the catalog.

Nonlinear Time Series

Nonparametric and Parametric Methods (Springer Series in Statistics)

by Jianqing Fan

  • 286 Want to read
  • 40 Currently reading

Published by Springer .
Written in English


The Physical Object
Number of Pages552
ID Numbers
Open LibraryOL7444943M
ISBN 100387261427
ISBN 109780387261423

a software project for the analysis of time series with methods based on the theory of nonlinear deterministic dynamical systems, or chaos theory, if you prefer. It has grown out of the work of our groups during the last few years. TISEANcopyright and citation. Get this from a library! Nonlinear time series analysis. [Holger Kantz; Thomas Schreiber] -- Topics include phase space embeddings, nonlinear prediction and noise reduction, Lyapunov exponents, dimensions and entropies, chaos control, wavelet analysis and pattern dynamics. All material is.

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. TISEAN Nonlinear Time Series Analysis: Rainer Hegger Holger Kantz Thomas Schreiber Go to Version (released March ) Go to Version (released December ).

Get this from a library! Nonlinear time series analysis. [Holger Kantz; Thomas Schreiber] -- "This book represents a modern approach to time series analysis which is based on the theory of dynamical systems. It starts from a sound outline of the underlying theory to arrive at very practical. This book is a collection of 14 original research articles presented at the conference Nonlinear Time Series Econometrics that was held in Ebeltoft, Denmark, in June The conference gathered several eminent time series econometricians to celebrate the work and outstanding career of Professor Timo Teräsvirta, one of the leading scholars in the field of nonlinear time series econometrics.


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Nonlinear Time Series by Jianqing Fan Download PDF EPUB FB2

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 by: 4. Nonlinear time series analysis uses chaos theory and nonlinear dynamics to understand such seemingly unpredictable behaviour.

Results are applied to real data from physics, biology, medicine, and engineering. While based on a sound mathematical background, the book emphasises practical usefulness.

Researchers from all experimental disciplines Cited by:   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 fullyCited by:   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. I would recommend it to postgraduate students who are interested in learning about recent developments in non-linear and non-parametric time series modelling as well as in understanding the use of complex parametric non-linear and non-parametric time series models in practice.” (Jiti Gao, Australian Journal of Agricultural and Resource.

Nonlinear Time Series Models However, there are many other types of nonlinear time series models that are not covered in this chapter, such as bilinear models, knearest neighbor methods and neural network models1. Book length treatment of nonlinear time series models can be found in Tong (), Granger and Ter¨asvirta.

Nonlinear Time Series book. Read reviews from world’s largest community for readers. This is the first book that integrates useful parametric and nonpara 4/5(5).

Nonlinear Time Series: Nonparametric and Parametric Methods Jianqing Fan and Qiwei Yao (pp)Springer-Verlag, New York (). Book Description.

Designed for researchers and students, Nonlinear Times Series: Theory, Methods and Applications with R Examples familiarizes readers with the principles behind nonlinear time series models—without overwhelming them with difficult mathematical developments.

By focusing on basic principles and theory, the authors give readers the background required to craft their own. Nonlinear Time Series: Nonparametric and Parametric Methods (Springer Series in Statistics series) by Jianqing Fan. The publisher has supplied this book in encrypted form, which means that you need to install free software in order to unlock and read it.

Required software. This book contains a up-to-date overview of nonlinear time series models and their application to modelling economic relationships.

It considers nonlinear models in stationary and nonstationary frameworks, and both parametric and nonparametric models are discussed. The book contains examples of nonlinear models in economic theory and presents the most common nonlinear 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. About this book Introduction In spite of the fact that the - plication of nonparametric techniques in time series can be traced back to the s at least, there still exists healthy and justi?ed skepticism about the capability of nonparametric methods in time series analysis.

After a brief introduction, the book examines semiparametric estimation and specification methods and then applies these approaches to a class of nonlinear continuous-time models with real-world data. It also assesses some newly proposed semiparametric estimation procedures for time series data with long-range dependence.

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 by: This book presents recent developments in nonlinear time series which have been motivated by present day problems in geosciences.

Modern methods of spatio-temporal data analysis, time-frequency analysis, dimension analysis, nonlinear correlation and synchronization analysis and other nonlinear concepts are used to study emerging questions in climatology, geophysics, solar-terrestrial physics.

This book offers a useful combination of probabilistic and statistical tools for analyzing nonlinear time series. Key features of the book include a study of the extremal behavior of nonlinear time series and a comprehensive list of nonlinear models that address different aspects of nonlinearity.

Nonlinear Time Series. • Frances and van Dijk (). Nonlinear Time Series Models in Empirical Finance. • Harvey (). Forecasting, Structural Time Series Models and the Kalman Filter. • Rosenblatt ().

Gaussian and Non-Gaussian Linear Time Series and Random Fields. • Subba-Rao and Gabr (). An Introduction to Bispectral. 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.5/5(1). Nonlinear time series Based on the book by Fan/Yao: Nonlinear Time Series Robert M.

Kunst @ University of Vienna and Institute for Advanced Studies Vienna Octo Nonlinear time series University of Vienna and Institute for Advanced Studies Vienna. Time is a series of “now moments” that you string together to call “time.”” – Wendy Kennedy She added: “What you perceive to be your future is based upon an agreed upon set of circumstances that are occurring in your now moment, that you will assign a value in the next now moment as your past.Nonlinear Time Series: Semiparametric and Nonparametric Methods - CRC Press Book 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.This book contains an extensive up-to-date overview of nonlinear time series models and their application to modelling economic relationships.

It considers nonlinear models in stationary and nonstationary frameworks, and both parametric and nonparametric models are discussed. The book contains examples of nonlinear models in economic theory and presents the most common nonlinear time series.