Stochastic Models for Time Series

Stochastic Models for Time Series
Author :
Publisher : Springer
Total Pages : 308
Release :
ISBN-10 : 3319769375
ISBN-13 : 9783319769370
Rating : 4/5 (370 Downloads)

Book Synopsis Stochastic Models for Time Series by : Paul Doukhan

Download or read book Stochastic Models for Time Series written by Paul Doukhan and published by Springer. This book was released on 2018-05-25 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents essential tools for modelling non-linear time series. The first part of the book describes the main standard tools of probability and statistics that directly apply to the time series context to obtain a wide range of modelling possibilities. Functional estimation and bootstrap are discussed, and stationarity is reviewed. The second part describes a number of tools from Gaussian chaos and proposes a tour of linear time series models. It goes on to address nonlinearity from polynomial or chaotic models for which explicit expansions are available, then turns to Markov and non-Markov linear models and discusses Bernoulli shifts time series models. Finally, the volume focuses on the limit theory, starting with the ergodic theorem, which is seen as the first step for statistics of time series. It defines the distributional range to obtain generic tools for limit theory under long or short-range dependences (LRD/SRD) and explains examples of LRD behaviours. More general techniques (central limit theorems) are described under SRD; mixing and weak dependence are also reviewed. In closing, it describes moment techniques together with their relations to cumulant sums as well as an application to kernel type estimation.The appendix reviews basic probability theory facts and discusses useful laws stemming from the Gaussian laws as well as the basic principles of probability, and is completed by R-scripts used for the figures. Richly illustrated with examples and simulations, the book is recommended for advanced master courses for mathematicians just entering the field of time series, and statisticians who want more mathematical insights into the background of non-linear time series.


Stochastic Models for Time Series Related Books

Stochastic Models for Time Series
Language: en
Pages: 308
Authors: Paul Doukhan
Categories: Mathematics
Type: BOOK - Published: 2018-05-25 - Publisher: Springer

DOWNLOAD EBOOK

This book presents essential tools for modelling non-linear time series. The first part of the book describes the main standard tools of probability and statist
Stochastic Modeling
Language: en
Pages: 372
Authors: Hossein Bonakdari
Categories: Science
Type: BOOK - Published: 2022-04-13 - Publisher: Elsevier

DOWNLOAD EBOOK

Stochastic Modeling: A Thorough Guide to Evaluate, Pre-Process, Model and Compare Time Series with MATLAB Software allows for new avenues in time series analysi
Bilinear Stochastic Models and Related Problems of Nonlinear Time Series Analysis
Language: en
Pages: 275
Authors: György Terdik
Categories: Mathematics
Type: BOOK - Published: 2012-12-06 - Publisher: Springer Science & Business Media

DOWNLOAD EBOOK

The object of the present work is a systematic statistical analysis of bilinear processes in the frequency domain. The first two chapters are devoted to the bas
Stochastic Models, Statistics and Their Applications
Language: en
Pages: 479
Authors: Ansgar Steland
Categories: Mathematics
Type: BOOK - Published: 2015-02-04 - Publisher: Springer

DOWNLOAD EBOOK

This volume presents the latest advances and trends in stochastic models and related statistical procedures. Selected peer-reviewed contributions focus on stati
Stochastic Modeling
Language: en
Pages: 305
Authors: Nicolas Lanchier
Categories: Mathematics
Type: BOOK - Published: 2017-01-27 - Publisher: Springer

DOWNLOAD EBOOK

Three coherent parts form the material covered in this text, portions of which have not been widely covered in traditional textbooks. In this coverage the reade