Stochastic Models For Time Series
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Stochastic Models For Time Series
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Author : Paul Doukhan
language : en
Publisher: Springer
Release Date : 2018-04-17
Stochastic Models For Time Series written by Paul Doukhan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-17 with Mathematics categories.
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.
Introductory Time Series With R
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Author : Paul S.P. Cowpertwait
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-05-28
Introductory Time Series With R written by Paul S.P. Cowpertwait and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-05-28 with Mathematics categories.
This book gives you a step-by-step introduction to analysing time series using the open source software R. Each time series model is motivated with practical applications, and is defined in mathematical notation. Once the model has been introduced it is used to generate synthetic data, using R code, and these generated data are then used to estimate its parameters. This sequence enhances understanding of both the time series model and the R function used to fit the model to data. Finally, the model is used to analyse observed data taken from a practical application. By using R, the whole procedure can be reproduced by the reader. All the data sets used in the book are available on the website http://staff.elena.aut.ac.nz/Paul-Cowpertwait/ts/. The book is written for undergraduate students of mathematics, economics, business and finance, geography, engineering and related disciplines, and postgraduate students who may need to analyse time series as part of their taught programme or their research.
Time Series Analysis
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Author : George E. P. Box
language : en
Publisher:
Release Date : 1976
Time Series Analysis written by George E. P. Box and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1976 with Mathematics categories.
Introduction and summary; Stochastic models and their forecasting; The autocorrelation function and spectrum; Linear stationary models; Linear nonstationary models; Forecasting; Stochastic model building; Model identification; Model estimation; Model diagnostic checking; Seasonal models; Transfer function models; Identification fitting, and checking of transfer function models.
Dynamic Stochastic Models From Empirical Data
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Author : Kashyap
language : en
Publisher: Academic Press
Release Date : 1976-09-17
Dynamic Stochastic Models From Empirical Data written by Kashyap and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1976-09-17 with Computers categories.
Dynamic Stochastic Models from Empirical Data
Stochastic Models Statistics And Their Applications
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Author : Ansgar Steland
language : en
Publisher: Springer Nature
Release Date : 2019-10-15
Stochastic Models Statistics And Their Applications written by Ansgar Steland and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-15 with Mathematics categories.
This volume presents selected and peer-reviewed contributions from the 14th Workshop on Stochastic Models, Statistics and Their Applications, held in Dresden, Germany, on March 6-8, 2019. Addressing the needs of theoretical and applied researchers alike, the contributions provide an overview of the latest advances and trends in the areas of mathematical statistics and applied probability, and their applications to high-dimensional statistics, econometrics and time series analysis, statistics for stochastic processes, statistical machine learning, big data and data science, random matrix theory, quality control, change-point analysis and detection, finance, copulas, survival analysis and reliability, sequential experiments, empirical processes, and microsimulations. As the book demonstrates, stochastic models and related statistical procedures and algorithms are essential to more comprehensively understanding and solving present-day problems arising in e.g. the natural sciences, machine learning, data science, engineering, image analysis, genetics, econometrics and finance.
An Introduction To Time Series Modeling
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Author : Andreas Jakobsson
language : en
Publisher:
Release Date : 2015-01-01
An Introduction To Time Series Modeling written by Andreas Jakobsson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-01-01 with categories.
Time series analysis concerns the mathematical modeling of time varying phenomena, e.g., ocean waves, water levels in lakes and rivers, demand for electrical power, radar signals, muscular reactions, ECG-signals, or option prices at the stock market. This book gives a comprehensive presentation of stochastic models and methods in time series analysis. The book treats stochastic vectors and both univariate and multivariate stochastic processes, as well as how these can be used to identify suitable models for various forms of observations. Furthermore, different approaches such as least squares, the prediction error method, and maximum likelihood are treated in detail, together with results on the Cramér-Rao lower bound, dictating the theoretically possible estimation accuracy. Residual analysis and prediction of stochastic models are also trated, as well as how one may form time-varying models, including the recursive least squares and the Kalman filter. The book discusses how to implement the various methods using Matlab, and several Matlab functions and data sets are provided with the book.
Periodicity And Stochastic Trends In Economic Time Series
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Author : Philip Hans Franses
language : en
Publisher: Oxford University Press, USA
Release Date : 1996
Periodicity And Stochastic Trends In Economic Time Series written by Philip Hans Franses and has been published by Oxford University Press, USA this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Business & Economics categories.
This book provides a self-contained account of periodic models for seasonally observed economic time series with stochastic trends. Two key concepts are periodic integration and periodic cointegration. Periodic integration implies that a seasonally varying differencing filter is required to remove a stochastic trend. Periodic cointegration amounts to allowing cointegration paort-term adjustment parameters to vary with the season. The emphasis is on useful econrameters and shometric models that explicitly describe seasonal variation and can reasonably be interpreted in terms of economic behaviour. The analysis considers econometric theory, Monte Carlo simulation, and forecasting, and it is illustrated with numerous empirical time series. A key feature of the proposed models is that changing seasonal fluctuations depend on the trend and business cycle fluctuations. In the case of such dependence, it is shown that seasonal adjustment leads to inappropriate results.
Time Series Analysis Forecasting Control 3 E
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Author :
language : en
Publisher: Pearson Education India
Release Date : 1994-09
Time Series Analysis Forecasting Control 3 E written by and has been published by Pearson Education India this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994-09 with categories.
This is a complete revision of a classic, seminal, and authoritative text that has been the model for most books on the topic written since 1970. It explores the building of stochastic (statistical) models for time series and their use in important areas of application -forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control.
Bilinear Stochastic Models And Related Problems Of Nonlinear Time Series Analysis
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Author : Gyorgy Terdik
language : en
Publisher:
Release Date : 1999-07-30
Bilinear Stochastic Models And Related Problems Of Nonlinear Time Series Analysis written by Gyorgy Terdik and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-07-30 with categories.
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 basic theory of nonlinear functions of stationary Gaussian processes, Hermite polynomials, cumulants and higher order spectra, multiple Wiener-ItA integrals and finally chaotic Wiener-ItA spectral representation of subordinated processes. There are two chapters for general nonlinear time series problems.
Time Series Theory And Methods
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Author : Peter J. Brockwell
language : en
Publisher: Springer Science & Business Media
Release Date : 1991
Time Series Theory And Methods written by Peter J. Brockwell and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with Business & Economics categories.
Here is a systematic account of linear time series models and their application to the modeling and prediction of data collected sequentially in time. It details techniques for handling data and offers a thorough understanding of their mathematical basis.