Introduction to Time Series Modeling - Chapman & Hall/CRC Monographs on Statistics and Applied Probability (Hardback)Genshiro Kitagawa (author)
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In time series modeling, the behavior of a certain phenomenon is expressed in relation to the past values of itself and other covariates. Since many important phenomena in statistical analysis are actually time series and the identification of conditional distribution of the phenomenon is an essential part of the statistical modeling, it is very important and useful to learn fundamental methods of time series modeling. Illustrating how to build models for time series using basic methods, Introduction to Time Series Modeling covers numerous time series models and the various tools for handling them.
The book employs the state-space model as a generic tool for time series modeling and presents convenient recursive filtering and smoothing methods, including the Kalman filter, the non-Gaussian filter, and the sequential Monte Carlo filter, for the state-space models. Taking a unified approach to model evaluation based on the entropy maximization principle advocated by Dr. Akaike, the author derives various methods of parameter estimation, such as the least squares method, the maximum likelihood method, recursive estimation for state-space models, and model selection by the Akaike information criterion (AIC). Along with simulation methods, he also covers standard stationary time series models, such as AR and ARMA models, as well as nonstationary time series models, including the locally stationary AR model, the trend model, the seasonal adjustment model, and the time-varying coefficient AR model.
With a focus on the description, modeling, prediction, and signal extraction of times series, this book provides basic tools for analyzing time series that arise in real-world problems. It encourages readers to build models for their own real-life problems.
Publisher: Taylor & Francis Inc
Number of pages: 314
Weight: 594 g
Dimensions: 234 x 156 x 23 mm
My first reaction before opening this book was if there is a market for yet another book on the subject. However, my skepticism disappeared fast once I started reading the book. ... The content of the book is well chosen ... I would strongly recommend this book for readers who want to have a first glance to the notion of time series analysis and modelling. It is also a valuable book for teaching a first course on time series modelling both for graduate and/or undergraduate students.
-Journal of Time Series Analysis, Volume 32, May 2011
This book provides an introduction to time series analysis with emphasis on the state space approach. It reflects the extensive experience and significant contributions of the author to non-linear and non-Gaussian modeling. ... The material from Chapter 8 on is worth reading by anybody wishing to extend their range of time series tools. ... This is a valuable book, especially with its broad and accessible introduction of models in the state space framework.
-Statistics in Medicine, 2011, 30
... What distinguishes this book from comparable introductory texts is the use of state space modeling. Along with this come a number of valuable tools for recursive filtering and smoothing including the Kalman filter, as well as non-Gaussian and sequential Monte Carlo filters. ... a useful reference for the application of state space modeling to time series.
-MAA Reviews, October 2010
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