Synopses & Reviews
This paperback edition is a reprint of the 1991 edition. Time Series: Theory and Methods is a systematic account of linear time series models and their application to the modeling and prediction of data collected sequentially in time. The aim is to provide specific techniques for handling data and at the same time to provide a thorough understanding of the mathematical basis for the techniques. Both time and frequency domain methods are discussed, but the book is written in such a way that either approach could be emphasized. The book is intended to be a text for graduate students in statistics, mathematics, engineering, and the natural or social sciences. It contains substantial chapters on multivariate series and state-space models (including applications of the Kalman recursions to missing-value problems) and shorter accounts of special topics including long-range dependence, infinite variance processes, and nonlinear models. Most of the programs used in the book are available in the modeling package ITSM2000, the student version of which can be downloaded from http://www.stat.colostate.edu/~pjbrock/student06.
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.
Table of Contents
Stationary Time Series.- Hilbert Spaces.- Stationary ARMA Processes.- The Spectral Representation of a Stationary Process.- Prediction of Stationary Processes.- Asymptotic Theory.- Estimation of the Mean and the Autocovariance Function.- Estimation for ARMA Models.- Model Building and Forecasting with ARIMA Processes.- Inference for the Spectrum of a Stationary Process.- Multivariate Time Series.- State-Space Models and the Kalman Recursions.- Further Topics.- Appendix: Data Sets.- Bibliography.- Index.