A Course in Time Series Analysis
A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, and signal extraction. They then move on to advanced topics, focusing on heteroscedastic models, nonlinear time series models, Bayesian time series analysis, nonparametric time series analysis, and neural networks. Multivariate time series coverage includes presentations on vector ARMA models, cointegration, and multivariate linear systems. Special features include:
Requiring no previous knowledge of the subject, A Course in Time Series Analysis is an important reference and a highly useful resource for researchers and practitioners in statistics, economics, business, engineering, and environmental analysis.
An Instructor's Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley editorial department.
BASIC CONCEPTS IN UNIVARIATE TIME SERIES.
Univariate Time Series: Autocorrelation, Linear Prediction, Spectrum, State Space Model (G. Wilson).
Univariate Autoregressive Moving Average Models (G. Tiao).
Model Fitting and Checking, and the Kalman Filter (G. Wilson).
Prediction and Model Selection (D. Pe?a).
Outliers, Influential Observations and Missing Data (D. Pe?a).
Automatic Modeling Methods for Univariate Series (V. Gomez & A. Maravall).
Seasonal Adjustment and Signal Extraction in Economic Time Series (V. Gomez & A. Maravall).
ADVANCED TOPICS IN UNIVARIATE TIME SERIES.
Heteroscedatic Models (R. Tsay).
Nonlinear Time Series Models (R. Tsay).
Bayesian Time Series Analysis (R. Tsay).
Nonparametric Time Series Analysis: Nonparametric Regression, Locally Weighted Regression, Autoregression and Quantile Regression (S. Heiler).
Neural Networks (K. Hornik & F. Leisch).
MULTIVARIATE TIME SERIES.
Vector ARMA Models (G. Tiao).
Cointegration in the VAR Model (S. Johansen).
Multivariate Linear Systems (M. Deistler).
GEORGE C. TIAO, PhD, is W. Allen Wallis Professor of Statistics and Econometrics, Graduate School of Business, University of Chicago.
RUEY S. TSAY, PhD, is H. G. B. Alexander Professor of Statistics and Econometrics, Graduate School of Business, University of Chicago.
"...material is thoroughly and carefully presented...a very useful addition to any collection both for learning and reference." (Short Book Reviews, Vol. 21, No. 2, August 2001)
"From the preface: The book can be used as a principal text or a complementary text for courses in time series. " (Mathematical Reviews, Issue 2001k)
"...an excellent complement...for a first graduate course in time series analysis...a nice addition to anyone s time series library." (Technometrics, Vol. 43, No. 4, November 2001)
"If you are familiar with the basics...and need a compass to navigate the vast world of time series literature, then this book is certainly what you need to have around...presents seamlessly and coherently overviews of the current status of time series research and applications." (The American Statistician, Vol. 56, No. 1, February 2002)
"...an excellent source of introductory surveys of several timely topics in time series analysis..." (Statistical Papers, July 2002)
"...a nice compendium covering a lot of relevant material..." (Statistics & Decisions, Vol.20, No.4, 2002)