Introduction to Time Series and Forecasting

1. Front Matter

   Pages i-xiii

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2. Introduction

   • Peter J. Brockwell, Richard A. Davis

   Pages 1-42

3. Stationary Processes

   • Peter J. Brockwell, Richard A. Davis

   Pages 43-80

4. ARMA Models

   • Peter J. Brockwell, Richard A. Davis

   Pages 81-108

5. Spectral Analysis

   • Peter J. Brockwell, Richard A. Davis

   Pages 109-134

6. Modelling and Forecasting with ARMA Processes

   • Peter J. Brockwell, Richard A. Davis

   Pages 135-175

7. Nonstationary and Seasonal Time Series Models

   • Peter J. Brockwell, Richard A. Davis

   Pages 177-215

8. Multivariate Time Series

   • Peter J. Brockwell, Richard A. Davis

   Pages 217-250

9. State-Space Models

   • Peter J. Brockwell, Richard A. Davis

   Pages 251-308

10. Forecasting Techniques

    • Peter J. Brockwell, Richard A. Davis

    Pages 309-322

11. Further Topics

    • Peter J. Brockwell, Richard A. Davis

    Pages 323-353

12. Back Matter

    Pages 355-422

    PDF

Some of the key mathematical results are stated without proof in order to make the underlying theory acccessible to a wider audience. The book assumes a knowledge only of basic calculus, matrix algebra, and elementary statistics. The emphasis is on methods and the analysis of data sets. The logic and tools of model-building for stationary and non-stationary time series are developed in detail and numerous exercises, many of which make use of the included computer package, provide the reader with ample opportunity to develop skills in this area. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectral analysis. Additional topics include harmonic regression, the Burg and Hannan-Rissanen algorithms, unit roots, regression with ARMA errors, structural models, the EM algorithm, generalized state-space models with applications to time series of count data, exponential smoothing, the Holt-Winters and ARAR forecasting algorithms, transfer function models and intervention analysis. Brief introducitons are also given to cointegration and to non-linear, continuous-time and long-memory models. The time series package included in the back of the book is a slightly modified version of the package ITSM, published separately as ITSM for Windows, by Springer-Verlag, 1994. It does not handle such large data sets as ITSM for Windows, but like the latter, runs on IBM-PC compatible computers under either DOS or Windows (version 3.1 or later). The programs are all menu-driven so that the reader can immediately apply the techniques in the book to time series data, with a minimal investment of time in the computational and algorithmic aspects of the analysis.

• Estimator
• ITSM
• Likelihood
• Random variable
• Trend
• correlation
• expectation–maximization algorithm
• innovation
• normal distribution
• probability
• probability distribution
• statistics
• time series
• time series analysis
• unit roots

From the reviews of the second edition:

"I like this book very much: as one who teaches an undergraduate course of time series, it covers everything that I require and more. I would have no hesitation in recommending it to my students." The Statistician

"This is the second edition of a popular time series course text … . The accompanying computer package is the book’s most appealing feature. It allows one to integrate theoretical discourse and methodologic practice with considerable ease. … In my opinion, this book is the best textbook choice for a course at the advanced undergraduate and master’s level in modern time-series analysis in time and frequency domains. Those who are teaching from other texts are unnecessarily complicating their lives." (Robert Lund, Journal of the American Statistical Association, Vol. 98 (463), 2003)

"This is … update to an introductory time series book that first appeared in 1996. … The book is not expensive. … If you need a good basic time series reference, this book would certainly be a good choice." (Technometrics, Vol. 45 (1), 2003)

"The book gives an introduction into time series analysis. … The book is highly recommendable. It provides an excellent introduction into time series analysis. … it can be used as a textbook for students of various disciplines. Moreover, it is suitable as a reference book for practitioners. The great number of examples coming from economics, engineering, natural and social sciences contribute to a better understanding of the methods. For handling the software, very little familiarity with computing is required." (Wolfgang Schmid, Zentralblatt MATH, Vol. 994, 2002)

• Royal Melbourne Institute of Technology, Melbourne, Australia

  Peter J. Brockwell

• Department of Statistics, Colorado State University, Fort Collins, USA

  Richard A. Davis

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