Introduction to Time Series and Forecasting

1. Front Matter

   Pages i-xiv

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2. Introduction

   Pages 1-44

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3. Stationary Processes

   Pages 45-82

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4. ARMA Models

   Pages 83-110

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5. Spectral Analysis

   Pages 111-136

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6. Modeling and Forecasting with ARMA Processes

   Pages 138-177

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7. Nonstationary and Seasonal Time Series Models

   Pages 179-221

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8. Multivariate Time Series

   Pages 223-257

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9. State-Space Models

   Pages 259-316

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10. Forecasting Techniques

    Pages 317-330

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11. Further Topics

    Pages 331-367

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12. Erratum

    • Springer Science+Business Media, LLC

    Pages 435-437

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13. Back Matter

    Pages 369-434

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Some of the key mathematical results are stated without proof in order to make the underlying theory accessible 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 nonstationary 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 introductions are also given to cointegration and to nonlinear, 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.

• Analysis
• Mathematica
• Random variable
• STATISTICA
• Time series
• expectation–maximization algorithm
• innovation
• modeling
• sets

From the reviews:
"The emphasis is on hands-on experience and the friendly software that accompanies the book serves the purpose admirably. ...
The authors should be congratulated for making the subject accessible and fun to learn. The book is a pleasure to read and highly recommended. I regard it as the best introductory text in town." ISI Short Book Reviews

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

  Peter J. Brockwell, Richard A. Davis

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