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Introduction

  • Peter J. Brockwell
  • Richard A. Davis
Part of the Springer Texts in Statistics book series (STS)

Abstract

In this chapter we introduce some basic ideas of time series analysis and stochastic processes. Of particular importance are the concepts Of stationarity and the autocovariance and sample autocovariance functions. Some standard techniques are described for the estimation and removal of trend and seasonality (of known period) from an observed time series. These are illustrated with reference to the data sets in Section 1.1. The calculations in all the examples can be carried out using the programs supplied on the enclosed diskette. The data sets are contained in files with names ending in.DAT. For example, the Australian red wine sales are filed as WINE.DAT. Most of the topics covered in this chapter will be developed more fully in later sections of the book. The reader who is not already familiar with random variables and random vectors should first read Appendix A where a concise account of the required background is given.

Keywords

Autocorrelation Function Time Series Model Quadratic Trend Stationary Time Series Seasonal Component 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1996

Authors and Affiliations

  • Peter J. Brockwell
    • 1
  • Richard A. Davis
    • 2
  1. 1.Royal Melbourne Institute of TechnologyMelbourneAustralia
  2. 2.Department of StatisticsColorado State UniversityFort CollinsUSA

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