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 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 time series package ITSM, the student version of which is supplied on the enclosed CD. The data sets are contained in files with names ending in. TSM. For example, the Australian red wine sales are field as WINE.TSM. 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 vectors should first read Appendix A, where a concise account of the required background in given.
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© 2002 Springer Science+Business Media, LLC
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(2002). Introduction. In: Brockwell, P.J., Davis, R.A. (eds) Introduction to Time Series and Forecasting. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/0-387-21657-X_1
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DOI: https://doi.org/10.1007/0-387-21657-X_1
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-95351-9
Online ISBN: 978-0-387-21657-7
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