Abstract
In this chapter we shall examine the problem of selecting an appropriate model for a given set of observations {X t , t = 1,..., n}. If the data (a) exhibits no apparent deviations from stationarity and (b) has a rapidly decreasing autocorrelation function, we shall seek a suitable ARMA process to represent the mean-corrected data. If not, then we shall first look for a transformation of the data which generates a new series with the properties (a) and (b). This can frequently be achieved by differencing, leading us to consider the class of ARIMA (autoregressive-integrated moving average) processes which is introduced in Section 9.1. Once the data has been suitably transformed, the problem becomes one of finding a satisfactory ARMA(p, q) model, and in particular of choosing (or identifying) p and q.
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© 1987 Springer Science+Business Media New York
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Brockwell, P.J., Davis, R.A. (1987). Model Building and Forecasting with ARIMA Processes. In: Time Series: Theory and Methods. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-0004-3_9
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DOI: https://doi.org/10.1007/978-1-4899-0004-3_9
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4899-0006-7
Online ISBN: 978-1-4899-0004-3
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