Abstract
We have focused until now on the construction of time series models for stationary and nonstationary series and the determination, assuming the appropriateness of these models, of minimum mean-squared error predictors. If the observed series had in fact been generated by the fitted model, this procedure would give minimum mean-squared error forecasts. In this chapter we discuss three forecasting techniques that have less emphasis on the explicit construction of a model for the data. Each of the three selects, from a limited class of algorithms, the one that is optimal according to specified criteria.
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© 1996 Springer Science+Business Media New York
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Brockwell, P.J., Davis, R.A. (1996). Forecasting Techniques. In: Introduction to Time Series and Forecasting. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-2526-1_9
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DOI: https://doi.org/10.1007/978-1-4757-2526-1_9
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4757-2528-5
Online ISBN: 978-1-4757-2526-1
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