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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Brockwell, P.J., Davis, R.A. (2016). Forecasting Techniques. In: Introduction to Time Series and Forecasting. Springer Texts in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-29854-2_10
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DOI: https://doi.org/10.1007/978-3-319-29854-2_10
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