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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References
Harvey, A. C. (1990). Forecasting, structural time series models and the Kalman filter. Cambridge: Cambridge University Press.
Holt, C. C. (1957). Forecasting seasonals and trends by exponentially weighted moving averages. ONR research memorandum (Vol. 52). Pittsburgh, PA: Carnegie Institute of Technology.
Makridakis, S., Andersen, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R., Newton, J., Parzen, E., & Winkler, R. (1984). The forecasting accuracy of major time series methods. New York: Wiley.
Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (1997). Forecasting: Methods and applications. New York: Wiley.
Newton, H. J., & Parzen, E. (1984). Forecasting and time series model types of 111 economic time series. In S. Makridakis, et al. (Eds.), The forecasting accuracy of major time series methods. New York: Wiley.
Parzen, E. (1982), ARARMA models for time series analysis and forecasting. Journal of Forecasting, 1, 67–82.
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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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