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Forecasting the NN5 time series with hybrid models

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  • Wichard, Jörg D.

Abstract

We propose a simple way of predicting time series with recurring seasonal periods. Missing values of the time series are estimated and interpolated in a preprocessing step. We combine several forecasting methods by taking the weighted mean of forecasts that were generated with time-domain models which were validated on left-out parts of the time series. The hybrid model is a combination of a neural network ensemble, an ensemble of nearest trajectory models and a model for the 7-day cycle. We apply this approach to the NN5 time series competition data set.

Suggested Citation

  • Wichard, Jörg D., 2011. "Forecasting the NN5 time series with hybrid models," International Journal of Forecasting, Elsevier, vol. 27(3), pages 700-707, July.
    • Handle: RePEc:eee:intfor:v:27:y::i:3:p:700-707
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    References listed on IDEAS

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    1. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    2. Timmermann, Allan, 2006. "Forecast Combinations," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 4, pages 135-196, Elsevier.
    3. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
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    Cited by:

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    2. Venkatesh, Kamini & Ravi, Vadlamani & Prinzie, Anita & Poel, Dirk Van den, 2014. "Cash demand forecasting in ATMs by clustering and neural networks," European Journal of Operational Research, Elsevier, vol. 232(2), pages 383-392.
    3. Kaihua Deng, 2015. "Predicting By Learning: An Adaptive Rationale," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 10(02), pages 1-14, December.
    4. Kück, Mirko & Freitag, Michael, 2021. "Forecasting of customer demands for production planning by local k-nearest neighbor models," International Journal of Production Economics, Elsevier, vol. 231(C).
    5. V. Kamini & V. Ravi & A. Prinzie & D. Van Den Poel, 2013. "Cash Demand Forecasting in ATMs by Clustering and Neural Networks," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/865, Ghent University, Faculty of Economics and Business Administration.

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