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Forecasting the NN5 time series with hybrid models

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  • Wichard, Jörg D.
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    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.

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    File URL: http://www.sciencedirect.com/science/article/pii/S016920701000049X
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    Bibliographic Info

    Article provided by Elsevier in its journal International Journal of Forecasting.

    Volume (Year): 27 (2011)
    Issue (Month): 3 (July)
    Pages: 700-707

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    Handle: RePEc:eee:intfor:v:27:y::i:3:p:700-707

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    Web page: http://www.elsevier.com/locate/ijforecast

    Related research

    Keywords: Forecasting competitions Combining forecasts Nonlinear time series Seasonality Neural networks;

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    1. Marco Aiolfi & Carlos Capistrán & Allan Timmermann, 2010. "Forecast Combinations," Working Papers 2010-04, Banco de México.
    2. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
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    Cited by:
    1. 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.
    2. 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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