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Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition

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  • Andrawis, Robert R.
  • Atiya, Amir F.
  • El-Shishiny, Hisham
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    Abstract

    In this work we introduce the forecasting model with which we participated in the NN5 forecasting competition (the forecasting of 111 time series representing daily cash withdrawal amounts at ATM machines). The main idea of this model is to utilize the concept of forecast combination, which has proven to be an effective methodology in the forecasting literature. In the proposed system we attempted to follow a principled approach, and make use of some of the guidelines and concepts that are known in the forecasting literature to lead to superior performance. For example, we considered various previous comparison studies and time series competitions as guidance in determining which individual forecasting models to test (for possible inclusion in the forecast combination system). The final model ended up consisting of neural networks, Gaussian process regression, and linear models, combined by simple average. We also paid extra attention to the seasonality aspect, decomposing the seasonality into weekly (which is the strongest one), day of the month, and month of the year seasonality.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0169207010001445
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    Bibliographic Info

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

    Volume (Year): 27 (2011)
    Issue (Month): 3 ()
    Pages: 672-688

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    Handle: RePEc:eee:intfor:v:27:y:2011:i:3:p:672-688

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

    Related research

    Keywords: NN5 competition; Time series forecasting; Neural network forecasting; Gaussian process forecasting; Forecast combination; Seasonality; Computational intelligence models;

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    Cited by:
    1. Bontempi, Gianluca & Ben Taieb, Souhaib, 2011. "Conditionally dependent strategies for multiple-step-ahead prediction in local learning," International Journal of Forecasting, Elsevier, vol. 27(3), pages 689-699.
    2. Xiong, Tao & Bao, Yukun & Hu, Zhongyi, 2013. "Beyond one-step-ahead forecasting: Evaluation of alternative multi-step-ahead forecasting models for crude oil prices," Energy Economics, Elsevier, vol. 40(C), pages 405-415.
    3. 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.

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