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MLP ensembles improve long term prediction accuracy over single networks

Author

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  • Adeodato, Paulo J.L.
  • Arnaud, Adrian L.
  • Vasconcelos, Germano C.
  • Cunha, Rodrigo C.L.V.
  • Monteiro, Domingos S.M.P.

Abstract

This work describes an award winning approach for solving the NN3 Forecasting Competition problem, focusing on the sound experimental validation of its main innovative feature. The NN3 forecasting task consisted of predicting 18 future values of 111 short monthly time series. The main feature of the approach was the use of the median for combining the forecasts of an ensemble of 15 MLPs to predict each time series. Experimental comparison to a single MLP shows that the ensemble increases the performance accuracy for multiple-step ahead forecasting. This system performed well on the withheld data, having finished as the second best solution of the competition with an SMAPE of 16.17%.

Suggested Citation

  • Adeodato, Paulo J.L. & Arnaud, Adrian L. & Vasconcelos, Germano C. & Cunha, Rodrigo C.L.V. & Monteiro, Domingos S.M.P., 2011. "MLP ensembles improve long term prediction accuracy over single networks," International Journal of Forecasting, Elsevier, vol. 27(3), pages 661-671, July.
    • Handle: RePEc:eee:intfor:v:27:y::i:3:p:661-671
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    References listed on IDEAS

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    1. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
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    3. Armstrong, J. Scott, 2006. "Findings from evidence-based forecasting: Methods for reducing forecast error," International Journal of Forecasting, Elsevier, vol. 22(3), pages 583-598.
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    5. Christian L Dunis & Spiros D Likothanassis & Andreas S Karathanasopoulos & Georgios S Sermpinis & Konstantinos A Theofilatos, 2013. "A hybrid genetic algorithm–support vector machine approach in the task of forecasting and trading," Journal of Asset Management, Palgrave Macmillan, vol. 14(1), pages 52-71, February.
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