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MLP ensembles improve long term prediction accuracy over single networks


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


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

    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.
    2. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    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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    Cited by:

    1. Sermpinis, Georgios & Theofilatos, Konstantinos & Karathanasopoulos, Andreas & Georgopoulos, Efstratios F. & Dunis, Christian, 2013. "Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization," European Journal of Operational Research, Elsevier, vol. 225(3), pages 528-540.
    2. Ait Maatallah, Othman & Achuthan, Ajit & Janoyan, Kerop & Marzocca, Pier, 2015. "Recursive wind speed forecasting based on Hammerstein Auto-Regressive model," Applied Energy, Elsevier, vol. 145(C), pages 191-197.
    3. Elsy Gómez-Ramos & Francisco Venegas-Martínez, 2013. "A Review of Artificial Neural Networks: How Well Do They Perform in Forecasting Time Series?," Analítika, Analítika - Revista de Análisis Estadístico/Journal of Statistical Analysis, vol. 6(2), pages 7-15, Diciembre.


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