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Adaptive neural network model for time-series forecasting

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  • Wong, W.K.
  • Xia, Min
  • Chu, W.C.

Abstract

In this study, a novel adaptive neural network (ADNN) with the adaptive metrics of inputs and a new mechanism for admixture of outputs is proposed for time-series prediction. The adaptive metrics of inputs can solve the problems of amplitude changing and trend determination, and avoid the over-fitting of networks. The new mechanism for admixture of outputs can adjust forecasting results by the relative error and make them more accurate. The proposed ADNN method can predict periodical time-series with a complicated structure. The experimental results show that the proposed model outperforms the auto-regression (AR), artificial neural network (ANN), and adaptive k-nearest neighbors (AKN) models. The ADNN model is proved to benefit from the merits of the ANN and the AKN through its' novel structure with high robustness particularly for both chaotic and real time-series predictions.

Suggested Citation

  • Wong, W.K. & Xia, Min & Chu, W.C., 2010. "Adaptive neural network model for time-series forecasting," European Journal of Operational Research, Elsevier, vol. 207(2), pages 807-816, December.
    • Handle: RePEc:eee:ejores:v:207:y:2010:i:2:p:807-816
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    References listed on IDEAS

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    1. Makridakis, Spyros, 1996. "Reply to comments on "Forecasting: its role and value for planning and strategy"," International Journal of Forecasting, Elsevier, vol. 12(4), pages 555-557, December.
    2. Makridakis, Spyros, 1996. "Forecasting: its role and value for planning and strategy," International Journal of Forecasting, Elsevier, vol. 12(4), pages 513-537, December.
    3. 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.
    4. Polterovich, Victor & Popov, Vladimir, 2006. "Эволюционная Теория Экономической Политики: Часть I: Опыт Быстрого Развития [An Evolutionary Theory of Economic Policy: Part I: The Experience of Fast Development]," MPRA Paper 22168, University Library of Munich, Germany.
    5. Bodyanskiy, Yevgeniy & Popov, Sergiy, 2006. "Neural network approach to forecasting of quasiperiodic financial time series," European Journal of Operational Research, Elsevier, vol. 175(3), pages 1357-1366, December.
    6. Zhang, G. Peter & Qi, Min, 2005. "Neural network forecasting for seasonal and trend time series," European Journal of Operational Research, Elsevier, vol. 160(2), pages 501-514, January.
    7. Freitas, Paulo S.A. & Rodrigues, Antonio J.L., 2006. "Model combination in neural-based forecasting," European Journal of Operational Research, Elsevier, vol. 173(3), pages 801-814, September.
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