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Stacked Heterogeneous Neural Networks for Time Series Forecasting

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  • Florin Leon
  • Mihai Horia Zaharia

Abstract

A hybrid model for time series forecasting is proposed. It is a stacked neural network, containing one normal multilayer perceptron with bipolar sigmoid activation functions, and the other with an exponential activation function in the output layer. As shown by the case studies, the proposed stacked hybrid neural model performs well on a variety of benchmark time series. The combination of weights of the two stack components that leads to optimal performance is also studied.

Suggested Citation

  • Florin Leon & Mihai Horia Zaharia, 2010. "Stacked Heterogeneous Neural Networks for Time Series Forecasting," Mathematical Problems in Engineering, Hindawi, vol. 2010, pages 1-20, May.
    • Handle: RePEc:hin:jnlmpe:373648
    DOI: 10.1155/2010/373648
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    Cited by:

    1. Tharindu P. De Alwis & S. Yaser Samadi, 2024. "Stacking-based neural network for nonlinear time series analysis," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(3), pages 901-924, July.

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