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**Stacked heterogeneous neural networks for time series forecasting.**
*(English)*
Zbl 1189.62154

Summary: 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.

### MSC:

62M20 | Inference from stochastic processes and prediction |

62M45 | Neural nets and related approaches to inference from stochastic processes |

62M10 | Time series, auto-correlation, regression, etc. in statistics (GARCH) |

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\textit{F. Leon} and \textit{M. H. Zaharia}, Math. Probl. Eng. 2010, Article ID 373648, 20 p. (2010; Zbl 1189.62154)

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