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'Simple' neural networks for forecasting

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  • Curry, Bruce

Abstract

In a recent article in this journal Hwarng and Ang (HA) introduce what they describe as a 'simple' neural network for time series forecasting. It is argued here that the approach is better described as logistic regression applied in a time-series context. However, the HA model cannot be implemented through the standard LOGIT technique for handling qualitative dependent variables. Nor is it same as the logistic difference equation used in population biology. In fact, it seems to have no 'pedigree' in the time-series literature. The paper explores the dynamic properties of the model. Chaotic behaviour will not arise, and stability, especially in the first-order case, is quite likely.

Suggested Citation

  • Curry, Bruce, 2004. "'Simple' neural networks for forecasting," Omega, Elsevier, vol. 32(2), pages 97-100, April.
    • Handle: RePEc:eee:jomega:v:32:y:2004:i:2:p:97-100
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    References listed on IDEAS

    as
    1. Curry, B. & Morgan, P., 1997. "Neural networks: a need for caution," Omega, Elsevier, vol. 25(1), pages 123-133, February.
    2. Hwarng, H. Brian & Ang, H. T., 2001. "A simple neural network for ARMA(p,q) time series," Omega, Elsevier, vol. 29(4), pages 319-333, August.
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