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Neural Network Models for Time Series Forecasts

Author

Listed:
  • Tim Hill

    (University of Hawaii, 2404 Maile Way, Honolulu, Hawaii 96822)

  • Marcus O'Connor

    (University of New South Wales, Kensington, New South Wales, Australia)

  • William Remus

    (University of Hawaii, 2404 Maile Way, Honolulu, Hawaii 96822)

Abstract

Neural networks have been advocated as an alternative to traditional statistical forecasting methods. In the present experiment, time series forecasts produced by neural networks are compared with forecasts from six statistical time series methods generated in a major forecasting competition (Makridakis et al. [Makridakis, S., A. Anderson, R. Carbone, R. Fildes, M. Hibon, R. Lewandowski, J. Newton, E. Parzen, R. Winkler. 1982. The accuracy of extrapolation (time series) methods: Results of a forecasting competition. J. Forecasting 1 111--153.]); the traditional method forecasts were estimated by experts in the particular technique. The neural networks were estimated using the same ground rules as the competition. Across monthly and quarterly time series, the neural networks did significantly better than traditional methods. As suggested by theory, the neural networks were particularly effective for discontinuous time series.

Suggested Citation

  • Tim Hill & Marcus O'Connor & William Remus, 1996. "Neural Network Models for Time Series Forecasts," Management Science, INFORMS, vol. 42(7), pages 1082-1092, July.
  • Handle: RePEc:inm:ormnsc:v:42:y:1996:i:7:p:1082-1092
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    File URL: http://dx.doi.org/10.1287/mnsc.42.7.1082
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    Keywords

    neural networks; time series; back propagation;

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