The accuracy of extrapolation (time series) methods: Results of a forecasting competition

The accuracy of extrapolation (time series) methods: Results of a forecasting competition In the last few decades many methods have become available for forecasting. As always, when alternatives exist, choices need to be made so that an appropriate forecasting method can be selected and used for the specific situation being considered. This paper reports the results of a forecasting competition that provides information to facilitate such choice. Seven experts in each of the 24 methods forecasted up to 1001 series for six up to eighteen time horizons. The results of the competition are presented in this paper whose purpose is to provide empirical evidence about differences found to exist among the various extrapolative (time series) methods used in the competition. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Forecasting Wiley

The accuracy of extrapolation (time series) methods: Results of a forecasting competition

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Publisher
Wiley
Copyright
Copyright © 1982 John Wiley & Sons, Ltd.
ISSN
0277-6693
eISSN
1099-131X
D.O.I.
10.1002/for.3980010202
Publisher site
See Article on Publisher Site

Abstract

In the last few decades many methods have become available for forecasting. As always, when alternatives exist, choices need to be made so that an appropriate forecasting method can be selected and used for the specific situation being considered. This paper reports the results of a forecasting competition that provides information to facilitate such choice. Seven experts in each of the 24 methods forecasted up to 1001 series for six up to eighteen time horizons. The results of the competition are presented in this paper whose purpose is to provide empirical evidence about differences found to exist among the various extrapolative (time series) methods used in the competition.

Journal

Journal of ForecastingWiley

Published: Apr 1, 1982

References

  • Forecasting with exponential smoothing: some guidelines for model selection
    Gardner, Gardner; Dannenbring, Dannenbring
  • ARARMA models for time series analysis and forecasting
    Parzen, Parzen

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