Forecasting nonlinear time series with feed-forward neural networks: a case study of Canadian lynx data

Yoshio Kajitani, A. Ian Mcleod and Keith W. Hipel
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Yoshio Kajitani: Central Research Institute of Electric Power Industry, Japan, Postal: Central Research Institute of Electric Power Industry, Japan
A. Ian Mcleod: University of Western Ontario, Canada, Postal: University of Western Ontario, Canada
Keith W. Hipel: University of Waterloo, Canada, Postal: University of Waterloo, Canada

Journal of Forecasting, 2005, vol. 24, issue 2, 105-117

Abstract: The forecasting capabilities of feed-forward neural network (FFNN) models are compared to those of other competing time series models by carrying out forecasting experiments. As demonstrated by the detailed forecasting results for the Canadian lynx data set, FFNN models perform very well, especially when the series contains nonlinear and non-Gaussian characteristics. To compare the forecasting accuracy of a FFNN model with an alternative model, Pitman's test is employed to ascertain if one model forecasts significantly better than another when generating one-step-ahead forecasts. Moreover, the residual-fit spread plot is utilized in a novel fashion in this paper to compare visually out-of-sample forecasts of two alternative forecasting models. Finally, forecasting findings on the lynx data are used to explain under what conditions one would expect FFNN models to furnish reliable and accurate forecasts. Copyright © 2005 John Wiley & Sons, Ltd.

Date: 2005
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Persistent link: https://EconPapers.repec.org/RePEc:jof:jforec:v:24:y:2005:i:2:p:105-117

DOI: 10.1002/for.940

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