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Forecasting nonlinear time series with feed‐forward neural networks: a case study of Canadian lynx data
Author(s) -
Kajitani Yoshio,
Hipel Keith W.,
Mcleod A. Ian
Publication year - 2005
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.940
Subject(s) - computer science , artificial neural network , econometrics , feedforward neural network , series (stratigraphy) , residual , time series , data set , feed forward , nonlinear system , machine learning , artificial intelligence , mathematics , algorithm , engineering , paleontology , physics , quantum mechanics , biology , control engineering
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.