Applications of artificial neural networks to the nonlinear combination of forecasts

Shanming Shi · Li Da Xu · Bao Liu

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Forecasting Techniques and Applications Neural Networks and Applications Stock Market Forecasting Methods

Abstract

Abstract: This paper proposes artificial neural networks (ANN) as a tool for nonlinear combination of forecasts. In this study, three forecasting models are used for individual forecasts, and then two linear combining methods are used to compare with the ANN combining method. The comparative experiment using real‐world data shows that the prediction by the ANN method outperforms those by linear combining methods. The paper suggests that the ANN method can be used as an alternative to conventional linear combining methods to achieve greater forecasting accuracy.

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References

1969The Combination of Forecasts 1984Improved methods of combining forecasts 1983Averages of Forecasts: Some Empirical Results 1992Forecasting the behavior of multivariate time series using neural networks 1991Time series forecasting using neural networks vs. Box- Jenkins methodology 1993Nonlinear modeling and forecasting 1995Beyond the fads: Systems thinking for managers 1995Forecaster Diversity and the Benefits of Combining Forecasts

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