Neural network moving averages for time series prediction

Bruce E. Rosen

doi:10.1117/12.152644

19 August 1993 Neural network moving averages for time series prediction

Bruce E. Rosen

Proceedings Volume 1966, Science of Artificial Neural Networks II; (1993) https://doi.org/10.1117/12.152644
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

Abstract

ARMA (autoregressive--moving average) time series methods have been found to be effective methods of forecasting and prediction. Using AR (autoregression) methods, predictions rely on regressing previous time series input values, while in MA (moving average) methods, predictions are calculated by regressing previous forecasting errors. We can improve ARMA type forecasts with backpropagation by nonlinear regression of both the inputs and the previous forecasting errors. The new predictions are calculated by adding a feedforward neural network that accepts the previous forecast and previously generated forecast errors as inputs and produces new forecasts having smaller prediction errors. The accuracy of these forecast can exceed that of ARMA, or backpropagation forecasts alone. The improved predictions of AR and backpropagation network forecasts are shown using the Mackey-Glass chaotic time series.

Citation Download Citation

Bruce E. Rosen "Neural network moving averages for time series prediction", Proc. SPIE 1966, Science of Artificial Neural Networks II, (19 August 1993); https://doi.org/10.1117/12.152644

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