Methods of Forecasting Based on Artificial Neural Networks

Arthur Stepchenko, Arkady Borisov


This article presents an overview of artificial neural network (ANN) applications in forecasting and possible forecasting accuracy improvements. Artificial neural networks are computational models and universal approximators, which can be applied to the time series forecasting with a high accuracy. A great rise in research activities was observed in using artificial neural networks for forecasting. This paper examines multi-layer perceptrons (MLPs) – back-propagation neural network (BPNN), Elman recurrent neural network (ERNN), grey relational artificial neural network (GRANN) and hybrid systems – models that fuse artificial neural network with wavelets and auto- regressive integrated moving average (ARIMA).



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