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Time series prediction by a neural network model based on the bi-directional computation style

A number of neural network models and training procedures for time series prediction have been proposed in the technical literature. These models typically used uni-directional computation flow or its modifications. In this study a novel concept of bi-directional computation style is proposed and applied to prediction tasks. Since the coupling effects between the future prediction system and the past prediction system help the proposed model improve its performance, it is found that the prediction score is better than with the traditional uni-directional method. The bi-directional predicting architecture has been found to perform better than the conventional one when tested with standard benchmark sunspots data.