Bi-directional computing architecture for time series prediction

A number of neural network models and training procedures for time series p rediction have been proposed in the technical literature. These models stud ied for different time-variant data sets have typically used uni-directiona l computation flow or its modifications. In this study, on the contrary, th e concept of bi-directional computational style is proposed and applied to prediction tasks. A bi-directional neural network model consists of two sub networks performing two types of signal transformations bi-directionally. T he networks also receive complementary signals from each other through mutu al connections. The model not only deals with the conventional future predi ction task, but also with the past prediction, an additional task from the viewpoint of the conventional approach. An improvement of the performance i s achieved through making use of the future-past information integration. S ince the coupling effects help the proposed model improve its performance, it is found that the prediction score is better than with the traditional u ni-directional method. The bi-directional predicting architecture has been found to perform better than the conventional one when tested with standard benchmark sunspots data. (C) 2001 Elsevier Science Ltd. All rights reserve d.