Time series prediction by a neural network model based on bi-directional computation style: A study on generalization performance with the computer-generated time series Data Set D

The principal goal of time series prediction is the enhancement of prediction accuracy. To achieve this goal, most previous investigations have adopted the so-called uni-directional computation style, focusing only on the forward-time direction (present &rarr future). This paper adopts a different approach, the bi-directional computation style, and applies it to real-time series prediction tasks. The objective of this style is to improve the accuracy of time series prediction by means of interaction between the forward-time direction processing system, which only predicts the future, and a separate backward-time processing system. Good results have previously been obtained on sunspot data, but since these involve time series that are short, there has been insufficient investigation of generalization performance. We therefore apply the proposed technique to different time series data in order to investigate further the generalization performance with untraining data. The time series data called Data Set D used here were artificially generated by a computer and are distinguished by their great length, consisting of 100,000 points. Computer simulations indicate that on average, and improvement in dealing with untraining data equal to that for training data is achievable. In addition, better prediction accuracy is achieved in areas where the conventional method is weak.