title: Bi-directional computing architecture for time series prediction.
abstract: A number of neural network models and training procedures for time series prediction have been proposed in the technical literature. These models studied for different time-variant data sets have typically used uni-directional computation flow or its modifications. In this study, on the contrary, the concept of bi-directional computational style is proposed and applied to prediction tasks. A bi-directional neural network model consists of two subnetworks performing two types of signal transformations bi-directionally. The networks also receive complementary signals from each other through mutual connections. The model not only deals with the conventional future prediction task, but also with the past prediction, an additional task from the viewpoint of the conventional approach. An improvement of the performance is achieved through making use of the future-past information integration. Since the coupling effects 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.
authors.affiliation: Department of Electrical and Computer Engineering, University of Louisville, KY 40292, USA. wakuya@aivo.spd.louisville.edu
authors.forename: H
authors.initials: H
authors.lastname: Wakuya
authors.forename: J M
authors.initials: JM
authors.lastname: Zurada
mesh_terms: D003627:Data Interpretation, Statistical; D005544:Forecasting / Q000379:methods*; D008432:Mathematical Computing; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012986:Software Validation; D013997:Time Factors