Chaotic Time Series Prediction using Spatio-Temporal RBF Neural Networks

TL;DR

This paper introduces a spatio-temporal RBF neural network model that improves chaotic time series prediction by leveraging both temporal and spatial information, outperforming standard RBF models.

Contribution

It presents a novel spatio-temporal extension of RBF neural networks that separately models temporal dynamics and spatial non-linearity for better chaotic series prediction.

Findings

Spatio-temporal RBF outperforms standard RBF in prediction accuracy.

Significantly reduced estimation error with the proposed model.

Effective handling of chaotic time series like Mackey-Glass.

Abstract

Due to the dynamic nature, chaotic time series are difficult predict. In conventional signal processing approaches signals are treated either in time or in space domain only. Spatio-temporal analysis of signal provides more advantages over conventional uni-dimensional approaches by harnessing the information from both the temporal and spatial domains. Herein, we propose an spatio-temporal extension of RBF neural networks for the prediction of chaotic time series. The proposed algorithm utilizes the concept of time-space orthogonality and separately deals with the temporal dynamics and spatial non-linearity(complexity) of the chaotic series. The proposed RBF architecture is explored for the prediction of Mackey-Glass time series and results are compared with the standard RBF. The spatio-temporal RBF is shown to out perform the standard RBFNN by achieving significantly reduced estimation error.