Chaotic Time Series Prediction using Spatio-Temporal RBF Neural Networks
This work addresses the challenge of chaotic time series prediction for signal processing applications, representing an incremental improvement over existing methods.
The paper tackled the problem of predicting chaotic time series by proposing a spatio-temporal extension of RBF neural networks, which outperformed standard RBF with significantly reduced estimation error on the Mackey-Glass dataset.
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.