  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.
