<b>Parallel Back-Propagation <BR>for the Prediction of Time Series </b>

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Parallel Back-Propagation for the Prediction of Time Series

Frank M. Thiesing, Ulrich Middelberg, Oliver Vornberger

University of Osnabrück
Dept. of Math. / Computer Science
D-49069 Osnabrück, Germany


Artificial neural networks are suitable for the prediction of chaotic time series. A modified back-propagation algorithm with neuron splitting is used to train feed-forward multilayer perceptron networks for prediction. There are two ways of parallelizing: distributing the training set for batch learning or distribute the vector-matrix-operations for on-line training. Three implementation are compaired: PVM on a workstation cluster and PARIX and the new PVM/PARIX on a Transputer system. Results about the quality of forecasting an examplary time series and speedups of the parallel programs are presented.

Frank M. Thiesing
Mon Dec 19 16:19:41 MET 1994