WIT Press


Improved Time Series Prediction Using Evolutionary Algorithms For The Generation Of Feedback Connections In Neural Networks

Price

Free (open access)

Paper DOI

10.2495/CF040201

Volume

38

Pages

9

Published

2004

Size

333 kb

Author(s)

E. Hulthen & M. Wahde

Abstract

Some results from a method for generating recurrent neural networks (RNN) for prediction of financial and macroeconomic time series are presented. In the presented method, a feedforward neural network (FFNN) is first obtained using backpropagation. While backpropagation is usually able to find a fairly good predictor, all FFNN are limited by their lack of short-term dynamic memory. RNNs, by contrast, may exhibit short-term memory due to feedback connections in the network. In the method presented here, the RNNs are generated by an evolutionary algorithm (EA). The preliminary results indicate that the evolved RNN indeed outperforms, by a few per cent, the FFNN obtained through backpropagation on several time series. However, it is noted that, regardless of t

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