Wavelet Packet Multi-layer Perceptron for Chaotic Time Series Prediction: Effects of Weight Initialization

Authors:
Kok Keong Teo;Lipo Wang;Zhiping Lin
Affiliations:
-;-;-
Venue:
ICCS '01 Proceedings of the International Conference on Computational Science-Part II
Year:
2001

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Abstract

We train the wavelet packet multi-layer perceptron neural network (WP-MLP) by backpropagation for time series prediction. Weights in the backpropagation algorithm are usually initialized with small random values. If the random initial weights happen to be far from a good solution or they are near a poor local optimum, training may take a long time or get trap in the local optimum. Proper weights initialization will place the weights close to a good solution with reduced training time and increased the possibility of reaching a good solution. In this paper, we investigate the effect of weight initialization on WP-MLP using two clustering algorithms. We test the initialization methods on WP-MLP with the sunspots and Mackey-Glass benchmark time series. We show that with proper weight initialization, better prediction performance can be attained.