A Neural Network Scheme for Long-Term Forecasting of Chaotic Time Series
Neural Processing Letters
Network traffic prediction based on wavelet transform and season ARIMA model
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
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This paper presents a wavelet neural-network for chaotic time series prediction. Waveletnetworks are inspired by both the feed-forward neural network and the theory underlying wavelet decompositions. Wavelet-networks are a class of neural network that take advantage of good localization properties of multiresolution analysis and combine them with the approximation abilities of neural networks. This kind of networks uses wavelets as activation functions in the hidden layer and a type of backpropagation algorithm is used for its learning. Comparisons are made between a wavelet-network and the typical feedforward network trained with the backpropagation algorithm. The results reported in this paper show that wavelet-networks have better prediction properties than its similar backpropagation networks.