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Combining Neural Network Forecasts on Wavelet-Transformed Time Series

Aussem, Alex and Murtagh, Fionn (1997) Combining Neural Network Forecasts on Wavelet-Transformed Time Series. [Journal (Paginated)]

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Abstract

We discuss a simple strategy aimed at improving neural network prediction accuracy, based on the combination of predictions at varying resolution levels of the domain under investigation (here: time series). First, a wavelet transform is used to decompose the time series into varying scales of temporal resolution. The latter provide a sensible decomposition of the data so that the underlying temporal structures of the original time series become more tractable. Then, a Dynamical Recurrent Neural Network (DRNN) is trained on each resolution scale with the temporal-recurrent backpropagation (TRBP) algorithm. By virtue of its internal dynamic, this general class of dynamic connectionist network approximates the underlying law governing each resolution level by a system of nonlinear difference equations. The individual wavelet scale forecasts are afterwards recombined to form the current estimate. The predictive ability of this strategy is assessed with the sunspot series.

Item Type:Journal (Paginated)
Keywords:Neural networks, time series prediction, wavelet transform.
Subjects:Computer Science > Artificial Intelligence
Computer Science > Dynamical Systems
Computer Science > Machine Learning
Computer Science > Neural Nets
Computer Science > Speech
Computer Science > Statistical Models
ID Code:551
Deposited By:Alex, Aussem
Deposited On:08 Oct 1999
Last Modified:11 Mar 2011 08:54

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