Combining Neural Network Forecasts on Wavelet-transformed Time Series
作者:
ALEX AUSSEM,
FIONN MURTAGH,
期刊:
Connection Science
(Taylor Available online 1997)
卷期:
Volume 9,
issue 1
页码: 113-122
ISSN:0954-0091
年代: 1997
DOI:10.1080/095400997116766
出版商: Taylor & Francis Group
关键词: Keywords: Dynamical Recurrent Neural Networks;Time-series Prediction;Wavelet Transform
数据来源: Taylor
摘要:
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 provides 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 netork is trained on each resolution scale with the temporal-recurrent backpropagation algorithm. By virtue of its internal dynamic, this general class of dynamic connections network approximates the underlying law governing each resolution level by a system of non-linear 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.
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