Automatic neural network modeling for univariate time series

Artificial neural networks (ANNs) are an information processing paradigm inspired by the way the brain processes information. Using neural networks requires the investigator to make decisions concerning the architecture or structure used. ANNs are known to be universal function approximators and are...

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Bibliographic Details
Published inInternational journal of forecasting Vol. 16; no. 4; pp. 509 - 515
Main Authors Balkin, Sandy D., Ord, J.Keith
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.10.2000
Elsevier
Elsevier Sequoia S.A
SeriesInternational Journal of Forecasting
Subjects
Accuracy
Architecture
Artificial intelligence
Artificial neural networks
Automated ANNs
Computer architecture
Forecasting
Mathematical models
Neural networks
Statistical methods
Studies
Time series
Architecture
Automated ANNs
Artificial neural networks
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Summary:Artificial neural networks (ANNs) are an information processing paradigm inspired by the way the brain processes information. Using neural networks requires the investigator to make decisions concerning the architecture or structure used. ANNs are known to be universal function approximators and are capable of exploiting nonlinear relationships between variables. This method, called Automated ANNs, is an attempt to develop an automatic procedure for selecting the architecture of an artificial neural network for forecasting purposes. It was entered into the M-3 Time Series Competition. Results show that ANNs compete well with the other methods investigated, but may produce poor results if used under certain conditions.
ISSN:0169-2070
1872-8200
DOI:10.1016/S0169-2070(00)00072-8