Automatic neural network modeling for univariate time series

Automatic neural network modeling for univariate time series

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Article ID: iaor20013128
Country: Netherlands
Volume: 16
Issue: 4
Start Page Number: 509
End Page Number: 515
Publication Date: Oct 2000
Journal: International Journal of Forecasting
Authors: ,
Keywords: neural networks
Abstract:

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.

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