Neural networks and seasonality: Some technical considerations

Neural networks and seasonality: Some technical considerations

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Article ID: iaor2009633
Country: Netherlands
Volume: 179
Issue: 1
Start Page Number: 267
End Page Number: 274
Publication Date: May 2007
Journal: European Journal of Operational Research
Authors:
Abstract:

Debate continues regarding the capacity of feedforward neural networks (NNs) to deal with seasonality without pre-processing. The purpose of this paper is to provide, with examples, some theoretical perspective for the debate. In the first instance it considers possible specification errors arising through use of autoregressive forms. Secondly, it examines seasonal variation in the context of the so-called ‘universal approximation’ capabilities of NNs, finding that a short (bounded) sinusoidal series is easy for the network but that a series with many turning points becomes progressively more difficult. This follows from results contained in one of the seminal papers on NN approximation. It is confirmed in examples which also show that, to model seasonality with NNs, very large numbers of hidden nodes may be required.

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