Tuesday, December 6, 2011

Neural networks and seasonality: Some technical considerations [An article from: European Journal of Operational Research]

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Neural networks and seasonality: Some technical considerations [An article from: European Journal of Operational Research]

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Neural networks and seasonality: Some technical considerations [An article from: European Journal of Operational Research] OverviewsThis digital document is a journal article from European Journal of Operational Research, published by Elsevier in 2007. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
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. .../ Neural networks and seasonality: Some technical considerations [An article from: European Journal of Operational Research] / currys digital




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Neural networks and seasonality: Some technical considerations [An article from: European Journal of Operational Research]
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Neural networks and seasonality: Some technical considerations [An article from: European Journal of Operational Research]





Neural networks and seasonality: Some technical considerations [An article from: European Journal of Operational Research]
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