Seasonal adjustment with 'bad' neural networks: a case of benign underfitting
by Bruce Curry, Peter H. Morgan
International Journal of Applied Management Science (IJAMS), Vol. 2, No. 4, 2010

Abstract: This paper is a response to reports by a number of authors of difficulties in using feedforward neural networks (NNs) in the presence of seasonality. Such difficulties appear to contradict the well-known property of 'universal approximation', but arise in fact because the property only applies to suitably smooth functions. We report on what is an unexpected benefit of this limitation of NNs: although the network is unable to follow seasonal movements it can identify an underlying trend, through being forced to steer a middle course. We achieve such deseasonalisation through network pruning, which restricts the approximation capacity of the network by depriving it of connections. Although pruning is in general motivated by the search for the most parsimonious network, in our particular context it forces the network to ignore both seasonal movements and noise.

Online publication date: Thu, 04-Nov-2010

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Applied Management Science (IJAMS):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com