Statistical methods for modelling neural networks
In this paper modelling time series by single hidden layer feedforward neural network models is considered. A coherent modelling strategy based on statistical inference is discussed. The problems of selecting the variables and the number of hidden units are solved by using statistical model selection criteria and tests. Misspecification tests for evaluating an estimated neural network model are considered. Forecasting with neural network models is discussed and an application to a real time series is presented.
|Date of creation:||Sep 2001|
|Date of revision:|
|Publication status:||Published in Intelligent Systems, v.9, p. 227-235, 2001|
|Contact details of provider:|| Postal: |
Phone: 021 35271078
Fax: 021 35271084
Web page: http://www.econ.puc-rio.br
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:rio:texdis:445. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ()
If references are entirely missing, you can add them using this form.