Forecasting with artificial neural network models

Gianluigi Rech
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Gianluigi Rech: QA Analysis, ELECTRABEL, Place de l'Universite', 16, LLN, B-1348 Belgium, Postal: Stockholm School of Economics, P.O. Box 6501, SE-113 83 Stockholm, Sweden

No 491, SSE/EFI Working Paper Series in Economics and Finance from Stockholm School of Economics

Abstract: This paper contains a forecasting exercise on 30 time series, ranging on several fields, from economy to ecology. The statistical approach to artificial neural networks modelling developed by the author is compared to linear modelling and to other three well-known neural network modelling procedures: Information Criterion Pruning (ICP), Cross-Validation Pruning (CVP) and Bayesian Regularization Pruning (BRP). The findings are that 1) the linear models outperform the artificial neural network models and 2) albeit selecting and estimating much more parsimonious models, the statistical approach stands up well in comparison to other more sophisticated ANN models.

Keywords: Neural networks; forecasting; nonlinear time series (search for similar items in EconPapers)
JEL-codes: C22 C53 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2002-02-11
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-ets
References: View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:hhs:hastef:0491

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