Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/103366
Type: Artigo de periódico
Title: Technique Of Identification Of Linear And Non-linear Time Series Models [técnica De Identificação De Modelos Lineares E Não-lineares De Séries Temporais]
Author: Luna I.
Ballini R.
Soares S.
Abstract: In this work, an algorithm for identifying time series models is proposed. The strategy is based on Partial Mutual Information Criterion (PMI), which considers not only linear but also non-linear relations between variables under study. For calculating the PMI criterion, it is necessary to approximate marginal and joint probability densities, as well as conditional expected values. In this work, these operators are estimated using the city-block distance function and product multivariate kernel estimators. The algorithm is applied for identifying time series linear models and for selecting inputs for a non-linear model based on neural networks. The neural model is used for modelling monthly average streamflow series of a Brazilian river. Experimental results show good performance of the proposed approach.
Editor: 
Rights: aberto
Identifier DOI: 
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-34047224128&partnerID=40&md5=a4ac270330ccdd653da6563149464002
Date Issue: 2006
Appears in Collections:Unicamp - Artigos e Outros Documentos

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