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Adaptive and self-adaptive techniques for evolutionary forecasting applications set in dynamic and uncertain environments

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dc.contributor.authorWagner, N.
dc.contributor.authorMichalewicz, Z.
dc.contributor.editorAbraham, A.
dc.contributor.editorHassanien, A.
dc.contributor.editorde Carvalho, A.
dc.date.issued2009
dc.description© Springer-Verlag Berlin Heidelberg 2009
dc.description.abstractEvolutionary Computation techniques have proven their applicability for time series forecasting in a number of studies. However these studies, like those applying other techniques, have assumed a static environment, making them unsuitable for many real-world forecasting concerns which are characterized by uncertain environments and constantly-shifting conditions. This chapter summarizes the results of recent studies that investigate adaptive evolutionary techniques for time series forecasting in non-static environments and proposes a new, self-adaptive technique that addresses shortcomings seen from these studies. A theoretical analysis of the proposed technique’s efficacy in the presence of shifting conditions and noise is given.
dc.description.statementofresponsibilityNeal Wagner and Zbigniew Michalewicz
dc.identifier.citationFoundations of computational intelligence Volume 4. Bio-inspired data mining, 2009 / Abraham, A., Hassanien, A., de Carvalho, A. (ed./s), vol.204, pp.3-21
dc.identifier.doi10.1007/978-3-642-01088-0_1
dc.identifier.isbn9783642010873
dc.identifier.urihttp://hdl.handle.net/2440/57121
dc.language.isoen
dc.publisherSpringer
dc.publisher.placeGermany
dc.relation.ispartofseriesStudies in Computational Intelligence
dc.source.urihttps://doi.org/10.1007/978-3-642-01088-0_1
dc.titleAdaptive and self-adaptive techniques for evolutionary forecasting applications set in dynamic and uncertain environments
dc.typeBook chapter
pubs.publication-statusPublished

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