Adaptive and self-adaptive techniques for evolutionary forecasting applications set in dynamic and uncertain environments
Date
2009
Authors
Wagner, N.
Michalewicz, Z.
Editors
Abraham, A.
Hassanien, A.
de Carvalho, A.
Hassanien, A.
de Carvalho, A.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Book chapter
Citation
Foundations of computational intelligence Volume 4. Bio-inspired data mining, 2009 / Abraham, A., Hassanien, A., de Carvalho, A. (ed./s), vol.204, pp.3-21
Statement of Responsibility
Neal Wagner and Zbigniew Michalewicz
Conference Name
Abstract
Evolutionary 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.
School/Discipline
Dissertation Note
Provenance
Description
© Springer-Verlag Berlin Heidelberg 2009