Estimating the impact of shocks with artificial neural networks

Nikolopoulos, K; Bougioukos, N; Giannelos, K; Assimakopoulos, V

dc.contributor.author	Nikolopoulos, K	en
dc.contributor.author	Bougioukos, N	en
dc.contributor.author	Giannelos, K	en
dc.contributor.author	Assimakopoulos, V	en
dc.date.accessioned	2014-03-01T02:44:36Z
dc.date.available	2014-03-01T02:44:36Z
dc.date.issued	2007	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31901
dc.subject	Artificial neural networks	en
dc.subject	Forecasting	en
dc.subject	Irregular events	en
dc.subject	Shocks	en
dc.subject.other	Decision support systems	en
dc.subject.other	Laws and legislation	en
dc.subject.other	Mathematical models	en
dc.subject.other	Parameter estimation	en
dc.subject.other	Time series analysis	en
dc.subject.other	Irregular events	en
dc.subject.other	Multiple Linear Regression	en
dc.subject.other	Neural networks	en
dc.title	Estimating the impact of shocks with artificial neural networks	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-540-74695-9_49	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-540-74695-9_49	en
heal.publicationDate	2007	en
heal.abstract	Quantitative models are very successful forr extrapolating the basic trend-cycle component of time series. On the contrary time series models failed to handle adequately shocks or irregular events, that is non-periodic events such as oil crises, promotions, strikes, announcements, legislation etc. Forecasters usually prefer to use their own judgment in such problems. However their efficiency in such tasks is in doubt too and as a result the need of decision support tools in this procedure seem to be quite important. Forecasting with neural networks has been very popular across the Academia in the last decade. Estimating the impact of irregular events has been one of the most successful application areas. This study examines the relative performance of Artificial Neural Networks versus Multiple Linear Regression for estimating the impact of expected irregular future events. © Springer-Verlag Berlin Heidelberg 2007.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/978-3-540-74695-9_49	en
dc.identifier.volume	4669 LNCS	en
dc.identifier.issue	PART 2	en
dc.identifier.spage	476	en
dc.identifier.epage	485	en