Forecasting with cue information: A comparison of multiple regression with alternative forecasting approaches

Nikolopoulos, K; Goodwin, P; Patelis, A; Assimakopoulos, V

dc.contributor.author	Nikolopoulos, K	en
dc.contributor.author	Goodwin, P	en
dc.contributor.author	Patelis, A	en
dc.contributor.author	Assimakopoulos, V	en
dc.date.accessioned	2014-03-01T01:26:22Z
dc.date.available	2014-03-01T01:26:22Z
dc.date.issued	2007	en
dc.identifier.issn	0377-2217	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18040
dc.subject	Analogies	en
dc.subject	Forecasting	en
dc.subject	Judgment	en
dc.subject	Neural networks	en
dc.subject	Regression	en
dc.subject.classification	Management	en
dc.subject.classification	Operations Research & Management Science	en
dc.subject.other	Decision theory	en
dc.subject.other	Forecasting	en
dc.subject.other	Linear programming	en
dc.subject.other	Mathematical models	en
dc.subject.other	Neural networks	en
dc.subject.other	Regression analysis	en
dc.subject.other	Alternative forecasting approaches	en
dc.subject.other	Bivariate regression models	en
dc.subject.other	Human judgment	en
dc.subject.other	Nearest neighbor analysis	en
dc.subject.other	Queueing theory	en
dc.title	Forecasting with cue information: A comparison of multiple regression with alternative forecasting approaches	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.ejor.2006.03.047	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.ejor.2006.03.047	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	Multiple linear regression (MLR) is a popular method for producing forecasts when data on relevant independent variables (or cues) is available. The accuracy of the technique in forecasting the impact on Greek TV audience shares of programmes showing sport events is compared with forecasts produced by: (1) a simple bivariate regression model, (2) three different types of artificial neural network, (3) three forms of nearest neighbour analysis and (4) human judgment. MLR was found to perform relatively poorly. The application of Theil's bias decomposition and a Brunswik lens decomposition suggested that this was because of its inability to handle complex non-linearities in the relationship between the dependent variable and the cues and its tendency to overfit the in-sample data. Much higher accuracy was obtained from forecasts based on a simple bivariate regression model, a simple nearest neighbour procedure and from two of the types of artificial neural network. (c) 2006 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	European Journal of Operational Research	en
dc.identifier.doi	10.1016/j.ejor.2006.03.047	en
dc.identifier.isi	ISI:000244380500022	en
dc.identifier.volume	180	en
dc.identifier.issue	1	en
dc.identifier.spage	354	en
dc.identifier.epage	368	en