Forecasting the exchange rate with multiple linear regression and heavy ordered weighted average operators

Flores-Sosa, M; León-Castro, E; Merigó, JM; Yager, RR

Forecasting the exchange rate with multiple linear regression and heavy ordered weighted average operators

Flores-Sosa, M León-Castro, E Merigó, JM Yager, RR

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Knowledge-Based Systems, 2022, 248, pp. 108863
Issue Date:: 2022-07-19

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Field	Value	Language
dc.contributor.author	Flores-Sosa, M
dc.contributor.author	León-Castro, E
dc.contributor.author	Merigó, JM
dc.contributor.author	Yager, RR
dc.date.accessioned	2023-03-21T23:10:18Z
dc.date.available	2023-03-21T23:10:18Z
dc.date.issued	2022-07-19
dc.identifier.citation	Knowledge-Based Systems, 2022, 248, pp. 108863
dc.identifier.issn	0950-7051
dc.identifier.uri	http://hdl.handle.net/10453/167993
dc.description.abstract	This paper introduces the multiple linear regression heavy ordered weighted average (MLR-HOWA) operator. On the MLR-HOWA operator, the beta values are obtained with the use of the HOWA means. In that sense, it provides a new range of possibilities by under or overestimating the result based on the decision maker's expectations and knowledge. Therefore, the MLR-HOWA provides a forecasting tool that can analyze multiple scenarios from minimum to maximum. The main properties and two extensions using induced and generalized variables are also presented. An application in exchange rate forecasting based on inflation and interest rate as independent variables for five Latin American countries is submitted. Among the main results, it is possible to identify that the forecasting error is reduced when different combinations of MLR with OWA operators are done.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Knowledge-Based Systems
dc.relation.isbasedon	10.1016/j.knosys.2022.108863
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 15 Commerce, Management, Tourism and Services, 17 Psychology and Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Forecasting the exchange rate with multiple linear regression and heavy ordered weighted average operators
dc.type	Journal Article
utslib.citation.volume	248
utslib.for	08 Information and Computing Sciences
utslib.for	15 Commerce, Management, Tourism and Services
utslib.for	17 Psychology and Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-21T23:10:17Z
pubs.publication-status	Published
pubs.volume	248

Abstract:

This paper introduces the multiple linear regression heavy ordered weighted average (MLR-HOWA) operator. On the MLR-HOWA operator, the beta values are obtained with the use of the HOWA means. In that sense, it provides a new range of possibilities by under or overestimating the result based on the decision maker's expectations and knowledge. Therefore, the MLR-HOWA provides a forecasting tool that can analyze multiple scenarios from minimum to maximum. The main properties and two extensions using induced and generalized variables are also presented. An application in exchange rate forecasting based on inflation and interest rate as independent variables for five Latin American countries is submitted. Among the main results, it is possible to identify that the forecasting error is reduced when different combinations of MLR with OWA operators are done.

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http://hdl.handle.net/10453/167993