Affiliations: [a]
University Autonomous of Occidente, Culiacán, México | [b] School of Information, Systems and Modelling, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia
| [c] Department of Management Control and Information Systems, School of Economics and Business, University of Chile, Santiago, Chile
Correspondence:
[*]
Corresponding author. Martha Flores-Sosa, University Autonomous of Occidente, Culiacán, México. E-mail: martha.flores@udo.mx.
Abstract: The induced ordered weighted average (IOWA) is an aggregation operator that provides a parameterized family of operators between the minimum and the maximum. This work presents a new application that uses the simple linear regression (LR) and the IOWA operator in the same formulation. We study some of its main properties and particular cases. The main advantage of the linear regression IOWA operator is that it unifies the IOWA operator with the linear regression in the same formulation considering the degree of optimism and pessimism of the decision maker. Thus, we can under- or over- estimate the regression according to complex attitudes that the decision may have in the analysis. The work ends analyzing the applicability of this new approach in a problem regarding exchange rate forecasting. The objective of the new approach is to analyze the information in a more complete way.
Keywords: IOWA operator, linear regression, aggregation operator
