Evolving fuzzy linear regression tree approach for forecasting sales volume of petroleum products

Andre Lemos, Daniel Leite, Leandro MacIel, Rosangela Ballini, Walmir Caminhas, Fernando Gomide

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

The 2012 FUZZ-IEEE conference competition Learning Fuzzy Systems from Data aims to establish the empirical accuracy of fuzzy forecasting algorithms in the domain of prediction of the sales volume of petroleum products. Currently, there are no guidelines or consensus on a best practice methodology. This paper proposes evolving fuzzy linear regression trees (eFT) to extract from both, daily prices and past sales volume data, information of interest to attain accurate forecasts of the next day sales. Essentially, eFT attempts to find spatio-temporal correlations from a historical perspective of competitors' prices and previous sales. A dimension reduction method based on the least angle regression (LARS) algorithm is considered for input variable selection. Computational experiments show that the eFT predictor using LARS is an effective approach to nonlinear time series forecasting providing encouraging results in the competition scenario.

Original languageEnglish
Title of host publication2012 IEEE International Conference on Fuzzy Systems, FUZZ 2012
DOIs
StatePublished - 2012
Externally publishedYes
Event2012 IEEE International Conference on Fuzzy Systems, FUZZ 2012 - Brisbane, QLD, Australia
Duration: 10 Jun 201215 Jun 2012

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584

Conference

Conference2012 IEEE International Conference on Fuzzy Systems, FUZZ 2012
Country/TerritoryAustralia
CityBrisbane, QLD
Period10/06/1215/06/12

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