Sales forecasting for a Turkish paint producer: Artificial intelligence based methods versus multiple linear regression

Alp Ustundag*, Emre Cevikcan, Mehmet Serdar Kilinc

*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

Sales forecasting has a great impact on facilitating effective and efficient allocation of scarce resources. However, how to best model and forecast sales has been a long-standing issue. There is no best method of forecasting in all circumstances. Therefore, confidence in the accuracy of sales forecasts is derived by corroborating the results using two or more methods. This paper evaluates the relative performance of Linear Multiple Regression, Artificial Neural Networks and Adaptive Neuro Fuzzy Networks by applying them to the problem of sales forecasting for a Turkish paint producer firm. The results indicate that Adaptive Neuro Fuzzy Networks yields better forecasting accuracy in terms of Root Mean Square Error and Mean Absolute Deviation.

Original languageEnglish
Title of host publicationWorld Scientific Proceedings Series on Computer Engineering and Information Science 1; Computational Intelligence in Decision and Control - Proceedings of the 8th International FLINS Conference
PublisherWorld Scientific Publishing Co. Pte Ltd
Pages49-54
Number of pages6
ISBN (Print)981279946X, 9789812799463
DOIs
Publication statusPublished - 2008
EventComputational Intelligence in Decision and Control - 8th International FLINS Conference - Madrid, Spain
Duration: 21 Sept 200824 Sept 2008

Publication series

NameWorld Scientific Proceedings Series on Computer Engineering and Information Science 1; Computational Intelligence in Decision and Control - Proceedings of the 8th International FLINS Conference

Conference

ConferenceComputational Intelligence in Decision and Control - 8th International FLINS Conference
Country/TerritorySpain
CityMadrid
Period21/09/0824/09/08

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