A Hybrid Model for Forecasting Sales in Turkish Paint Industry

DOI

10.2991/ijcis.2009.2.3.9How to use a DOI?

Abstract

Sales forecasting is important for 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 forecasting method that is applicable in all circumstances. Therefore, confidence in the accuracy of sales forecasts is achieved by corroborating the results using two or more methods. This paper proposes a hybrid forecasting model that uses an artificial intelligence method (AI) with multiple linear regression (MLR) to predict product sales for the largest Turkish paint producer. In the hybrid model, three different AI methods, fuzzy rule-based system (FRBS), artificial neural network (ANN) and adaptive neuro fuzzy network (ANFIS), are used and compared to each other. The results indicate that FRBS yields better forecasting accuracy in terms of root mean squared error (RMSE) and mean absolute percentage error (MAPE).

Copyright

© 2009, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Alp Ustundag
PY  - 2009
DA  - 2009/10/01
TI  - A Hybrid Model for Forecasting Sales in Turkish Paint Industry
JO  - International Journal of Computational Intelligence Systems
SP  - 277
EP  - 287
VL  - 2
IS  - 3
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2009.2.3.9
DO  - 10.2991/ijcis.2009.2.3.9
ID  - Ustundag2009
ER  -