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Sales Forecasting Using Artificial Neural Networks

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  • Marusia Ivanova

Abstract

This paper is an attempt to introduce the essence and methodology of Artificial Neural Networks (ANN) in marketing. In order to outline the benefits of their analytical application in marketing management, it is demonstrated how to solve a predictive problem applying one of the most widely used types of ANN - Multi-Layered Feedforward Neural Network. Sales history data about four brands from the Fast Moving Consumer Goods sector in Bulgaria are used for the analysis. Time series are examined for presence of sparsity, outliers and nonstationarity, because these data characteristics can have a significant effect on the accuracy of predictions. It has been proven that ANN gives more accurate predictions in comparison with traditional methods, such as ARIMA and Exponential Smoothing.

Suggested Citation

  • Marusia Ivanova, 2005. "Sales Forecasting Using Artificial Neural Networks," Economic Thought journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 6, pages 60-81.
    • Handle: RePEc:bas:econth:y:2005:i:6:p:60-81
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    References listed on IDEAS

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    1. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
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    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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