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Forecasting Demand With Support Vector Regression Technique Incorporating Feature Selection in the Presence of Calendar Effect

Forecasting Demand With Support Vector Regression Technique Incorporating Feature Selection in the Presence of Calendar Effect
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Author(s): Malek Sarhani (ENSIAS, Mohammed V University, Morocco) and Abdellatif El Afia (ENSIAS, Mohammed V University, Morocco)
Copyright: 2018
Pages: 15
Source title: Contemporary Approaches and Strategies for Applied Logistics
Source Author(s)/Editor(s): Lincoln C. Wood (University of Otago, New Zealand)
DOI: 10.4018/978-1-5225-5273-4.ch012

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Abstract

Reliable prediction of future demand is needed to better manage and optimize supply chains. However, a difficulty of forecasting demand arises due to the fact that heterogeneous factors may affect it. Analyzing such data by using classical time series forecasting methods will fail to capture such dependency of factors. This chapter addresses these problems by examining the use of feature selection in forecasting using support vector regression while eliminating the calendar effect using X13-ARIMA-SEATS. The approach is investigated in three different case studies.

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