Customer Demand Forecasting via Support Vector Regression Analysis

This paper presents a systematic optimization-based approach for customer demand forecasting through support vector regression (SVR) analysis. The proposed methodology is based on the recently developed statistical learning theory (Vapnik, 1998) and its applications on SVR. The proposed three-step algorithm comprises both nonlinear programming (NLP) and linear programming (LP) mathematical model formulations to determine the regression function while the final step employs a recursive methodology to perform customer demand forecasting. Based on historical sales data, the algorithm features an adaptive and flexible regression function able to identify the underlying customer demand patterns from the available training points so as to capture customer behaviour and derive an accurate forecast. The applicability of our proposed methodology is demonstrated by a number of illustrative examples.