Journal
CHEMICAL ENGINEERING RESEARCH & DESIGN
Volume 83, Issue A8, Pages 1009-1018Publisher
INST CHEMICAL ENGINEERS
DOI: 10.1205/cherd.04246
Keywords
customer demand forecasting; support vector regression; statistical learning theory; nonlinear optimization
Categories
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available