Title :
Demand forecasting method in logistics management based on support vector machine
Author_Institution :
Wuhan S&T information center, Evaluation and tendering dept., Wuhan S&T information center, Wuhan, China
Abstract :
This paper surveyed a novel demand forecasting method in logistics management based on Support vector machine. Firstly, a sliding time window is built and data in the sliding time window are employed to construct the model. Then we set up the demand forecasting model based on support vector regression. Results showed that this model proves to be effective and applicable for the demand forecasting in logistics management.
Keywords :
Demand forecasting; Kernel; Logistics; Predictive models; Support vector machines; Time series analysis; Support vector machine; demand forecasting; logistics; sliding time window; time series;
Conference_Titel :
E -Business and E -Government (ICEE), 2011 International Conference on
Conference_Location :
Shanghai, China
Print_ISBN :
978-1-4244-8691-5
DOI :
10.1109/ICEBEG.2011.5881491
