• DocumentCode
    1631238
  • Title

    Demand forecasting method in logistics management based on support vector machine

  • Author

    Yan, Yao

  • Author_Institution
    Wuhan S&T information center, Evaluation and tendering dept., Wuhan S&T information center, Wuhan, China
  • fYear
    2011
  • Firstpage
    1
  • Lastpage
    4
  • 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;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    E -Business and E -Government (ICEE), 2011 International Conference on
  • Conference_Location
    Shanghai, China
  • Print_ISBN
    978-1-4244-8691-5
  • Type

    conf

  • DOI
    10.1109/ICEBEG.2011.5881491
  • Filename
    5881491
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1631238