English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 56568/90363 (63%)
造访人次 : 11873308      在线人数 : 50
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻

    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/111777

    题名: Sales forecasting by combining clustering and machine-learning techniques for computer retailing
    作者: I-Fei Chen;Chi-Jie Lu
    关键词: Sales forecasting;Computer retailing;Clustering algorithm;Machine learning
    日期: 2017-09
    上传时间: 2017-10-06 02:10:16 (UTC+8)
    出版者: Springer
    摘要: Sales forecasting is a critical task for computer retailers endeavoring to maintain favorable sales performance and manage inventories. In this study, a clustering-based forecasting model by combining clustering and machine-learning methods is proposed for computer retailing sales forecasting. The proposed method first used the clustering technique to divide training data into groups, clustering data with similar features or patterns into a group. Subsequently, machine-learning techniques are used to train the forecasting model of each group. After the cluster with data patterns most similar to the test data was determined, the trained forecasting model of the cluster was adopted for sales forecasting. Since the sales data of computer retailers show similar data patterns or features at different time periods, the accuracy of the forecast can be enhanced by using the proposed clustering-based forecasting model. Three clustering techniques including self-organizing map (SOM), growing hierarchical self-organizing map (GHSOM), and K-means and two machine-learning techniques including support vector regression (SVR) and extreme learning machine (ELM) are used in this study. A total of six clustering-based forecasting models were proposed. Real-life sales data for the personal computers, notebook computers, and liquid crystal displays are used as the empirical examples. The experimental results showed that the model combining the GHSOM and ELM provided superior forecasting performance for all three products compared with the other five forecasting models, as well as the single SVR and single ELM models. It can be effectively used as a clustering-based sales forecasting model for computer retailing.
    關聯: Neural Computing and Applications 28(9), p.2633-2647
    DOI: 10.1007/s00521-016-2215-x
    显示于类别:[管理科學學系暨研究所] 期刊論文


    档案 描述 大小格式浏览次数
    Sales forecasting by combining clustering and machine-learning techniques for computer retailing.pdf8407KbAdobe PDF3检视/开启



    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回馈