English  |  正體中文  |  简体中文  |  Items with full text/Total items : 62637/95499 (66%)
Visitors : 3023405      Online Users : 312
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/74246

    Title: 應用灰色支援向量自我迴歸於新產品之需求銷售預測 : 以iPhone為例
    Other Titles: Demand forecasting analysis for IPhone using grey support vector auto regression model
    Authors: 林俊延;Lin, Jyun-Yan
    Contributors: 淡江大學管理科學研究所碩士班
    Keywords: 支援向量迴歸;灰色理論;科技預測;灰色支援向量迴歸;support vector regression (SVR);grey theory;technology forecasting;grey support vector regression
    Date: 2011
    Issue Date: 2011-12-28 18:18:11 (UTC+8)
    Abstract: 因iPhone手機熱賣,打開了一股智慧型手機的風潮,智慧型手機市場的無限商機且市場的發展快速,使得iPhone手機的銷售需求預測,對於相關產業及蘋果公司的市場評估、投資經營和經營管理都具有相當的必要性。
    The surprising boom of iPhone series has led the trend of smartphones. There are infinite business opportunities in the smartphone market and the market grows so fast, so it is significant for us to keep an eye on the development of smartphone market. The forecast of iPhone demand is necessary in the market assessment, investment and management of relevant industries and Apple Computer, Inc.
    Because the iPhone new going on the market product''s sales volume data information are few, and is indefinite, therefore this research uses the grey theory analysis to be suitable in the minority indefinite question merit and in the support vector return supports the vector and the tolerance error''s analysis concept, proposed that forecasts and the support vector using the union gray returns both merit the model “the gray support vector return pattern” and unifies the self-vector return the concept, forecast that the new product the sales demand, hoped may take advantage of the enhancement forecast accurate; And proposes the method achievement forecast achievements weight target by MAPE and this research institute.
    The real diagnosis result showed that the AutoGSVR model (MAPE=11.69%) showed significantly better than GSVR model (MAPE=12.99%), simple regression model (MAPE=18.06%), single exponential smoothing model (MAPE=27.38%) and GM(1,1) model (MAPE=13.06%), and can successfully extrapolation forecast that in 2011 the second season the iPhone handset sale digit, the MAPE values up to 0.01%.
    Appears in Collections:[Department of Management Sciences] Thesis

    Files in This Item:

    File SizeFormat

    All items in 機構典藏 are protected by copyright, with all rights reserved.

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