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    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/78054
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/78054


    Title: 指數平滑模型應用於來店人數預測之研究
    Applications of exponential smoothing to store traffic forecasting
    Authors: 施佩吟
    Shih, Pei Yin
    Contributors: 翁久幸
    莊皓鈞

    Weng, Chui Hsing
    Chuang, Hao Chun

    施佩吟
    Shih, Pei Yin
    Keywords: 指數平滑法
    Holt-Winters模型
    狀態空間模型
    模型之最佳化準則
    Date: 2015
    Issue Date: 2015-09-01 16:09:30 (UTC+8)
    Abstract:   零售業是美國最大的產業之一,近年來科技進步以及網路購物擁有價格優勢、交易方便等優點,未來電子商務將成為主流的銷售形式之一,一般實體零售業者如何因應這股潮流是一大課題。
      與本研究有關之美國服飾零售業,實體店家還是占市場的多數,因此,為了提升服飾零售實體店家的競爭優勢,我們預測來店人數,一方面調整人力資源的分配與進貨量,提供顧客優良的服務品質,另一方面視情況提出促銷方案吸引顧客上門,進而提升營運效率。
      每年從感恩節到聖誕節這一個月的時間,是關乎全美零售業生存與否的重要時刻,這段時間的銷售額約占全年銷售總額的1/5,也就表示來店人數在這段期間會維持在一定的數值以上甚至達到全年巔峰,而如何不受影響達到精準預測?本研究欲找出指數平滑法中適合的模型精準預測來店人數的資料。
      本研究旨在探討指數平滑法與延伸之狀態空間模型,指數平滑法屬於時間序列(Time series)的預測方法,是應用相當廣的一種預測方法,一般由趨勢(Trend)以及季節性(Seasonality)組合而成,而將指數平滑模型加入誤差項以後的狀態空間模型,過去一直沒有一個隨機模型做為架構納入概似估計與預測區間等,近幾年才發展出模型之最佳化準則來估計參數,而本研究想探討哪一個狀態空間模型適用於預測來店人數資料以及狀態空間模型之最佳化準則是否能使預測結果更準確。
      本研究之資料為美國時尚精品服飾店2007年營業時間內每小時來店人數,而實證分析後發現Holt-Winters季節性加法模型ETS(A,A,A)蠻適合用來預測來店人數,此外ETS(A,A,A)模型之最佳化準則以AMSE準則與MLE準則表現最佳, MAE準則表現最差。
    Reference: 英文文獻
    1. Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. in B. N. Petrov and F. Csaki (eds.) Second International Symposium on Information Theory, pp. 267–281.
    2. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, Vol. 19, Issue 6, pp. 716–723.
    3. Aoki, M., & Havenner, A. (1991). State space modelling of multiple time series. Econometric Reviews, Vol. 10, Issue 1, pp.1–59.
    4. Dielman, T. E. (2006). Choosing Smoothing Parameters For Exponential Smoothing: Minimizing Sums Of Squared Versus Sums Of Absolute Errors. Journal of Modern Applied Statistical Methods, Vol. 5, No. 1, pp. 118-129.
    5. Gardner, E. S., Jr. (1999). Rule-Based Forecasting vs. Damped-Trend Exponential Smoothing. Management Science, Vol. 45, No. 8, pp. 1169-1176.
    6. Gardner, E. S., Jr. (2006). Exponential smoothing: The state of the art—Part II. International Journal of Forecasting, Vol. 22, Issue 4, pp. 637-666.
    7. Hyndman, R. J. (2013). Online course on forecasting using R.
    8. Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: the forecast package for R. Journal of Statistical Software, Vol. 27, Issue 3, pp. 1-22.
    9. Hyndman, R. J., Koehler, A. B., Ord, J. K., & Snyder, R. D. (2008). Forecasting with Exponential Smoothing. Springer Series in Statistics, pp. 9-29.
    10. Hyndman, R. J., Koehler, A. B., Snyder, R. D., & Grose, S. (2002). A state space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting, Vol.18, Issue 3, pp. 439–454.
    11. Kalman, R. E. (1960). A new approach to linear filtering and prediction problem. Journal of Basic Engineering, Vol. 82, Issue 1, pp.35–45.
    12. Kalman, R. E., & Bucy, R. S. (1961). New results in linear filtering and prediction theory. Journal of Basic Engineering, Vol. 83, Issue 3, pp.95–108.
    13. Lewis, E. B. (1982). Control of body segment differentiation in Drosophila by the bithorax gene complex. Embryonic Development, Part A: Genetics Aspects, Edited by Burger, M. M. and R. Weber. Alan R. Liss, New York, pp. 269-288.
    14. Makridakis, S., & Hibon, M. (1991). Exponential smoothing: The effect of initial values and loss functions on post-sample forecasting accuracy. International Journal of Forecasting, Vol. 7, Issue 3, pp. 317– 330.
    15. Muth, J. F. (1960). Optimal properties of exponentially weighted forecasts. Journal of the American Statistical Association, Vol. 55, Issue 290, pp.299–306.
    16. Ord, J. K., Koehler, A. B., & Snyder, R. D. (1997). Estimation and Prediction for a Class of Dynamic Nonlinear Statistical Models. Journal of the American Statistical Association, Vol. 92, Issue 440, pp.1621-1629.
    17. Stock, K. (2014). These retailers need holiday sales to survive. Business Week.
    18. Winters, P. R. (1960). Forecasting Sales by Exponentially Weighted Moving Averages. Management Science, 6, pp. 324-342.
    中文文獻
    1. 黃心惟 (2010) 以數位口碑為基礎之流行性商品銷售預測,國立臺灣大學資訊管理學研究所碩士論文
    Description: 碩士
    國立政治大學
    統計研究所
    102354021
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0102354021
    Data Type: thesis
    Appears in Collections:[統計學系] 學位論文

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