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研究生:劉育吟
研究生(外文):Yu-Yin Liu
論文名稱:運用正規化迴歸分析線上銷售預測: 以在亞馬遜上的運動商品為例
論文名稱(外文):Online Sales Forecasting by Regularized Regression for functional products: Taking Sport Goods on Amazon.com as an Example.
指導教授:孔令傑孔令傑引用關係
指導教授(外文):Ling-Chieh Kung
口試委員:陳聿宏林真如
口試委員(外文):Yu-Hung ChenChen-Ju Lin
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:企業管理碩士專班
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:英文
論文頁數:76
中文關鍵詞:電子商務銷售預測機器學習套索回歸嶺回歸
外文關鍵詞:e-commercesales forecastingmachine learningLASSO regressionRidge regression
DOI:10.6342/NTU202000275
相關次數:
  • 被引用被引用:3
  • 點閱點閱:284
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
Abstract
In this study, we research on a company’s sport good sales forecasting on Amazon.com. We analyze data including transactions, advertisement reports, customer reviews, competitors’ prices and customer reviews, holiday-or-not, and weekend-or-not for more than 500 days. We implement machine learning models to tackle the sales forecasting problem. The main objective of this study is to discover the most efficient model among linear, LASSO, and Ridge regression by comparing their mean absolute error in the testing set. We find that the most efficient model is LASSO regression in general, whose performance may be better than linear regression by 87 % on a certain product.
Contents
Acknowledgements ........................................................................................................... I
Abstract ............................................................................................................................. II
List of Tables .................................................................................................................. VI
List of Figures ................................................................................................................ VII
Chapter 1 Introduction .............................................................................................. 1
1.1 Background and motivation ................................................................................. 1
1.2 Research objectives ............................................................................................. 4
1.3 Research plan ...................................................................................................... 5
Chapter 2 Literature Review ..................................................................................... 6
2.1 E-commerce ....................................................................................................... 6
2.2 Customer reviews ................................................................................................ 7
2.3 Sales/demand forecasting ..................................................................................... 8
2.4 Machine learning ................................................................................................ 9
2.4.1 LASSO regression ........................................................................................... 10
2.4.2 Ridge regression .............................................................................................. 10
Chapter 3 Problem definition and research method ................................................ 12
3.1 Data collection .................................................................................................. 13
3.1.1 Company T’s transaction records ........................................................................ 13
3.1.2 Company T’s advertising report .......................................................................... 13
3.1.3 Competitor reviews .......................................................................................... 14
3.1.4 Star bar .......................................................................................................... 15
3.1.5 Time related variables ...................................................................................... 18
3.1.6 Variable table ................................................................................................. 19
3.2 Regression model .............................................................................................. 22
3.2.1 Training, validation, and testing sets .................................................................... 22
3.2.2 Linear regression ............................................................................................. 23
3.2.3 LASSO regression ........................................................................................... 24
3.2.4 Ridge regression .............................................................................................. 25
3.3 Regression performance metric: MAE ................................................................ 26
Chapter 4 Analysis and Results ............................................................................... 28
4.1 Data cleansing ................................................................................................... 28
4.2 Technical result ................................................................................................. 29
4.2.1 Model comparison ........................................................................................... 30
4.2.2 Investigation for Yoga Mat Strap ........................................................................ 38
4.2.3 Result of different splitting ratio ......................................................................... 41
4.3 Managerial implications .................................................................................... 57
Chapter 5 Conclusions and Future Works ............................................................... 60
5.1 Conclusions ...................................................................................................... 60
5.2 Future works ..................................................................................................... 61
Bibliography ................................................................................................................... 63
Appendix ........................................................................................................................ 65
Bibliography
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algorithm to enhance real estateappraisal forecasting. Expert Systems with
Applications, pp. 8369-8379.
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64
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Yves, R.S., E. Aghezzaf, N. Kourentzes, and B. Desmet. (2017). Tactical sales
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Open Cybernetics & Systemics, pp. 2135-2140.
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