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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 孔令傑(Ling-Chieh Kung) | |
| dc.contributor.author | Yu-Yin Liu | en |
| dc.contributor.author | 劉育吟 | zh_TW |
| dc.date.accessioned | 2021-06-17T01:08:26Z | - |
| dc.date.available | 2020-02-24 | |
| dc.date.copyright | 2020-02-24 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-02-03 | |
| dc.identifier.citation | Bibliography
Ahn, J.J., H.W. Byun, K.J. Oh, and T.Y. Kim. (2012). Using ridge regression with genetic algorithm to enhance real estateappraisal forecasting. Expert Systems with Applications, pp. 8369-8379. Amazon. (2019). FBA inventory storage limit. Retrieved from Amazon Seller Central: https://sellercentral.amazon.com/gp/help/external/XLRKWL8L5BMSHWB Cook, A., and T. Goette. (2006). Mobile Electronic Commerce: What Is It? Who Uses It? And Why Use It? Communications of the IIMA , pp. 49-58. Cui, G., H.K. Lui, and X. Guo. (2012). The Effect of Online Consumer Reviews on New Product Sales. International Journal of Electronic Commerce, pp. 39-58. Thorsten, H.T., and W. Gianfranco. (2004). Electronic Word-of-Mouth: Motives for and Consequences of Reading Customer Articulations on the Internet. International Journal of Electronic Commerce, pp. 51-74. Hoerl, E., and W.K. Robert. (1969). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, pp. 55-67. Huang, J.H., and Y.F. Chen. (2006). Herding in Online Product Choice. Psychology & Marketing, pp. 413-428. 64 Krishna, A., V. Akhilesh, A. Aich, and C. Hegde. (2018). Sales-forecasting of Retail Stores using Machine Learning Techniques. IEEE, pp. 160-166. Lin, K.J. (2008). E-Commerce Technology Back to a Prominent Future. IEEE, pp. 60-65. Ma, S., R. Fildes, and T. Huang. (2016). Demand forecasting with high dimensional data: The case of SKU retailsales forecasting with intra- and inter-category promotional information. European Journal of Operational Research, pp. 245-257. Nikolay, O., G. Vishal, and S. Sridhar. (2013). Sales Forecasting with Financial Indicators and Experts’ Input. Production and Operations Management, pp. 1056-1076. Ripley, B.D. (1996). Pattern Recognition and Neural Networks. Tibshirani, R. (1996). Regression shrinkage and selection via the LASSO. Royal Statistical Society, pp. 267-288. Yeo, J., S. Kim, E. Koh, S. Hwang, and N. Lipka. (2016). Browsing2purchase: Online Customer Model for Sales Forecasting in an E-Commerce Site. (pp. 133-134). WWW '16 Companion. Yves, R.S., E. Aghezzaf, N. Kourentzes, and B. Desmet. (2017). Tactical sales forecasting using a very large set of macroeconomic indicators. Belgium. Zhou, C. (2015). Impact of Electronic Commerce on the Sporting Goods Market. The Open Cybernetics & Systemics, pp. 2135-2140. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/66788 | - |
| dc.description.abstract | 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. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T01:08:26Z (GMT). No. of bitstreams: 1 ntu-109-R07749018-1.pdf: 6037684 bytes, checksum: 702e9d205867de4e8bf2780344449367 (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 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 | |
| dc.language.iso | en | |
| dc.subject | 套索回歸 | zh_TW |
| dc.subject | 嶺回歸 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 銷售預測 | zh_TW |
| dc.subject | 電子商務 | zh_TW |
| dc.subject | e-commerce | en |
| dc.subject | sales forecasting | en |
| dc.subject | machine learning | en |
| dc.subject | LASSO regression | en |
| dc.subject | Ridge regression | en |
| dc.title | 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 | zh_TW |
| dc.title | Online Sales Forecasting by Regularized Regression
for functional products: Taking Sport Goods on Amazon.com as an Example. | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳聿宏(Yu-Hung Chen),林真如(Chen-Ju Lin) | |
| dc.subject.keyword | 電子商務,銷售預測,機器學習,套索回歸,嶺回歸, | zh_TW |
| dc.subject.keyword | e-commerce,sales forecasting,machine learning,LASSO regression,Ridge regression, | en |
| dc.relation.page | 76 | |
| dc.identifier.doi | 10.6342/NTU202000275 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2020-02-03 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 企業管理碩士專班 | zh_TW |
| 顯示於系所單位： | 管理學院企業管理專班(Global MBA) | |
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|---|---|---|---|
| ntu-109-1.pdf 未授權公開取用 | 5.9 MB | Adobe PDF |
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