請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/72538完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 孔令傑(Ling-Chieh Kung) | |
| dc.contributor.author | Chu-Lien Ku | en |
| dc.contributor.author | 古竺璉 | zh_TW |
| dc.date.accessioned | 2021-06-17T07:00:32Z | - |
| dc.date.available | 2021-02-22 | |
| dc.date.copyright | 2021-02-22 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2021-01-14 | |
| dc.identifier.citation | Arkieva. (2018). Why is Demand Forecasting important for effective Supply Chain Management? Retrieved from https://blog.arkieva.com on December 10, 2020. BCG. (2018). Unlocking Growth in CPG with AI and Advanced Analytics. Retrieved from https://www.bcg.com on December 8, 2020 Epstein, R., Neely, A., Weintraub, A., Valenzuela, F., Hurtado, S., Gonzalez, G., Beiza, A., Naveas, M., Infante, F., Alarcon, F., Angulo, G., Berner, C., Catalan, J., Gonzalez, C., Yung, D. (2012, January–February). A Strategic Empty Container Logistics Optimization in a Major Shipping Company. Interfaces, 42(1), pp. 5-16. Forbes. (2014). Two Key Executives Leave Walgreen Due To a $1 Billion Forecasting Error. Retrieved from https://www.forbes.com on December 15, 2020. Mckinsey. (2020). Supply-chain recovery in coronavirus times—plan for now and the future. Retrieved from https://www.mckinsey.com on December 8, 2020 Pekgün, P., Menich, R. P., Acharya, S., Finch, P. G., Deschamps, F., Mallery, K., Sistine, J. V., Christianson, K., Fuller, J. (2013, January-February). Carlson Rezidor Hotel Group Maximizes Revenue through Improved Demand Management and Price Optimization. Interfaces, 43(1), pp. 21-36. Thomopoulos, N. T. (2015). Demand Forecasting for Inventory Control. Springer Zhang, X., Meiser, D., Liu, Y., Bonner, B., Lin, L. (2014, January-February). Kroger Uses Simulation-Optimization to Improve Pharmacy Inventory Management. Interfaces, 44(1), pp. 70-84. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/72538 | - |
| dc.description.abstract | NONE | zh_TW |
| dc.description.abstract | In this thesis, we research a cosmetics company’s sales forecasting. We analyze the sales data from 2017 to 2020, a total of 38 months sales record. The objective of this study is to find the best forecasting model automatically. We implement twelve models using four time series forecasting methods, including Moving Average, Linear Regression, Exponential Smoothing, and Holt-Winter Exponential Smoothing, with three different ways of applying seasonality. Among the top 100 sales items, we improve the accuracy of forecasting averagely by 12.9% and increase the forecasting performance for more than half of the items by more than 20% by using the proposed models. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T07:00:32Z (GMT). No. of bitstreams: 1 U0001-1301202115263300.pdf: 1579567 bytes, checksum: 0784a0205fce7244107ad3a747197bab (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | Contests Abstract I List of Tables II List of Figures III Chapter 1 Introduction 1 1.1 Background and motivation 1 1.2 Research objectives 3 1.3 Research plan 4 Chapter 2 Literature Review 5 Chapter 3 Research Methods 9 3.1 Data collection 9 3.2 Data preprocessing 10 3.3 Forecasting methods 12 3.3.1 Evaluation of Accuracy 13 3.3.2 Time series methods 13 3.3.2.1 Moving Average 13 3.3.2.2 Linear Regression 14 3.3.2.3 Exponential Smoothing 15 3.3.2.4 Holt-Winters Exponential Smoothing 17 3.3.3 Seasonal indices 18 3.3.4 Proposed forecasting frameworks 21 Chapter 4 Analysis and Results 22 4.1 Training Set and Testing Set 22 4.2 Current Forecasting Method 23 4.3 Proposed Forecasting Method 25 4.4 Result of applying seasonality 27 Chapter 5 Conclusions 29 Bibliography 31 Appendix 33 | |
| dc.language.iso | en | |
| dc.subject | 線性迴歸 | zh_TW |
| dc.subject | 移動平均 | zh_TW |
| dc.subject | 時間序列 | zh_TW |
| dc.subject | 自動化選擇 | zh_TW |
| dc.subject | 需求預測 | zh_TW |
| dc.subject | 指數平滑 | zh_TW |
| dc.subject | time series | en |
| dc.subject | Exponential Smoothing | en |
| dc.subject | Linear Regression | en |
| dc.subject | Moving Average | en |
| dc.subject | Holt-Winters Exponential Smoothing | en |
| dc.subject | automatic model selection | en |
| dc.subject | sales forecasting | en |
| dc.title | 銷售預測之自動化方法選擇：理論模型與實證研究 | zh_TW |
| dc.title | Demand Forecasting by Automatic Model Selection | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 109-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 柯冠州(Kuan-Chou Ko),陳聿宏(Yu-Hung Chen) | |
| dc.subject.keyword | 需求預測,自動化選擇,時間序列,移動平均,線性迴歸,指數平滑, | zh_TW |
| dc.subject.keyword | sales forecasting,automatic model selection,time series,Moving Average,Linear Regression,Exponential Smoothing,Holt-Winters Exponential Smoothing, | en |
| dc.relation.page | 35 | |
| dc.identifier.doi | 10.6342/NTU202100054 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2021-01-14 | |
| 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) | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-1301202115263300.pdf 未授權公開取用 | 1.54 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。