| dc.contributor.advisor | 莊皓鈞 | zh_TW |
| dc.contributor.advisor | Chuang, Howard | en_US |
| dc.contributor.author (Authors) | 阮宣浩 | zh_TW |
| dc.contributor.author (Authors) | Nguyen, Xuan-Hoa | en_US |
| dc.creator (作者) | 阮宣浩 | zh_TW |
| dc.creator (作者) | Nguyen, Xuan-Hoa | en_US |
| dc.date (日期) | 2019 | en_US |
| dc.date.accessioned | 7-Aug-2019 16:22:02 (UTC+8) | - |
| dc.date.available | 7-Aug-2019 16:22:02 (UTC+8) | - |
| dc.date.issued (上傳時間) | 7-Aug-2019 16:22:02 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0106933064 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/124802 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 國際經營管理英語碩士學位學程(IMBA) | zh_TW |
| dc.description (描述) | 106933064 | zh_TW |
| dc.description.abstract (摘要) | Nowadays, the retail industry is very competitive. Most companies in this industry are facing many problems to satisfy customers the most and to be the most efficient. One of the most important problems is to make sales forecasting. In the past, it is more up to experiences to make sales forecasting, therefore the accuracy is often not good. With the development of computer and AI, machine learning methods, in the present, it is easier and more accurate to make a forecast for sales. In this thesis, time series models are applied with the aid of R programming to make sales forecasting. Firstly, we go to understand the basic knowledge about time series models, then we take an example of forecasting sales for a retail shop to apply these methods, including average, naive, snaive, drift, exponential smoothing, ARIMA, dynamic regression models. In the end, we come up with a conclusion about what we did in this thesis. | en_US |
| dc.description.tableofcontents | 1. Introduction 12. Forecasting methods for time series sales data. 32.1. Simple forecasting methods. 42.2. Exponential smoothing. 52.3. ARIMA models. 92.4. Dynamic regression models. 113. The forecasting process. 124. Applying Forecasting to Corporacion Favorita Grocery 155. Conclusion. 306. References 31Appendix 32 | zh_TW |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0106933064 | en_US |
| dc.subject (關鍵詞) | 預測 | zh_TW |
| dc.subject (關鍵詞) | 時間序列 | zh_TW |
| dc.subject (關鍵詞) | 模型 | zh_TW |
| dc.subject (關鍵詞) | 訓練 | zh_TW |
| dc.subject (關鍵詞) | 測試 | zh_TW |
| dc.subject (關鍵詞) | Forecasting | en_US |
| dc.subject (關鍵詞) | Time series | en_US |
| dc.subject (關鍵詞) | Models | en_US |
| dc.subject (關鍵詞) | Training | en_US |
| dc.subject (關鍵詞) | Testing | en_US |
| dc.title (題名) | 時間序列模型於零售銷售預測的應用 | zh_TW |
| dc.title (題名) | An application of time series models to retail sales forecasting | en_US |
| dc.type (資料類型) | thesis | en_US |
| dc.relation.reference (參考文獻) | Brown, R. G. (1959). Statistical forecasting for inventory control. McGraw/Hill.Galit Shmueli, Kenneth C.Lichtendahl Jr (2015) Practical time series forecasting with R: a hand-on guide. Axelrod Schnall Publishers.Holt, C. E. (1957). Forecasting seasonals and trends by exponentially weighted averages (O.N.R. Memorandum No. 52). Carnegie Institute of Technology, Pittsburgh USA.https://www.kaggle.com/c/favorita-grocery-sales-forecasting/overview/descriptionHyndman, R.J., & Athanasopoulos, G. (2018) Forecasting: principles and practice, 2nd edition, OTexts: Melbourne, Australia. OTexts.com/fpp2Wickham, H. (2016). ggplot2: Elegant graphics for data analysis (2nd ed). Springer.Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6, 324–342. | zh_TW |
| dc.identifier.doi (DOI) | 10.6814/NCCU201900291 | en_US |
