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題名 時間序列模型於零售銷售預測的應用
An application of time series models to retail sales forecasting
作者 阮宣浩
Nguyen, Xuan-Hoa
貢獻者 莊皓鈞
Chuang, Howard
阮宣浩
Nguyen, Xuan-Hoa
關鍵詞 預測
時間序列
模型
訓練
測試
Forecasting
Time series
Models
Training
Testing
日期 2019
上傳時間 7-Aug-2019 16:22:02 (UTC+8)
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.
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/description
Hyndman, R.J., & Athanasopoulos, G. (2018) Forecasting: principles and practice, 2nd edition, OTexts: Melbourne, Australia. OTexts.com/fpp2
Wickham, 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.
描述 碩士
國立政治大學
國際經營管理英語碩士學位學程(IMBA)
106933064
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0106933064
資料類型 thesis
dc.contributor.advisor 莊皓鈞zh_TW
dc.contributor.advisor Chuang, Howarden_US
dc.contributor.author (Authors) 阮宣浩zh_TW
dc.contributor.author (Authors) Nguyen, Xuan-Hoaen_US
dc.creator (作者) 阮宣浩zh_TW
dc.creator (作者) Nguyen, Xuan-Hoaen_US
dc.date (日期) 2019en_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) G0106933064en_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) 106933064zh_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 1
2. Forecasting methods for time series sales data. 3
2.1. Simple forecasting methods. 4
2.2. Exponential smoothing. 5
2.3. ARIMA models. 9
2.4. Dynamic regression models. 11
3. The forecasting process. 12
4. Applying Forecasting to Corporacion Favorita Grocery 15
5. Conclusion. 30
6. References 31
Appendix 32		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0106933064en_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) Forecastingen_US
dc.subject (關鍵詞) Time seriesen_US
dc.subject (關鍵詞) Modelsen_US
dc.subject (關鍵詞) Trainingen_US
dc.subject (關鍵詞) Testingen_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 forecastingen_US
dc.type (資料類型) thesisen_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/description
Hyndman, R.J., & Athanasopoulos, G. (2018) Forecasting: principles and practice, 2nd edition, OTexts: Melbourne, Australia. OTexts.com/fpp2
Wickham, 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/NCCU201900291en_US