臺灣博碩士論文加值系統

近年來由於科技不斷的進步發展，人們已經從網路時代正式進入到大數據的時代，大數據不但提供消費者找尋到更多符合自己喜好的商品，也幫助企業帶來更多的獲利。對於現今而言已經沒有一個產業是不需要依靠大數據的幫忙來優化本身的營運能力，但即便擁有了眾多的數據資料，更加重要的是該如何善用這些有效的資源來幫助企業得到更好的預測結果。本研究將針對零售業進行大數據預測分析，利用美國知名零售業Walmart所提供的數據資料進行銷售預測，並透過創建模型發現提升決策樹迴歸(Boosted Decision Tree Regression)對於零售業的預測是最為準確的，期望模型結果未來在其他零售業也能有良好的預測效果。

In recent years, due to the continuous advancement of technology, people have formally entered the era of big data from the Internet era. Big data not only provides consumers with the opportunity to find more products that meet their preferences but also helps enterprises bring more profits. Nowadays, no industry does not need to rely on big data to optimize its operational capability. Even with a large amount of data, it is more important to make the best use of these effective resources to help enterprises get better forecasting. In this study, we will conduct a big data forecasting analysis for the retail industry, using the data provided by Walmart, a well-known retailer in the United States. By creating a model to determine which regression model is the most accurate for the retail industry. It is expected that the result of the model will have good forecast effects on other retail industries in the future.

致謝 V
目錄 VI
圖目錄 VIII
表目錄 IX
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 7
第三節 研究流程 8
第二章 文獻探討 9
第一節 銷售情報管理系統(Point Of Sale) 9
第二節 大數據在虛擬與實體零售業的應用 10
第三節 時間序列在零售業銷售預測的應用 13
第三章 研究方法 16
第一節 資料結構 16
第二節 資料整理 18
第三節 數據分析流程 19
第四章 結果與討論 26
第一節 描述性統計 26
第二節 機器學習過程 30
第三節 機器學習結果整理 35
第五章 結論 39
第一節 研究結論 39
第二節 實務貢獻 39
第三節 研究限制 42
第四節 未來研究 42
參考文獻 43
附錄 49

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