「小餐廳‧大數據」計畫成果專區
Our Achievement for "Small Restaurant, Big Data" Project
☆ 研究團隊 Research Team
☆ 研究成果 Research Results
① 每日來客數預測 Daily incoming customers forecasting
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② 【Conference Paper】Ashouri, M., Shmueli, G., (2017), “A New Tree-Based Method for Clustering Time Series”, Statistical Conference in E-Commerce Research (SCECR), Ho Chi Minh City, Vietnam.
Slides in SCECR talk: A New Tree-Based Method for Clustering Time Series
Slides in SCECR talk: A New Tree-Based Method for Clustering Time Series
③【 Student Thesis】,Lee, Tung-Yu (2017),"Sales Forecasting for New Items with Short Histories: Comparing Strategies for Time-Series Matching and Forecasting using Time-Series Clusters"Master's Thesis of Institute of Service Science,National Tsing Hua University, Hsinchu, Taiwan.
④ 【BAFT Team Project】
⒜ Topic: Forecasting the daily number of customers in each restaurant
Team members: Edison Lee, Celia Chen, Sehyeon Jeong, Guan-Jie Chen, Web Yuan
Find more details about Forecasting the daily number of customers in each restaurant
⒝ Topic: Forecasting restaurant sales using data from iChef, weather forecasts, and holiday information
Team members: Nicholas Danks, Isaac Martinez, Mahsa Ashouri, Paul Rivera
Find more details about Forecasting restaurant sales using data from iChef, weather forecasts, and holiday information
⒜ Topic: Forecasting the daily number of customers in each restaurant
Team members: Edison Lee, Celia Chen, Sehyeon Jeong, Guan-Jie Chen, Web Yuan
Find more details about Forecasting the daily number of customers in each restaurant
⒝ Topic: Forecasting restaurant sales using data from iChef, weather forecasts, and holiday information
Team members: Nicholas Danks, Isaac Martinez, Mahsa Ashouri, Paul Rivera
Find more details about Forecasting restaurant sales using data from iChef, weather forecasts, and holiday information
⑤【Video】Crust Preferences
⑥【Video】Popular Combinations of Take out Menu Items
⑦【Video】Forecasting Daily Number of Customers
☆ 媒體報導 Media Exposure
【三立新聞】小餐廳大資料!機器學習引爆餐飲革命
【中央社】 餐廳資料像小宇宙 比銀河系行星多十倍
【新唐人亞洲台】 拉亞漢堡App升級大資料科技與台灣微軟、清大合作
【數位時代】iCHEF攜手森邦集團、服務科學專家,切入餐廳資料大數據分析
【中央社】 餐廳資料像小宇宙 比銀河系行星多十倍
【新唐人亞洲台】 拉亞漢堡App升級大資料科技與台灣微軟、清大合作
【數位時代】iCHEF攜手森邦集團、服務科學專家,切入餐廳資料大數據分析