查詢教學大綱與進度

教學大綱與進度

課程基本資料：

學年期課號課程名稱階段學分時數修教師班級人撤備註

113-2 342120 時間序列分析與製程控制 1 3.0 3 ★ 張漢利工管所 15 0 　

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
113-2	342120	時間序列分析與製程控制	1	3.0	3	★	張漢利	工管所	15	0

教學大綱與進度：

教師姓名張漢利　教師諮商時間(Office Hours)

Email hendri@mail.ntut.edu.tw

最後更新時間 2025-02-17 17:18:46

課程大綱 This course provides a comprehensive understanding of time series analysis and process control techniques, with an emphasis on coding and conducting experiments, such as verifying results from research papers. Students will learn to analyze time-dependent data and apply various forecasting and process monitoring methods. Key topics include time series preprocessing, regression analysis, ARIMA models, and advanced techniques such as statistical process control, CUSUM, EWMA, and multivariate monitoring. The course also covers real-time contrasts and includes paper reading and presentations to explore contemporary applications in time series analysis and process control. Basic programming skills is expected.

課程進度 Week 1: Course Orientation Week 2: Time series data preprocessing Week 3: Time series visualization & analysis Week 4: Introduction to forecasting Week 5: Regression analysis Week 6: Exponential smoothing Week 7: ARIMA Week 8: Mid term Week 9: Time series data mining (I) Week 10: Time series data mining (II) Week 11: Statistical process control Week 12: CUSUM charts Week 13: EWMA charts Week 14: Multivariate process monitoring Week 15: Real-time contrasts Week 16: Paper reading & presentation Week 17: Paper reading & presentation Week 18: Course wrap up

評量方式與標準 1. Quizzes and assignments: 30% 2. Mid-term exam: 20% 3. Paper reading & presentation: 20% 4. Final-term written report: 30%

使用教材、參考書目或其他 【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】

使用外文原文書：是
1. Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to time series analysis and forecasting. John Wiley & Sons. 2. Montgomery, D. C. (2020). Introduction to statistical quality control (8th ed.). Wiley. 3. Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: principles and practice (3rd ed). OTexts. 4. https://www.cs.ucr.edu/~eamonn/MatrixProfile.html

課程諮詢管道 Email, office hour

課程對應SDGs指標 SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）

課程是否導入AI

備註

教師姓名	張漢利　教師諮商時間(Office Hours)
Email	hendri@mail.ntut.edu.tw
最後更新時間	2025-02-17 17:18:46
課程大綱	This course provides a comprehensive understanding of time series analysis and process control techniques, with an emphasis on coding and conducting experiments, such as verifying results from research papers. Students will learn to analyze time-dependent data and apply various forecasting and process monitoring methods. Key topics include time series preprocessing, regression analysis, ARIMA models, and advanced techniques such as statistical process control, CUSUM, EWMA, and multivariate monitoring. The course also covers real-time contrasts and includes paper reading and presentations to explore contemporary applications in time series analysis and process control. Basic programming skills is expected.
課程進度	Week 1: Course Orientation Week 2: Time series data preprocessing Week 3: Time series visualization & analysis Week 4: Introduction to forecasting Week 5: Regression analysis Week 6: Exponential smoothing Week 7: ARIMA Week 8: Mid term Week 9: Time series data mining (I) Week 10: Time series data mining (II) Week 11: Statistical process control Week 12: CUSUM charts Week 13: EWMA charts Week 14: Multivariate process monitoring Week 15: Real-time contrasts Week 16: Paper reading & presentation Week 17: Paper reading & presentation Week 18: Course wrap up
評量方式與標準	1. Quizzes and assignments: 30% 2. Mid-term exam: 20% 3. Paper reading & presentation: 20% 4. Final-term written report: 30%
使用教材、參考書目或其他	【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】使用外文原文書：是 1. Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to time series analysis and forecasting. John Wiley & Sons. 2. Montgomery, D. C. (2020). Introduction to statistical quality control (8th ed.). Wiley. 3. Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: principles and practice (3rd ed). OTexts. 4. https://www.cs.ucr.edu/~eamonn/MatrixProfile.html
課程諮詢管道	Email, office hour
課程對應SDGs指標	SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
課程是否導入AI
備註