課程名稱 |
時序資料分析 Time Series Analytics |
開課學期 |
112-1 |
授課對象 |
工學院 工業工程學研究所 |
授課教師 |
藍俊宏 |
課號 |
IE5057 |
課程識別碼 |
546EU4050 |
班次 |
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學分 |
3.0 |
全/半年 |
半年 |
必/選修 |
選修 |
上課時間 |
星期一2,3,4(9:10~12:10) |
上課地點 |
國青101 |
備註 |
本課程以英語授課。 總人數上限:24人 |
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課程簡介影片 |
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核心能力關聯 |
核心能力與課程規劃關聯圖 |
課程大綱
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課程概述 |
Time series and signals are ubiquitous, and with the advancements in modern information technology, data collection and analysis have become more accessible than ever before. This course begins by modeling deterministic and stochastic time series, including demands and economic indicators. It then focuses on analyzing digital signals such as machine sensor readings, ECG, and sound waves using signal processing techniques. The objective is to cultivate a comprehensive understanding of handling temporal signals. |
課程目標 |
Students from this course shall learn to:
1. comprehend the characteristics of different time series and signals;
2. understand the time series identification, estimation, and diagnostic;
3. understand the analytical techniques for digital signal processing;
4. apply proper treatments for analyzing time-series data. |
課程要求 |
Pre-requisites are probability & statistics, linear algebra, calculus, and programming skills.
Evaluation: Homework (25%), Mid-term (30%), Final-term (30%), Project (12%), Participation (3%)
Course details and communications are all on NTU COOL. |
預期每週課後學習時數 |
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Office Hours |
備註: to be announced later |
指定閱讀 |
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參考書目 |
Box, G. E. P., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M. (2016). Time Series Analysis: Forecasting and Control.
Davis, M. H. A., and Vinter, R. B. (1985). Stochastic Modelling and Control.
Tsay, R. (2010). Analysis of Financial Time Series.
Smith, S. W. (1999). The Scientist and Engineer's Guide to Digital Signal Processing.
Lyons, R. G. (2010). Understanding Digital Signal Processing.
Mallat, S. (2008). A Wavelet Tour of Signal Processing. |
評量方式 (僅供參考) |
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針對學生困難提供學生調整方式 |
上課形式 |
以錄影輔助 |
作業繳交方式 |
延長作業繳交期限, 學生與授課老師協議改以其他形式呈現 |
考試形式 |
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其他 |
由師生雙方議定 |
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週次 |
日期 |
單元主題 |
Week 1 |
Sept. 4 |
Review & Preview |
Week 2 |
Sept. 11 |
Exponential Smoothing Models |
Week 3 |
Sept. 18 |
Stationarity vs. Invertibility |
Week 4 |
Sept. 25 |
Univariate Stationary Time Series Models |
Week 5 |
Oct. 2 |
Univariate Stationary Time Series Models |
Week 6 |
Oct. 9 |
Univariate Stationary Time Series Models (Offline Video Learning) |
Week 7 |
Oct. 16 |
Univariate Nonstationary Time Series Models |
Week 8 |
Oct. 23 |
Mid-term Exam |
Week 9 |
Oct. 30 |
Model Identification, Estimation, and Diagnostic |
Week 10 |
Nov. 06 |
Model Identification, Estimation, and Diagnostic |
Week 11 |
Nov. 13 |
Model Identification, Estimation, and Diagnostic |
Week 12 |
Nov. 20 |
Seasonal Time Series Models |
Week 13 |
Nov. 27 |
Time Series Forecasting and Multivariate Models |
Week 14 |
Dec. 4 |
Time-Frequency Analysis |
Week 15 |
Dec. 11 |
Wavelet Transformation |
Week 16 |
Dec. 18 |
Final-term Exam |
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