課程資訊
課程名稱
時序資料分析
Time Series Analytics 
開課學期
109-1 
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
 
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:31人 
 
課程簡介影片
 
核心能力關聯
核心能力與課程規劃關聯圖
課程大綱
為確保您我的權利,請尊重智慧財產權及不得非法影印
課程概述

Time series and signals exist everywhere, and, in particular, the data collection and analysis are much easier than before with the advancement of modern information technology. This course starts by modeling the common time series, such as the demands, economic indicators. Digital signals, such as the machine sensor readings, ECG, and soundwaves, are then analyzed with signal processing techniques. The goal is to develop a general sense of treating 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
probability & statistics, linear algebra, calculus, and programming skills
Course info and communications are all on NTU COOL. 
預期每週課後學習時數
 
Office Hours
 
指定閱讀
待補 
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)
   
課程進度
週次
日期
單元主題
第1週
9/14  Review & Preview 
第2週
9/21  Exponential Smoothing Models 
第3週
9/28  Stationarity vs. Invertibility 
第4週
10/05  Univariate Stationary Time Series Models 
第5週
10/12  Univariate Stationary Time Series Models 
第6週
10/19  Univariate Stationary Time Series Models 
第7週
10/26  Univariate Nonstationary Time Series Models 
第8週
11/02  Model Identification, Estimation, and Diagnostic 
第9週
11/09  Mid-term Exam 
第10週
11/16  Model Identification, Estimation, and Diagnostic 
第11週
11/23  Seasonal Time Series Models 
第12週
11/30  Time Series Forecasting and Multivariate Models 
第13週
12/07  Time Series Modeling Practices in R* 
第14週
12/14  Time-Frequency Analysis  
第15週
12/21  Wavelet Transformation 
第16週
12/28  Recurrent Neural Network 
第17週
1/04  Fina-term Exam 
第18週
1/11  Paper Reading Report Due