課程資訊
課程名稱
時序資料分析
Time Series Analytics 
開課學期
112-1 
授課對象
共同教育中心  統計碩士學位學程  
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。工程與環境統計領域選修課程之一。
總人數上限:24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 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
 
Office Hours
備註: to be announced later 
指定閱讀
 
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)
   
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
其他
由師生雙方議定
課程進度
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