Type of Credit: Partially Required
Credit(s)
Number of Students
The objective of this course is to equip students with forecasting techniques and knowledge on statistical methods for analyzing time series data.
Prerequisite Capacities(先備課程): Mathematical Statistics
註：
1. 先備課程「數理統計學」：2月開學前，需修過二學期。
2. 期中考、期末考，僅為暫定之日期，會視課程進度調整時間。
能力項目說明
The topics include ACF and PACF functions, ARIMA processes, best linear prediction, model building and model selection, frequency domain analysis, GARCH and Stochastic Volatility models, VAR models, and other multivariate time series models.
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
|
週次 Week |
課程主題 Topic |
課程內容與指定閱讀 Content and Reading Assignment |
教學活動與作業 Teaching Activities and Homework |
學生學習投入時間 Student workload expectation |
|
|
課堂講授 In-class Hours |
課程前後 Outside-of-class Hours |
||||
|
1 |
(Feb. 20) Introduction |
(S&S, 2016) Chapter 1 |
Lecture |
3 |
2 |
|
2 |
(Feb. 27) ACF and PACF functions |
(S&S, 2016) Chapter 1 |
Lecture |
3 | 2 |
|
3 |
(Mar. 6) ACF and PACF functions; AR(p) |
(S&S, 2016) Chapter 3 |
Lecture HW |
3 |
2 |
|
4 |
(Mar. 13) MA(q) process |
(S&S, 2016) Chapter 3 |
Lecture R lab |
3 |
2 |
|
5 |
(Mar. 20) ARIMA processes |
(S&S, 2016) Chapter 3 |
Lecture |
3 |
2 |
|
6 |
(Mar. 27) SARIMA processes |
(S&S, 2016) Chapter 3 |
Lecture |
3 |
2 |
|
7 |
(Apr. 3) School Holiday |
|
|
|
|
|
8 |
(Apr. 10) Midterm Exam |
(S&S, 2016) Chapter 3 |
|
0 |
7 |
|
9 |
(Apr. 17) model building and model selection |
(S&S, 2016) Chapter 3 and 4 |
R lab and HW |
3 |
3 |
|
10 |
(Apr. 24) model building and model selection: R lab |
(S&S, 2016) Chapter 4 |
Lecture |
3 |
2 |
|
11 |
(May 1) real data analysis: some examples |
|
Lecture |
3 |
2 |
|
12 |
(May 8) frequency domain analysis |
(Tsay, 2012) Chapter 4 |
Lecture R lab |
3 |
2 |
|
13 |
(May 15) ARCH and GARCH models |
(Tsay, 2014) Chapter 2 and 4 |
Lecture HW |
3 | 2 |
|
14 |
(May 22) Stochastic Volatility models and VAR models |
|
3 |
2 | |
|
15 |
(May 29) multivariate time series models |
(Tsay, 2014) Chapter 2 ~ 7 |
Lecture |
3 |
2 |
|
16 |
(June 5) Final Exam |
|
|
0 |
10 |
|
17 |
(June 12) 16+2周，自主統整學習 |
|
|||
|
18 |
(June 19) 16+2周，自主統整學習 |
|
|||
Midterm Exam: 35%
Final Exam: 35%
Homework: 15%
Attendance/Participation: 15%
All the data analysis (homework) will be implemented using software R.
Reference (參考書目):
1. Shumway and Sto ffer. (2016). Time Series Analysis and its Applications with R Examples.
2. Tsay. (2012). An Introduction to Analysis of Financial Data with R.
3. Tsay. (2014). Multivariate Time Series Analysis.
http://moodle.nccu.edu.tw/