1
课程详述
COURSE SPECIFICATION
以下课程信息可能根据实际授课需要或在课程检讨之后产生变动。如对课程有任何疑问，请联
系授课教师。
The course information as follows may be subject to change, either during the session because of unforeseen
circumstances, or following review of the course at the end of the session. Queries about the course should be
directed to the course instructor.
1.
课程名称 Course Title
时间序列分析
Time series analysis
2.
授课院系
Originating Department
数学系
Department of Mathematics
3.
课程编号
Course Code
MA309
4.
课程学分 Credit Value
3
5.
课程类别
Course Type
专业选修课 Major Elective Courses
6.
授课学期
Semester
秋季 Fall
7.
授课语言
Teaching Language
英文 English
8.
授课教师、所属学系、联系方
式（如属团队授课，请列明其
他授课教师）
Instructor(s), Affiliation&
Contact
（For team teaching, please list
all instructors）
蒋学军， 数学系，0755-88018687
Xuejun Jiang, Department of Mathematics
9.
实验员/助教、所属学系、联系
方式
Tutor/TA(s), Contact
To be announced
10.
选课人数限额(可不填)
Maximum Enrolment
（Optional）
80
2
授课方式
Delivery Method
习题/辅导/讨论
Tutorials
实验/实习
Lab/Practical
其它(请具体注明)
Other（Please specify）
总学时
Total
11.
学时数
Credit Hours
48
12.
先修课程、其它学习要求
Pre-requisites or Other
Academic Requirements
概率论与数理统计（MA212）（或数理统计（MA204））
Probability and Statistics (MA212) (or Mathematical Statistics(MA204))
13.
后续课程、其它学习规划
Courses for which this course
is a pre-requisite
金融风险管理， 高等时间序列分析，金融统计，统计机器学习
Financial risk management, Advanced time series, Financial statistics, Statistical machine
learning
14.
其它要求修读本课程的学系
Cross-listing Dept.
教学大纲及教学日历 SYLLABUS
15.
教学目标 Course Objectives
本课程系统地介绍时间序列分析的重要概念（比如平稳性），一些基本的平稳时间序列模型(滑动平均模型，自回归模型，
自回归-滑动平均模型)，趋势及处理非平稳性的一些方法，非平稳时间序列模型(自回归-求和-滑动平均模型，季节模型
等)，参数估计及模型诊断，时间序列模型的预测，异方差时间序列模型，及一些选题介绍-协整、协整检验、因果关系等。
本课程既注重时间序列分析理论的介绍又注重时间序列分析方法的实际应用及使用 R 语言编程实现。
The aim of this course is to present important concepts of time series analysis such as stationarity, stationary time series models
(MA, AR, ARMA models), tends and methods for dealing with non-stationarity, nonstationary time series models (ARIMA models,
SEASONAL MODELS etc.), parametric estimation, model diagnostic, forecasting, heteroscedasticity time series models, and other
selected topics such as Co-integration and Causality, etc.). The course focuses bothe on the theory of linear time series and on the
practical applications with R.
16.
预达学习成果 Learning Outcomes
学生学习完本课程后，能够理解时间序列分析的基本原理和方法、掌握一些常见的时间序列数据建模的技术，能对所建立
的模型进行估计、诊断和预测，初步具备做实证分析的能力.
After completing this course, students will be able to understand some basic principles and methods of time series analysis, to master
some frequent modelling techniques for time series data, to estimate the established model, and to conduct model diagnosis and
forecasting. They are expected to have a preliminary capacity to do empirical analysis.
17.
课程内容及教学日历 （如授课语言以英文为主，则课程内容介绍可以用英文；如团队教学或模块教学，教学日历须注明
主讲人）
Course Contents (in Parts/Chapters/Sections/Weeks. Please notify name of instructor for course section(s), if
this is a team teaching or module course.)
3
CHAPTER 1 FUNDAMENTAL CONCEPTS (4 hours)
1.1 Time Series and Stochastic Processes
1.2 Means, Variances, and Covariances
1.3 Stationarity
1.4 Summary
Exercises
Appendix A: Expectation, Variance, Covariance, and Correlation.
CHAPTER 2 MODELS FOR STATIONARY TIME SERIES (6 hours)
2.1 General Linear Processes
2.2 Moving Average Processes
2.3 Autoregressive Processes
2.4 The Mixed Autoregressive Moving Average Model
2.5 Invertibility
2.6 Summary
Exercises
CHAPTER 3 MODELS FOR NONSTATIONARY TIME SERIES (4 hours)
3.1 Stationarity Through Differencing
3.2 ARIMA Models
3.3 Constant Terms in ARIMA Models
3.4 Other Transformations
3.5 Summary
Exercises
Appendix D: The Backshift Operator
CHAPTER 4 MODEL SPECIFICATION (4 hours)
4.1 Properties of the Sample Autocorrelation Function
4.2 The Partial and Extended Autocorrelation Functions
4.3 Specification of Some Simulated Time Series
4.4 Nonstationarity
4.5 Other Specification Methods
4.6 Specification of Some Actual Time Series
4
4.7 Summary
Exercises
CHAPTER 5 PARAMETER ESTIMATION (4 hours)
5.1 The Method of Moments
5.2 Least Squares Estimation
5.3 Maximum Likelihood and Unconditional Least Squares
5.4 Properties of the Estimates
5.5 Illustrations of Parameter Estimation
5.6 Bootstrapping ARIMA Models
5.7 Summary
Exercises
CHAPTER 6 MODEL DIAGNOSTICS (2 hours)
6.1 Residual Analysis
6.2 Overfitting and Parameter Redundancy
6.3 Summary
Exercises
CHAPTER 7 FORECASTING (4 hours)
7.1 Minimum Mean Square Error Forecasting
7.2 Deterministic Trends
7.3 ARIMA Forecasting
7.4 Prediction Limits
7.5 Forecasting Illustrations
Exercises Appendix E: Conditional Expectation.
Appendix F: Minimum Mean Square Error Prediction
Appendix G: The Truncated Linear Process
Appendix H: State Space Models
CHAPTER 8 SEASONAL MODELS (6 hours)
8.1 Seasonal ARIMA Models
8.2 Multiplicative Seasonal ARMA Models
8.3 Nonstationary Seasonal ARIMA Models
8.4 Model Specification, Fitting, and Checking
8.5 Forecasting Seasonal Models
8.6 Summary
Exercises
CHAPTER 9 TIME SERIES MODELS OF HETEROSCEDASTICITY (6 hours)
9.1 Some Common Features of Financial Time Series
9.2 The ARCH(1) Model
5
9.3 GARCH Models
9.4 Maximum Likelihood Estimation
9.5 Model Diagnostics
9.6 Some Extensions of the GARCH Model
9.7 Another Example: The Daily USD/HKD Exchange Rates
Exercises
Appendix I: Formulas for the Generalized Portmanteau Tests
CHAPTER 10 Selected Topics 1 : Vector autoregressive model and Co-integration; (4 hours)
CHAPTER 11 Selected Topics 2: Causality in time series (4 hours)
Final exam review
18.
教材及其它参考资料 Textbook and Supplementary Readings
[1]. Textbook required: Time Series Analysis With Applications in R, Second Edition. Springer. Author by Jonathan D. Cryer Kung-
Sik Chan
[2]. Reference 1: Analysis of Financial time series by Ruey S, Tsay. Second Edition. Wiley Series
[3]. Reference 2: Time Series Analysi by Hamilton.Second Editon, Princeton University Press. ISBN: 691042896
课程评估 ASSESSMENT
19.
评估形式
Type of
Assessment
评估时间
Time
占考试总成绩百分比
% of final
score
违纪处罚
Penalty
备注
Notes
出勤 Attendance
5%
One point penalized for absence
without leave each time
课堂表现
Class
Performance
小测验
Quiz
课程项目 Projects
20%
Curriculum small paper
平时作业
Assignments
15%
期中考试
Mid-Term Test
30%
期末考试
Final Exam
30
期末报告
Final
Presentation
其它（可根据需要
改写以上评估方
式）
Others (The
6
above may be
modified as
necessary)
20.
记分方式 GRADING SYSTEM
A. 十三级等级制 Letter Grading
B. 二级记分制（通过/不通过） Pass/Fail Grading
课程审批 REVIEW AND APPROVAL
21.
本课程设置已经过以下责任人/委员会审议通过
This Course has been approved by the following person or committee of authority