ISOM 4540
Time Series Analysis and Forecasting
Instructor
Inchi Hu, Professor of ISOM, Room 4436, Tel: 2358-7734
Office hours: By appointment
Email: imichu@ust.hk;
Tutorials
The weekly tutorial will go over assignments and supplementary
materials. The tutorials will begin in the second week.
Tutors: Ms. Jenny Choi
Email: kailam@ust.hk
Ms. Mengmeng AO Email: maoaa@ust.hk
Homepage: http://lmes2.ust.hk
All lecture notes, assignments, solutions, old exam papers, and
important announcements will be posted on the homepage
Course Objective
The objective of this course is to equip students with various
forecasting techniques and knowledge on modern statistical methods
for analyzing time series data. The course consists of three parts: I.
Univariate methods; II. Regression methods; III. ARIMA models.
Intended Learning Outcomes
Upon completion of the course, you should be able to
 Understand the fundamental advantage and necessity of
forecasting in various situations.
 Know how to choose an appropriate forecasting method in a
particular environment.
 Know how to apply various forecasting methods, which
includes obtaining the relevant data and carrying out the necessary
computation (running suitable statistical software, if necessary).
 Improve forecast with better statistical models based on statistical
analysis
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Teaching Method
The method is lecturing aided by directed discussion. The context of
the relevant concepts and methods will be presented first followed by
the discussion of pre-designed questions and examples to explore the
concepts and methods in depth.
Course Materials:
 “Forecasting and Time Series”, 4th Edition, by Bowerman and
O’Connell, Duxbury
 Additional materials (lecture notes and business cases) will be
available from course website.
Assessment scheme
Your grade is based on the following components:
 The 1st exam (40%) covers Parts I & II; an in-class, closed-book,
written exam of approximately 90 minutes long.
 The 2nd exam (40%) covers Part III; similar format to 1st exam.
 Assignments (15%) will be given every one to two weeks.
There are two types of assignments: individual-based and group
projects, where students work in groups. All assignments will be
collected and returned by your TA during tutorial sessions.
 Participation (5%) is crucial to a lively and effective learning
environment. Most students can get 3% by attending the class
regularly without disturbing behaviors. For those students who
actively participate classroom discussions and raising and
answering questions that help the whole class to learn better, a full
5% will be awarded.
In exceptional situations, when a student shows continuous and
significant improvements, the instructor reserves the right to put
more weight on the 2nd exam than the 1st exam.
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Schedule
Part I: Univariate methods
 Feb. 2, Setting the stage
 Feb. 7, Simple smoothing methods
 Feb. 9, Decomposition methods [HW1]
 Feb. 14, Holt’s and Winters’ methods
Part II: Regression method
 Feb. 16, Review simple linear regression [HW2; Group]
 Feb. 21, correlation analysis & multiple regression
 Feb. 23, Marginal sums of squares
 Feb. 28, Use of variables in regression
 Mar 1, meaning of regression coefficients [HW3]
 Mar 6, Variable selection techniques
 Mar 8, Model selection in regression [HW4; Group]
 Mar 13, Residual analysis [Read Appendix B for matrix
algebra of regression analysis]
 Mar 15, Detecting outliers in regression
 Mar 20, Autocorrelated errors in regression
☆ Thur. Mar. 22, Review for the 1st Examination
☆ Tue. Mar. 27, the 1st Examination
Part III: Box-Jenkins method
 Mar 29 Introduction to Box-Jenkins method
 Apr. 10, 2nd order stationary processes
 Apr. 12, Identification of ARMA models. [HW5]
 Apr. 17, Parameter estimation in ARMA models
 Apr. 19, Diagnostic checking in ARMA models
 Apr 24, Forecast using ARMA models
 Apr 26, ARIMA model and forecasting [HW6; Group]
 May 3, Seasonal ARIMA models
 May 8, Seasonal ARIMA model building [HW7; Group]
☆ Thu. May 10, the 2nd examination
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Academic Integrity
Without academic integrity, there is no serious learning. Thus you are
expected to hold the highest standard of academic integrity in the
course. You are encouraged to study and do the project in groups.
However, no cheating and plagiarism will be tolerated. Anyone caught
cheating or plagiarism will fail the course. Please make sure you
adhere to the HKUST Academic Honor Code at all time (see
http://www.ust.hk/vpaao/integrity/).
Learning Environment
Maintaining an exciting learning environment is the responsibility of
both the teacher and the students. I expect students to behave
responsibly during class without disturbing your classmates. You are
encouraged to raise and answer questions in classroom. Particularly
welcomed are active participation in the classroom discussion and
sharing your thoughts that enhance the learning.
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