SDST4601 Time-series analysis (6 credits) Academic Year 2025
Offering Department SCDS (Department of Statistics and Actuarial Science) Quota ---
Course Co-ordinator Prof G Li, SCDS (Department of Statistics and Actuarial Science) < gdli@hku.hk >
Teachers Involved (Prof G Li,Statistics & Actuarial Science)
Course Objectives A time series consists of a set of observations on a random variable taken over time.  Time series arise naturally in climatology, economics, environment studies, finance and many other disciplines.  The observations in a time series are usually correlated; the course establishes a framework to discuss this.  This course distinguishes different type of time series, investigates various representations for the processes and studies the relative merits of different forecasting procedures.  Students will analyse real time-series data on the computer.
Course Contents & Topics Stationarity and the autocorrelation functions; linear stationary models; linear non-stationary modes; model identification; estimation and diagnostic checking; seasonal models and forecasting methods for time series.
Course Learning Outcomes
On successful completion of this course, students should be able to:

CLO 1 recognize a stationary vs non-stationary time series
CLO 2 understand some basic properties of commonly used time series models such as AR (autoregressive), MA (moving average) and ARMA models
CLO 3 transform non-stationary time series into stationary ones
CLO 4 identify different time series models based on autocorrelation functions
CLO 5 fit a suitable AR, MA or ARMA model to real data using SAS (after transforming to stationarity if necessary)
CLO 6 perform goodness of fit tests for such models
CLO 7 do forecasting with these fitted time series models
Pre-requisites
(and Co-requisites and
Impermissible combinations)		 Pass in SDST3600; and
Not for students who have passed in SDST3614, or have already enrolled in this course; and
Not for students who have passed in SDST3907, or have already enrolled in this course.
Only for students admitted in 2025 and thereafter.
Course Status with Related Major/Minor /Professional Core 2U000C00 Course not offered under any Major/Minor/Professional core
2024 Bachelor of Arts and Sciences in Applied Artificial Intelligence ( Disciplinary Elective )
2024 Major in Decision Analytics ( Disciplinary Elective )
2024 Major in Risk Management ( Disciplinary Elective )
2024 Major in Statistics ( Disciplinary Elective )
2024 Minor in Risk Management ( Disciplinary Elective )
2024 Minor in Statistics ( Disciplinary Elective )
2023 Bachelor of Arts and Sciences in Applied Artificial Intelligence ( Disciplinary Elective )
2023 Major in Decision Analytics ( Disciplinary Elective )
2023 Major in Risk Management ( Disciplinary Elective )
2023 Major in Statistics ( Disciplinary Elective )
2023 Minor in Risk Management ( Disciplinary Elective )
2023 Minor in Statistics ( Disciplinary Elective )
2022 Bachelor of Arts and Sciences in Applied Artificial Intelligence ( Disciplinary Elective )
2022 Major in Decision Analytics ( Disciplinary Elective )
2022 Major in Risk Management ( Disciplinary Elective )
2022 Major in Statistics ( Disciplinary Elective )
2022 Minor in Risk Management ( Disciplinary Elective )
2022 Minor in Statistics ( Disciplinary Elective )
2021 Bachelor of Arts and Sciences in Applied Artificial Intelligence ( Disciplinary Elective )
2021 Major in Decision Analytics ( Disciplinary Elective )
2021 Major in Risk Management ( Disciplinary Elective )
2021 Major in Statistics ( Disciplinary Elective )
2021 Minor in Risk Management ( Disciplinary Elective )
2021 Minor in Statistics ( Disciplinary Elective )
Course to PLO Mapping 2024 Bachelor of Arts and Sciences in Applied Artificial Intelligence < PLO 2,3,4 >
2024 Major in Decision Analytics < PLO 1,2,3,5 >
2024 Major in Risk Management < PLO 2,3,4,5 >
2024 Major in Statistics < PLO 1,2,3,4,5,6 >
2023 Bachelor of Arts and Sciences in Applied Artificial Intelligence < PLO 2,3,4 >
2023 Major in Decision Analytics < PLO 1,2,3,5 >
2023 Major in Risk Management < PLO 2,3,4,5 >
2023 Major in Statistics < PLO 1,2,3,4,5,6 >
2022 Bachelor of Arts and Sciences in Applied Artificial Intelligence < PLO 2,3,4 >
2022 Major in Decision Analytics < PLO 1,2,3,5 >
2022 Major in Risk Management < PLO 2,3,4,5 >
2022 Major in Statistics < PLO 1,2,3,4,5,6 >
2021 Bachelor of Arts and Sciences in Applied Artificial Intelligence < PLO 2,3,4 >
2021 Major in Decision Analytics < PLO 1,2,3,5 >
2021 Major in Risk Management < PLO 2,3,4,5 >
2021 Major in Statistics < PLO 1,2,3,4,5,6 >
Offer in 2025 - 2026 Y        1st sem    Examination Dec     
Offer in 2026 - 2027 Y
Course Grade A+ to F
Grade Descriptors
A Demonstrate thorough mastery at an advanced level of extensive knowledge and skills required for attaining all the course learning outcomes. Show strong analytical and critical abilities and logical thinking, with evidence of original thought, and ability to apply knowledge to a wide range of complex, familiar and unfamiliar situations. Apply highly effective organizational and presentational skills.
B Demonstrate substantial command of a broad range of knowledge and skills required for attaining at least most of the course learning outcomes. Show evidence of analytical and critical abilities and logical thinking, and ability to apply knowledge to familiar and some unfamiliar situations. Apply effective organizational and presentational skills.
C Demonstrate general but incomplete command of knowledge and skills required for attaining most of the course learning outcomes. Show evidence of some analytical and critical abilities and logical thinking, and ability to apply knowledge to most familiar situations. Apply moderately effective organizational and presentational skills.
D Demonstrate partial but limited command of knowledge and skills required for attaining some of the course learning outcomes. Show evidence of some coherent and logical thinking, but with limited analytical and critical abilities. Show limited ability to apply knowledge to solve problems. Apply limited or barely effective organizational and presentational skills.
Fail Demonstrate little or no evidence of command of knowledge and skills required for attaining the course learning outcomes. Lack of analytical and critical abilities, logical and coherent thinking. Show very little or no ability to apply knowledge to solve problems. Organization and presentational skills are minimally effective or ineffective.
Communication-intensive Course N
Course Type Lecture-based course
Course Teaching
& Learning Activities
Activities Details No. of Hours
Lectures 36.0
Tutorials 12.0
Reading / Self study 100.0
Assessment Methods
and Weighting
Methods Details Weighting in final
course grade (%)		 Assessment Methods
to CLO Mapping
Assignments Coursework (assignments, tutorials, and a class test) 40.0 1,2,3,4,5,6,7
Examination One 2-hour written examination 60.0 1,2,3,4,6,7
Required/recommended reading
and online materials		 J. D. Cryer & K.S. Chan: Time Series Analysis with Applications in R (Springer, 2008, 2nd edition)
Bovas Abraham & Johannes Ledolter: Statistical Methods for Forecasting (John Wiley & Sons, 2005, 2nd edition)
W. W .S. Wei: Time Series Analysis: Univariate and Multivariate Methods (Addison-Wesley, 2006, 2nd edition)
W. K. Li: Diagnostic Checks in Time Series (Chapman & Hall/CRC, 2004)
Howell Tong: Non-linear Time Series: A Dynamical System Approach (Oxford University Press, 1990)
Course Website http://moodle.hku.hk
Additional Course Information NIL