Name: Tan Yan Shuo
Email: [email protected]
Office: S16 06-112
This course provides an introduction to the analysis of time series data. Time series data is encountered in various fields such as finance, economics, engineering, and environmental sciences. The course covers fundamental concepts, techniques, and models for analyzing and forecasting time series data.
This course will have a blended learning format, which means that half the weekly lecture content will be delivered via online lecture videos. Students are expected to have viewed and understood the lecture video before coming for in-person class.
Section 1: Thursdays 7-8:30pm, LT34
Section 2: Tuesdays 7-8:30pm, LT34
- Getting started with time series data
- Data wrangling
- Visualizations
- Transformations
- Time series decomposition
- Summary statistics
- Introduction to forecasting
- Exponential smoothing
- Statistical modeling of time series data
- Stationary processes
- Autoregressive models
- ARMA models
- ARIMA models
- State space models
- Advanced topics
- Machine learning models
- Other selected topics
- Quizzes: 20%
- Assignments: 40%
- Final Exam: 40%
- There will be a Canvas quiz due once a week.
- You will have 3 tries for each quiz and you will be awarded the maximum of your scores.
- Quizzes are due 7:00pm on Tuesday.
- The quiz covers prerecorded lecture video content and will help prepare students for in-person class.
- No late sumbmissions will be accepted.
- The two lowest scoring quizzes will be dropped.
- There will be an assignment due once every two weeks.
- Assignments are due 11:59pm on Mondays two weeks after they are uploaded.
- No late sumbmissions will be accepted.
- The lowest scoring assignment will be dropped.
- In-person. Details to be announced.
- We will use Ed Discussion to manage to course Q&A.
- All students should have been invited to the course discussion page.
- Please submit all your questions via this platform instead of emailing the course staff.
- This repo will contain all materials for in-person class and assignments.
- The Canvas site will be used for course announcements and for submitting assignments and quizzes.
- We will be closely following https://yanshuo.quarto.pub/nus-ts-book.
- Teaching assistant: Zhuang Tianyi
- Video support: Shielda Kamilia Hidajat
- Hyndman, R.J., & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia. OTexts.com/fpp3
- Shumway, R. H., & Stoffer, D. S. (2017). Time Series Analysis and Its Applications (4th ed.). Springer.
- Basic knowledge of statistics, probability, and linear algebra.
- Familiarity with R and Python programming.