st5209-2024

KO | EN

gitlite — search

#python #java #python3 #arduino #golang #machine-learning #rust #html #flask #javascript #seismology #nodejs

★ 24 Open GitHub ↗

Course repo for NUS ST5209/X in Semester II 2023/2024

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# NUS ST5209/X in Semester II 2023/2024

## Instructor
**Name**: Tan Yan Shuo

**Email**: yanshuo@nus.edu.sg

**Office**: S16 06-112

## Course Description
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.

## Course Format and Meeting Times
This course will have a [blended learning](https://cit.nus.edu.sg/blended-learning-2-0-2/) 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, LT28

**Section 2**: Tuesdays 7-8:30pm, LT28

## Course Outline
1. Introduction to Time Series Analysis
    - Definition and characteristics of time series data
    - Time series components: trend, seasonality, and noise
    - Time series visualization and exploratory data analysis

2. Time Series Decomposition
    - Trend estimation and removal
    - Seasonal adjustment techniques
    - Residual analysis

3. Time Series Forecasting and Modeling
    - Exponential Smoothing
    - Autoregressive Integrated Moving Average (ARIMA) models
    - Seasonal ARIMA (SARIMA) models

4. Model Evaluation and Selection
    - Model diagnostics and residual analysis
    - Model selection criteria: AIC, BIC, and cross-validation
    - Forecast accuracy measures

5. Advanced Topics in Time Series Analysis
    - State space models
    - Nonlinear time series models
    - Multivariate time series analysis
    - Time series regression models

## Assessment
- Assignments: 40%
- Midterm Exam: 25%
- Final Exam: 35%

## Assignment Policy
- There will be assignments due once every two weeks
- Assignments are due 23.59 on Mondays two weeks after they are uploaded
- No late sumbmissions will be accepted
- The lowest scoring assignment will be dropped

## Midterm Exam
- The midterm exam will be take home and open book. It will be released at noon on 8 March 2024 (Friday) and due at noon on 15 March 2024 (Friday).

## Final Exam
- The final exam will take place on 30 April 2024, 5-7pm at MPSH 6. It will be closed book, but you are allowed one doubled-sided A4-sized help sheet.

## Prerequisites
- Basic knowledge of statistics, probability, and linear algebra
- Familiarity with R and Python programming

## References
- Class notes/textbook: https://yanshuo.quarto.pub/nus-ts-book
- 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.

st5209-2024

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README