Degree Level Course
Applied Time Series Analysis
This course aims to enable the students to learn and apply statistical methods for the analysis of data that have been observed over time. The course will enable the students with the knowledge of working principles of different time series models and their applications.
Code
BSMS4201
Credits
4 Credits
Type
Core Course
Prerequisites
None
12-Week Roadmap
Course Structure & Syllabus
For details of standard term assessment timelines and exam structures, visit our Academics page.
WEEK 1
Introduction to time series data and analysis, stationarity
WEEK 2
Autocorrelation (AR) and Moving average (MA) model, autocorrelation function and properties
WEEK 3
ARIMA models, identification, estimation, and diagnosis of models
WEEK 4
Seasonal AR integrated MA (ARIMA) models, decomposition
Reading List
Prescribed Books & References
- J D Hamilton, Time Series Analysis, Princeton University Press
- R J Hyndman, G Athanasopoulos, Forecasting: principles and practice, https://otexts.com/fpp2/
- R H Shumway, D S Stoffer, Time Series Analysis and Its Applications: With R Examples, Springer, Fourth Edition