Time Series Analysis
Course Code
AK2283
Number of Credits
3
Semester
Course Type
Study Material
| Study Material | Depth |
|---|---|
| Introduction to time series models and linear regression models | Explore |
| Stationarity | Explore |
| Stationary models | Express |
| Non-stationary models | Express |
| Model identification | Express |
| Parameter estimation uses the moment method and least squares | Expert |
| Parameter estimation uses the maximum likelihood method | Expert |
| Diagnostic tests use residual analysis | Expert |
| Forecast (forecasting) ARIMA models | Expert |
| Prediction limits of ARIMA model forecasts | Expert |
| Seasonal model (seasonal) | Expert |
| Time series model heteroscedasticity ARCH(1) and GARCH(1) (enrichment) | Express |
| Maximum likelihood estimation and diagnostic testing of time series modelsheteroscedasticity(enrichment) | Express |
Graduate Learning Outcomes (GLO) carried by the course
| CPMK Code | Course Learning Outcomes Elements (CLO) |
|---|---|
| CPMK 1 | Have sufficient knowledge and insight into the concept of time series and stationarity assumptions. |
| CPMK 2 | Can identify stationary and non-stationary time series models. |
| CPMK 3 | Able to solve problems related to the application of ARIMA time series models. |
| CPMK 4 | Can interpret forecasts (forecasting) time series model results. |
Learning Method
- Lectures and discussions
Learning Modality
- Mixed, Synchronous/asynchronous, and Independent/Group
Assessment Methods
- Exam, Quiz, Assignment and Lab