After the course, the student will be able to characterize the basic properties of a time series and decompose it into a trend, seasonal component and noise. He will also be able to identify and diagnose linear time series models, estimate their parameters and use them in forecasting. Further, he will be able to use the periodogram to detect possible periodic components in the series.
Contents
Simple time series models, stationary time series models (ARMA), nonstationary and seasonal time series models (SARIMA), time series regression, periodogram.
Modes of study
Option
1
Available for:
Degree Programme Students
Other Students
Open University Students
Doctoral Students
Exchange Students
Participation in course work
In
English
Option
2
Available for:
Degree Programme Students
Other Students
Open University Students
Doctoral Students
Exchange Students
Written exam
In
English
Evaluation
Numeric 1-5.
Study materials
Brockwell, Davis: Introduction to Time Series and Forecasting, Springer, 2nd ed, 2010