Opinto-oppaat
2018–2019
Arkistoitu opetussuunnitelma 2017–2019
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MTTTS18
Time Series Analysis 1
5
op
Organised by
Degree Programme in Mathematics and Statistics
Corresponding course units in the curriculum
Learning outcomes
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
Belongs to following study modules
Luonnontieteiden tiedekunta
2018–2019
Archived Teaching Schedule. Please refer to current Teaching Shedule.
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Luonnontieteiden tiedekunta