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732A62 Time Series Analysis

Course information

Course description

The course introduces the main concepts and tools in time series analysis. The course is given for master students in Statistics and Data Mining / Statistics and Machine learning and as a single subject course.

The course is divided into topics, where each topic includes lectures, a computer lab and/or a teaching session and a follow-up seminar. All lecture slides and lab assignments can be found at LISAM.




Course literature

- Main course book (SS): 'Time Series Analysis and its applications' by Shumway and Stoffer. Fourth Edition (2017). ISBN 978-3-319-52451-1. Can be bought, for example, here.
- Secondary book (CC): 'Time Series analysis' by Cryer and Chan. Second Edition (2008). ISBN 9878-0-387-75958-6


Preliminary schedule

Topic 1: Introduction. Exploratory analysis and Time Series Regression. Introduction to ARIMA.

Read: SS, ch. 1.1-1.5, 2.1-2.3, 3.1 alternatively CC, ch. 1-5.
Lecture 1: Introduction. Slides.
Lecture 2:Exploratory analysis and Time Series regression. Slides.
Lecture 3: Introduction to ARIMA models.



Topic 2: ARIMA models.

Read: SS, ch.3.2-3.9 alternatively CC, ch. 6-10.
Lecture 4: ARIMA, part 1: Difference equations, PACF, Forecasting.
Lecture 5: ARIMA, part 2: Estimation, non-stationary data.
Lecture 6: ARIMA, part 3: Model specification, seasonal models.



Topic 3: Frequency domain models and advanced time domain models

Read: ?
Lecture 7: Spectral analysis and filtering
Lecture 8: GARCH models and transfer function models




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Last updated: 2017-08-29