732A62 Time Series Analysis
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
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 modelsRead: ?
Lecture 7: Spectral analysis and filtering
Lecture 8: GARCH models and transfer function models
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Last updated: 2017-08-29