The two main purposes of econometrics are to give empirical content to economic theory. Econometric analysis is divided into time-series analysis and cross-sectional analysis.

Time-series analysis examines variables over time, such as the effects of population growth on a nation's GDP. Cross-sectional analysis examines the relationship between different variables at a point in time; for instance, the relationship between individuals' income and food expenditures. When time-series analysis and cross-sectional analysis are conducted simultaneously on the same sample, it is called panel analysis. We will concentrate mostly on time series analysis. Statistical as well as machine learning methods will be introduced.

The seminar aims at presenting a broad overview of the methods used in econometrics and time series analysis.

Talks can be given in English or German.

Supervisor: Christine Preisach

Topics:

0. -- Introduction --
1. Introduction to Time Series and ARMA Models.
2. Time Series Analysis with Machine Learning Methods.
3. Pattern Detection in Time Series.
4. Clustering and Similarity Measures for Time Series.
5. Indexing Time Series for Search.
6. Time Series Classification.
7. Semi-Supervised Time Series Classification.
8. Prediction of Financial Time Series unsing Nearest Neighbor Classification.
9. Forcasting with Neuronal Networks.
10. Outlier Detection in Time Series

Interested students can register for a topic from now via email to .