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Time Series Analysis and Machine Learning

Course code
Course type
BSc Course
Weekly Hours
FS 2024
Prof. Dr. Michael Massmann
Please note that exchange students obtain a higher number of credits in the BSc-program at WHU than listed here. For further information please contact directly the International Relations Office.
This course is centered around the problem of predicting time series. Applications are taken from finance, macroeconomics and the earth sciences. Classical methods for time series modelling such as autoregressions are introduced. This is followed by advanced models for the analysis of non-stationary systems, namely unit root and GARCH models. Finally, machine learning techniques are covered to cater for present-day massive datasets. The empirical analyses are implemented in RStudio, the most popular data science software environment, and in RMarkdown, the prime language for producing replicable research.
Date Time
Monday, 19.02.2024 11:30 - 15:15
Monday, 04.03.2024 11:30 - 15:15
Monday, 11.03.2024 11:30 - 15:15
Monday, 18.03.2024 11:30 - 15:15
Monday, 08.04.2024 11:30 - 15:15
Friday, 19.04.2024 09:45 - 17:00
By the end of the course, students will be familiar with modern econometric and machine learning techniques and will be able to apply them to real world data sets using state-of-the-art software. Students will have acquired a sound theoretical mindset for causal inference and time series prediction. They will have developed programming skills for conducting replicable empirical work. This proficiency will prove indispensable for their Bachelor's thesis, for a Master's degree or for data science projects in industry.
Stock and Watson (2019): Introduction to Econometrics. Pearson. 4th edition.
The course takes the form of interactive lectures with exercises: on the one hand, theoretical material is discussed in class and illustrated by means of empirical examples in live demonstrations in RStudio; on the other hand, participants are given theoretical and coding exercises to practice the use of newly-learned concepts.
seminar paper and presentation (50%), written examination (50%)
Familiarity with the topics covered in Mathematics I and II, as well as those in Statistics I and II is assumed.
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