Time Series Analysis and Machine Learning
Kurs ID
QUANT312
Art des Kurses
BSc Kurs
Wochenstunden
2,0
ECTS
6.0
Semester
FS 2024
Vortragssprache
Englisch
Vortragende/r
Prof. Dr. Michael Massmann
Bitte beachten Sie, dass AustauschstudentInnen im BSc-Programm der WHU eine höhere Anzahl an Credits erwerben als hier aufgeführt. Für weitere Informationen wenden Sie sich bitte direkt an das [International Relations Office].
Kursinhalt
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.
Termine
| 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 |
Lernerfolge
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.
Literatur
Stock and Watson (2019): Introduction to Econometrics. Pearson. 4th edition.
Lernmethoden
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.
Art der Prüfung
seminar paper and presentation (50%), written examination (50%)
Voraussetzungen
Familiarity with the topics covered in Mathematics I and II, as well as those in Statistics I and II is assumed.
Umfang
180 hrs