Applied Time Series Analysis with Python
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- 46 Students Enrolled
Applied Time Series Analysis with Python
Applied Time Series Analysis with Python
-
- 0 Rating
- 0 Reviews
- 46 Students Enrolled
Requirements
- Probability theory and statistics, as well as an introductory econometrics course would be very useful. Prior experiences with computer programming are helpful but not mandatory.
General Overview
Description
This course observes classical time series analysis methods of ARIMA models, state-space models, the text includes modern developments including categorical time series analysis, multivariate spectral methods, multivariate and financial time series related models like GARCH the course also includes modern developments including ARMAX models, stochastic volatility, State Space Models and Markov switching models as well as introduction to machine learning. The course focuses on implementation of all methodological concepts in python with help of PythonTsa package.
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Meet the instructors !
Lecturer and scientific employee at School of Computing, Communication and Business|University of Applied Sciences for Engineering and Economics (HTW Berlin)
Management Committee member of COST "Fintech and AI in finance" fin-ai.com
Member of Blockchain Research Center blockchain-research-center.com
Alla holds PhD degree in Statistics from the Humboldt University of Berlin. She served as coordinator of FinTech HO2020 project in Germany. Her research interest cover portfolio allocation strategies and risk management for alternative assets, cryptocurrencies, data science for finance, high frequency financial time series analysis.
Teaching Assitant at Chair of Econometrics | Technical University of Berlin
Patrick is currently a Master's student in Statistics at the Humboldt University of Berlin. His research interest covers high-dimensional nonstationary time series, volatility modeling, and options theory for alternative assets and cryptocurrencies.