Time Series
In the winter term we’ll offer the lecture “Time Series” (6 ECTS).
General
This course provides a comprehensive introduction to time series analysis, emphasizing both classical statistical techniques and modern machine learning methods. Students will learn to model, analyze, and forecast time series data using various approaches, equipping them with practical skills to tackle real-world time series problems.
Course Objectives:
- Understand the fundamental concepts of time series analysis.
- Apply classical statistical methods to time series data.
- Utilize machine learning techniques for time series forecasting.
- Implement and evaluate time series models.
- Interpret the results and make informed decisions based on time series analysis.
Prerequisites:
- Basic knowledge of statistics and probability.
- Familiarity with linear algebra and calculus.
- Basic Machine Learning knowledge.
- Proficiency in a programming language (preferably Python).
Organisation
Credits: 6 ECTS
Language: English
Lecture: Tuesdays, 12noon - 2pm c.t., Lecture Hall TTR2, (Maria-von-Linden Str 6, ground floor)
Exercise Session: Tuesdays, 2pm - 4pm (starting in the second week), Lecture Hall TTR2
more information will be available on Moodle.
Topics to be covered (preliminary)
- Autoregression & Dependence
- Stationarity & Transformations
- Spectral/ Frequency Domain
- State Space Models
- Latent Space Models
- Probabilistic Models
- Conditional Density Estimates