Time Series

Last updated on Oct 16, 2023

In the winter term we’ll offer the lecture “Time Series” (6 ECTS).

General

The learning goal for the course is to have an understanding of time series analysis techniques and their practical applications, ranging from classical time series analysis to deep learning. Specifically, at the end of the course you should possess the following key knowledge and skills:

  1. Strong foundation in classical time series analysis
  2. Proficiency in model selection and evaluation
  3. Ability to conduct advanced time series modelling and forecasting
  4. Knowledge of practical application of time series analysis (including knowledge of software and tools)

Organisation

Credits: 6 ECTS

Language: English

Lecture: Thursdays, 10am - 12 noon, Hörsaal 21, Kupferbau

Tutorial: Friday, 8am - 10am (starting in the second week), Lecture Hall TTR2 (Maria-von-Linden Str 6, ground floor)

more information will be available on ILIAS soon.

Topics to be covered (preliminary)

  1. Time Series Analysis (Smoothing, lag operators, stationarity, etc.)
  2. Linear Time Series Models (ARIMA, etc.)
  3. Non-Linear Time Series Models (GARCH, etc.)
  4. Multi-Variate Time Series Models (VAR, etc.)
  5. Forecasting, model evaluation and comparison
  6. Filtering and State Space Models
  7. Bayesian Approaches and Proper Scoring Rules
  8. Deep Learning Models
  9. Current Research
overview