Time Series Analysis 2024
The course aims to teach you the basic knowledge of time series analysis with several hands-on sessions. On the first day, you will learn about stochastic models and on the second day, we will deal with deterministic effects (trends and periodic patterns).
Day 1: Stochastic Models
- Autoregressive models
- Markov Chains
- Short Introduction
- Application to wind power time series
- Discussion: The problem of correlations
- Kramers Moyal expansion
- Not-so-short introduction
- Application to power grid frequency time series: estimation of essential power system parameters such as the effective primary control.
- If time allows: Application to a Geophysical Time Series.
Day 2: Deterministic Patterns
- Intro: When is a trend significant?
- Application to annual wind speed time series
- Periodic patterns
- Intro: The Fourier transformation
- Application to a sample time series.
- Discussion Limitations of Fourier analysis for short and noisy time series.
- Alternatives to Fourier analysis for short time series such as SSA or multi-taper methods
Requirements: For the hands-on session, please bring your laptop.