Time Series Analysis

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).

Content

Day 1: Stochastic Models

1. Autoregressive models
2. Markov Chains
   1. Short Introduction
   2. Application to wind power time series
   3. Discussion: The problem of correlations 
3. Kramers Moyal expansion
   1. Not-so-short introduction
   2. Application to power grid frequency time series: estimation of essential power system parameters such as the effective primary control.
   3. If time allows: Application to a Geophysical Time Series.

Day 2: Deterministic Patterns

1. Trends
   1. Intro: When is a trend significant?
   2. Application to annual wind speed time series
   3. Discussion
2. Periodic patterns
   1. Intro: The Fourier transformation
   2. Application to a sample time series.
   3. Discussion Limitations of Fourier analysis for short and noisy time series. 
3. 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.