Time Series Analysis

Autumn semester 2016

General information

Lecturer Nicolai Meinshausen
Lectures Wed 9-10 (HG D 7.1); Thu 10-12 (HG D 1.1)
Course catalogue data >>

Course content

Statistical analysis and modeling of observations in temporal order, which exhibit dependence. Stationarity, trend estimation, seasonal decomposition, autocorrelations, spectral and wavelet analysis, ARIMA-, GARCH- and state space models. Implementations in the software R.


  • September 1st, 2016:
    Beginning of lecture: Wednesday, 20th September.

Course materials

Week Topic
Week 1 Characteristics of time-series: stationarity, auto-correlation function, examples
Week 2 Characteristics of time-series: auto-correlation function and estimation
Week 3 Characteristics of time-series: transformations and trend estimation
Week 4 Time-domain models
Week 5 Invertible moving averages and ARMA models
Week 6 Linear forecasting and partial autocorrelations
Week 7 Inference for ARMA models
Week 8 Spectral methods
Week 9 State-space models
Week 10 State-space models