401-4623-00L Time Series Analysis
| Semester | Autumn Semester 2019 |
| Lecturers | N. Meinshausen |
| Periodicity | two-yearly recurring course |
| Course | Does not take place this semester. |
| Language of instruction | English |
| Abstract | 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. |
| Learning objective | Understanding of the basic models and techniques used in time series analysis and their implementation in the statistical software R. |
| Content | This course deals with modeling and analysis of variables which change randomly in time. Their essential feature is the dependence between successive observations. Applications occur in geophysics, engineering, economics and finance. Topics covered: Stationarity, trend estimation, seasonal decomposition, autocorrelations, spectral and wavelet analysis, ARIMA-, GARCH- and state space models. The models and techniques are illustrated using the statistical software R. |
| Lecture notes | Not available |
| Literature | A list of references will be distributed during the course. |
| Prerequisites / Notice | Basic knowledge in probability and statistics |