401-4623-00L Time Series Analysis
|Semester||Autumn Semester 2016|
|Periodicity||two-yearly recurring course|
|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.
|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|