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.
|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