Syllabus
I. Classification of random processes.
II. Decomposition methods: 1. Trend. 2. Seasonality and periodicity. 3. Tests of randomness.
III. Box-Jenkins methodology 1. ARMA models ARMA 2. Identification, estimation, verification and prediction. 3. ARIMA and seasonal models.
IV. Multivariate time series (vector autoregression, Kalman filter).
V. Financial time series: 1. Models of volatility (GARCH). 2. Models nonlinear in mean.
Annotation
Basic methods of time series analysis and their applications, time series decomposition and adaptive techniques, Box-Jenkins methodology including ARIMA and seasonal models, multivariate time series (vector autoregression, Kalman filter), financial time series (models of volatility and nonlinear in mean). Requirements:
Basic knowledge of statistics.