The scope of the course is the introduction of concepts and methods of time series analysis, as well as their application to real problems with time series data. Within the framework of application the scope is the use of relevant software.
Course Content (Syllabus)
Basic characteristics of time series: stationarity; autocorrelation; removal of trends and seasonality; independence test of time series. Linear stochastic processes: autoregressive (AR), moving average (MA), autoregressive moving average (ARMA). Time series models: AR, MA and ARMA for stationary time series; autoregressive integrated moving average (ARIMA) models and seasonal ARIMA (SARIMA) for non-stationary time series. Prediction of time series. Spectral analysis of time series.
Nonlinear analysis of time series: Extensions of linear stochastic models; nonlinear characteristics of time series; nonlinear dynamics and chaos; nonlinear prediction of time series.
Additional bibliography for study
1. “The Analysis of Time Series, An Introduction”, Chatfield C., Sixth edition, Chapman & Hall, 2004
2. “Introduction to time series and forecasting”, Brockwell P.J. and Davis R.A., Second edition, Springer, 2002
3. “Non-Linear Time Series, A Dynamical System Approach”, Tong H., Oxford University Press, 1993
4. “Nonlinear Time Series Analysis”, Kantz H. and Schreiber T., Cambridge University Press, 2004