Introduction. Time series plot. Features of time series. Classical trend and seasonal models. Moving
average and Decomposition methods. Holt-Winters forecasting methods. Time series and stochastic
processes. Autocovariance function. Random walk. White Noise. Stationarity. Building models for
stationary time series : general linear process, MA processes. AR processes. Invertibility. Box-Jenkins
methods. ARIMA and SARIMA processes. Models for non-stationary time series. Differencing. Model
specification. Properties of sample acf and pacf. Nonstationarity. AIC criterion. Model Estimation and
diagnostics. Residual analysis. Box-Pierce and Ljung-Box statistics. Forecasting. Using R Statistical
Language for time series analysis
