Modern methods of modeling and forecasting time series. The principal topic is the Box-Jenkins method of using autoregressive and moving average models, including non-seasonal and seasonal models, transformations to achieve stationarity, model identification by analysis of the sample autocorrelation and partial autocorrelation functions, criteria for model selection, and applications in R.