Time Series: Forecasting and Statistical Modeling

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 the use of SAS. Includes an introduction to the use of control charts.