Time Series Analysis (C)

Catalog description

Statistical methods for analyzing data collected sequentially in time where successive observations are dependent. Includes smoothing techniques, decomposition, trends and seasonal variation, forecasting methods, models for time series: stationarity, autocorrelation, linear filters, ARMA processes, non-stationary processes, model building, forecast errors and confidence intervals. Prerequisites and Corequisites Prerequisites: STAT 441, STAT 481 , 482 or MATH 382, 481 Note (C) Denotes common course Dual list STAT 560