STAT 460 - Time Series Analysis

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, 481, 482, 541, 581, or 786

Note
Dual list STAT 560

Credits: 3

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