Statistics | Time Series Analysis
S650 | 27062 | Jerome Busemeyer


Techniques for analyzing data collected at different points in time.
Probability models, forecasting methods, analysis in both time and
frequency domains, linear systems, state-space models, intervention
analysis, transfer function models and the Kalman filter. Topics also
include: Stationary processes, autocorrelations, partial
autocorrelations, autoregressive, moving average, and ARMA processes,
spectral density of stationary processes, periodograms and estimation
of spectral density. Course is equivalent to MATH M568.