AMS223: Time Series Analysis

*****COURSES ARE SUBJECT TO CHANGE*****

Graduate level introductory course on time series data and models in the time and frequency domains: descriptive time series methods; the periodogram; basic theory of stationary processes; linear filters; spectral analysis; time series analysis for repeated measurements; ARIMA models; introduction to Bayesian spectral analysis; Bayesian learning, forecasting, and smoothing; introduction to Bayesian Dynamic Linear Models (DLMs); DLM mathematical structure; DLMs for trends and seasonal patterns; and autoregression and time series regression models. Prerequisite(s): course 206B, or by permission of instructor. Enrollment restricted to graduate students.

5 Credits

YearFallWinterSpringSummer
2017-18
2016-17
2014-15
2013-14
2011-12
2009-10
2007-08
2005-06
2003-04
2002-03

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