*****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
Year | Fall | Winter | Spring | Summer |
2017-18 |
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2016-17 |
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2014-15 |
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2013-14 |
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2011-12 |
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2009-10 |
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2007-08 |
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2005-06 |
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2003-04 |
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2002-03 |
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While the information on this web site is usually the most up to date, in the event of a discrepancy please contact your adviser to confirm which information is correct.