STAT 223Time Series Analysis
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. (Formerly AMS 223.)
Prerequisite(s): STAT 206B, or by permission of instructor. Enrollment is restricted to graduate students.
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When it runs
Winter 2027Open Winter 2027
| Section | Type | When | Where | Instructor | Seats |
|---|---|---|---|---|---|
| 01 | Lecture | Asynchronous | — | Staff | Open 0/10 |
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