Instructor: Raquel Prado

Instructor Office Hours: Tu 1:50-2:50pm and Fr 8:30-9:30am in BE-365C

Class Time: Tu-Th 11:40am-1:15pm  

Classroom: BE 169

General Description:  This is a graduate level course on time series analysis. Frequency and time domain approaches for the analysis of time series will be considered, with emphasis on the later.  Some of the topics that will be covered include: descriptive time series methods; basic theory of stationary processes; ARIMA models; Bayesian learning, forecasting and smoothing; Bayesian Dynamic Linear Models (DLMs); time-varying autoregressions, time series regression models, spectral analysis, MCMC and sequential Monte Carlo for general dynamic models, mixture models and selected topics on multivariate time series (if time permits). 


  • Textbook: Prado and West (2010). Time Series: Modeling, Computation, and Inference. Chapman & Hall CRC Press. 

  • West M. and Harrison J. (1997). Bayesian Forecasting and Dynamic Models. Springer-Verlag. Second Edition. Highly recommended and available online at the UCSC library.

  • Shumway R. and Stoffer D. (2011). Time Series Analysis and Its Applications with R examples. Springer Texts in Statistics. Recommended and available at the UCSC library.

  • Petris G., Petrone S. and Campagnoli P. (2009) Dynamic Linear Models with R. Springer-Verlag. Recommended and available at the UCSC Library. 

Pre-requisites: AMS-205, AMS-205B, AMS-206, AMS-206B, AMS-207 or similar courses. Students are expected to be familiar with R, Matlab or some programming language for solving some of the homework problems and/or class projects. No formal R/Matlab instruction will be provided.