JSM 2015 Online Program

Online Program Home
My Program

Legend: Washington State Convention Center = CC, Sheraton Seattle = S, Grand Hyatt = GH and The Conference Center = TCC
* = applied session       ! = JSM meeting theme

Activity Details

CE_18C Mon, 8/10/2015, 1:00 PM - 5:00 PM S-Grand Ballroom A
Bayesian Structural Time Series (ADDED FEE) — Professional Development Continuing Education Course
ASA , Section on Statistical Computing
Structural time series models are a natural, practical, and useful alternative to classic Box-Jenkins ARIMA models. Because stuctural time series are defined through a set of latent variables, they have a natural Bayesian interpretation. This course introduces the basic ideas of structural time series (i.e., decomponsing the model into interpretable components of state) and the fundamental tools for computing with them (mainly the Kalman filter). In the modern Big Data computing environment, it is helpful to think about time series models that contain a regression component, so a target time series can be predicted based on other series whose values are known in advance. Examples of where this technique is useful include causal modeling, economic time series released (and revised) with a lag, and handling calendar effects by including them as regression components with deterministic predictors. The number of potential predictor series can be quite large, so it is natural to consider Bayesian spike and slab priors for introducing model sparsity. All concepts taught in this course have been implemented in the "bsts" R package, which is freely downloadable from CRAN under the GNU public license.
Instructor(s): Steven Scott, Google

For program information, contact the JSM Registration Department or phone (888) 231-3473.

For Professional Development information, contact the Education Department.

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

2015 JSM Online Program Home