Using bsts In R

Steven L. Scott explains what the bsts package does:

Time series data appear in a surprising number of applications, ranging from business, to the physical and social sciences, to health, medicine, and engineering. Forecasting (e.g. next month’s sales) is common in problems involving time series data, but explanatory models (e.g. finding drivers of sales) are also important. Time series data are having something of a moment in the tech blogs right now, with Facebook announcing their “Prophet” system for time series forecasting (Taylor and Letham 2017), and Google posting about its forecasting system in this blog (Tassone and Rohani 2017).

This post summarizes the bsts R package, a tool for fitting Bayesian structural time series models. These are a widely useful class of time series models, known in various literatures as “structural time series,” “state space models,” “Kalman filter models,” and “dynamic linear models,” among others. Though the models need not be fit using Bayesian methods, they have a Bayesian flavor and the bsts package was built to use Bayesian posterior sampling.

If you’re looking for time series models, this looks like a good one.

Related Posts

Creating Map Plots With ggmap

Laura Ellis shows how to use the ggmap package to create choropleth maps in R: In the last map, it was a bit tricky to see the density of the incidents because all the graphed points were sitting on top of each other.  In this scenario, we are going to make the data all one […]

Read More

R 3.5.0 Released

Tal Galili announces that R 3.5.0 is now available: By default the (arbitrary) signs of the loadings from princomp() are chosen so the first element is non-negative. If –default-packages is not used, then Rscript now checks the environment variable R_SCRIPT_DEFAULT_PACKAGES. If this is set, then it takes precedence over R_DEFAULT_PACKAGES. If default packages are not specified on the command line or by one […]

Read More

Categories

July 2017
MTWTFSS
« Jun Aug »
 12
3456789
10111213141516
17181920212223
24252627282930
31