The mbsts package: Multivariate Bayesian Structural Time Series Models in R
It provides a tool for researchers and practitioners in fields like economics or finance needing time series analysis, but it is incremental as it packages existing methodology.
The paper introduces the mbsts R package for multivariate Bayesian structural time series modeling, enabling inference and prediction for multiple correlated time series with flexible predictor selection.
The multivariate Bayesian structural time series (MBSTS) model is a general machine learning model that deals with inference and prediction for multiple correlated time series, where one also has the choice of using a different candidate pool of contemporaneous predictors for each target series. The MBSTS model has wide applications and is ideal for feature selection, time series forecasting, nowcasting, inferring causal impact, and others. This paper demonstrates how to use the R package mbsts for MBSTS modeling, establishing a bridge between user-friendly and developer-friendly functions in the package and the corresponding methodology. Object-oriented functions in the package are explained in the way that enables users to flexibly add or deduct some components, as well as to simplify or complicate some settings.