The mbsts package: Multivariate Bayesian Structural Time Series Models in R

• URL: http://arxiv.org/abs/2106.14045v1
• Date: Sat, 26 Jun 2021 15:28:38 GMT
• Title: The mbsts package: Multivariate Bayesian Structural Time Series Models in R
• Authors: Ning Ning and Jinwen Qiu
• Abstract summary: This paper demonstrates how to use the R package mbsts for MBSTS modeling. The MBSTS model has wide applications and is ideal for feature selection, time series forecasting, nowcasting, inferring causal impact, and others.
• Score: 2.8935588665357086
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The multivariate Bayesian structural time series (MBSTS) model \citep{qiu2018multivariate,Jammalamadaka2019Predicting} as a generalized version of many structural time series models, 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 \pkg{mbsts} for MBSTS modeling, establishing a bridge between user-friendly and developer-friendly functions in package and the corresponding methodology. A simulated dataset and object-oriented functions in the \pkg{mbsts} 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.

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