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.

翻译：多变量贝叶斯结构时间序列(MBSTS) 模型 \ citep{qiu2018多变量, Jammalamadaka2019Prediting} 作为许多结构时间序列模型的通用版本, 处理多个相关时间序列的推论和预测, 其中一个人也可以选择为每个目标序列使用不同候选的同步预测器库。 MBSTS 模型具有广泛的应用性, 并且是地物选择、 时间序列预报、 即时预报、 预测、 推断因果影响等的理想。 本文展示了如何使用 R 包\ pkkg{mbsts} 进行MBSTS 建模, 在软件包和相应方法中建立用户友好和开发者友好功能之间的桥梁 。 模拟数据集和对象导向功能在\pkgn{mbst} 软件包中的解释方式使用户能够灵活添加或扣减某些组件, 以及简化或复杂一些设置 。