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.

翻译：多元贝叶斯结构时间序列（MBSTS）模型是一种通用的机器学习模型，用于处理多个相关时间序列的推断与预测，且允许为每个目标序列选择不同的同期预测变量候选池。MBSTS模型具有广泛的应用场景，特别适用于特征选择、时间序列预测、即时预测、因果影响推断等领域。本文展示了如何使用R语言的`mbsts`包进行MBSTS建模，在包中用户友好型函数与开发者友好型函数及其对应方法论之间建立了桥梁。本文以支持用户灵活增减组件、简化或复杂化某些配置的方式，阐释了包中的面向对象函数。