The use of time series for sequential online prediction (SOP) has long been a research topic, but achieving robust and computationally efficient SOP with non-stationary time series remains a challenge. This paper reviews a framework, called Bayesian Dynamic Ensemble of Multiple Models (BDEMM), which addresses SOP in a theoretically elegant way, and have found widespread use in various fields. BDEMM utilizes a model pool of weighted candidate models, adapted online using Bayesian formalism to capture possible temporal evolutions of the data. This review comprehensively describes BDEMM from five perspectives: its theoretical foundations, algorithms, practical applications, connections to other research, and strengths, limitations, and potential future directions.

翻译：时间序列在序贯在线预测（SOP）中的应用一直是研究热点，但在非平稳时间序列上实现鲁棒且计算高效的SOP仍具挑战性。本文综述了一种名为"贝叶斯多模型动态集成"（BDEMM）的框架，该框架以理论优雅的方式解决了SOP问题，并在多个领域得到广泛应用。BDEMM利用带权重的候选模型池，通过贝叶斯形式在线自适应地捕捉数据的时间演化特征。本综述从五个维度全面阐述BDEMM：理论基础、算法实现、实际应用、与其他研究的关联，以及优势、局限性和潜在未来方向。