Robust Sequential Online Prediction with Dynamic Ensemble of Multiple Models: A Review

2021/12/04 by Bin Liu, Liu, Bin · 1 citation
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Data Stream Mining Techniques #FOS: Computer and information sciences #Methodology (stat.ME) #Spam and Phishing Detection

paper · pdf · doi:10.48550/arxiv.2112.02374

openalex publication_date 2021/12/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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