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

arXiv: https://arxiv.org/abs/2112.02374 · HTML: https://mlsanity.com/paper/2112.02374/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2021.

## Built on by (most cited first, 4 of 4)

- [A unifying framework for generalised Bayesian online learning in non-stationary environments](https://mlsanity.com/paper/2411.10153/) (2024)
- [A Gaussian Process-based Streaming Algorithm for Prediction of Time Series With Regimes and Outliers](https://mlsanity.com/paper/2406.00570/) (2024)
- [PB-OEL: A Performance-Bounded Online Ensemble Learning Framework With Mixed Feedback for Real-Time Safety Assessment](https://mlsanity.com/paper/2503.15581/) (2025)
- [Exploring the Link Between Bayesian Inference and Embodied Intelligence: Toward Open Physical-World Embodied AI Systems](https://mlsanity.com/paper/2507.21589/) (2025)

## Abstract (arXiv)

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.