Robust Sequential Online Prediction with Dynamic Ensemble of Multiple Models: A Concise Introduction
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In this paper, I give a concise introduction to a generic theoretical framework termed Bayesian Dynamic Ensemble of Multiple Models (BDEMM) that is used for robust sequential online prediction. This framework has three major features: (1) it employs a model pool, rather than a single model, to capture possible statistical regularities underlying the data; (2) the model pool consists of multiple weighted candidate models, wherein the model weights are adapted online to capture possible temporal evolutions of the data; (3) the adaptation for the model weights follows Bayesian formalism. These features together define BDEMM. To make this introduction comprehensive, I describe BDEMM from four perspectives, namely the related theories, the different forms of its algorithmic implementations, its classical applications, related open resources, followed by a discussion of open problems that are worth further research.
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