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

In this paper, I give a concise introduction to a generic theoretical framework termed Bayesian Dynamic Ensemble of Multiple Models (BDEMM), which is used for robust sequential online prediction with time series data. 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 five perspectives, namely the basic theories, its different forms of algorithmic implementations, its applications, its connections to related research, open resources for algorithm implementations, followed by a discussion of practical issues for applying it and some open problems that are worth further research.