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- W4384157306 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." @default.
- W4384157306 created "2023-07-14" @default.
- W4384157306 creator A5048826252 @default.
- W4384157306 date "2023-10-01" @default.
- W4384157306 modified "2023-10-14" @default.
- W4384157306 title "Robust sequential online prediction with dynamic ensemble of multiple models: A review" @default.
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- W4384157306 doi "https://doi.org/10.1016/j.neucom.2023.126553" @default.
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