Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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.

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The categories of this paper are: q-alg
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The categories of this paper are: nucl-th
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The categories of this paper are: econ
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The categories of this paper are: alg-geom
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The categories of this paper are: chem-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-th
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The categories of this paper are: bayes-an
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The categories of this paper are: nlin
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The categories of this paper are: q-fin
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The categories of this paper are: cmp-lg
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The categories of this paper are: cs
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The categories of this paper are: ao-sci
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The categories of this paper are: dg-ga
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The categories of this paper are: physics
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The categories of this paper are: quant-ph