Learning and selection of dynamic Bayesian networks for online non-stationary process

Learning and selection of dynamic Bayesian networks for online non-stationary process

Matthieu Hourbracq Pierre-Henri Wuillemin Christophe Gonzales Philippe Baumard 

Sorbonne Université, CNRS, LIP6, UMR 7606, F-75005 Paris, France

Akheros S.A.S., France

Corresponding Author Email: 
firstName.lastName@lip6.fr; firstName.lastName@akheros.com
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Dynamic Bayesian Networks (DBNs) provide a principled scheme for modeling and learning conditional dependencies from complex multivariate time-series data. However, in most cases, the underlying generative Markov model is assumed to be homogeneous, meaning that neither its topology nor its parameters evolve over time. Therefore, learning a DBN to model a non-stationary process under this assumption will amount to poor predictions capabilities. Thus we build a framework to identify, in a streamed manner, transition times between underlying models and a framework to learn them in real time, without assumptions about their evolution. We propose a model for the dynamic of the transitions between modes stemming from Hidden semi-Markov Models (HsMMs) and Graphical Duration Models (GDMs). We show the method performances on simulated datasets.


DBN, ns-DBN, tv-DBN, non-stationnary, learning, real time

1. Introduction
2. Réseaux bayésiens dynamiques (non stationnaires)
3. Apprentissage de processus non stationnaires
4. Graphical duration models
5. Expériences et résultats
6. Conclusions et travaux futurs

Ce travail est supporté par Akheros S.A.S./bourse ANRT CIFRE #2014/0268 et le projet européen SCISSOR H2020-ICT-2014-1 #644425.


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