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Pré-Publication, Document De Travail Année : 2022

Discovery of Extended Summary Graphs in Time Series

Résumé

This study addresses the problem of learning an extended summary causal graph from time series. The algorithms we propose fit within the well-known constraint-based framework for causal discovery and make use of information-theoretic measures to determine (in)dependencies between time series. We first introduce generalizations of the causation entropy measure to any lagged or instantaneous relations, prior to using this measure to construct extended summary causal graphs by adapting two wellknown algorithms, namely PC and FCI. The behaviour of our method is illustrated through several experiments.
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Dates et versions

hal-03815841 , version 1 (15-10-2022)

Identifiants

  • HAL Id : hal-03815841 , version 1

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Charles K Assaad, Emilie Devijver, Eric Gaussier. Discovery of Extended Summary Graphs in Time Series. 2022. ⟨hal-03815841⟩
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