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Article Dans Une Revue Entropy Année : 2022

Entropy-Based Discovery of Summary Causal Graphs in Time Series

Résumé

This study addresses the problem of learning a summary causal graph on time series with potentially different sampling rates. To do so, we first propose a new causal temporal mutual information measure for time series. We then show how this measure relates to an entropy reduction principle that can be seen as a special case of the probability raising principle. We finally combine these two ingredients in PC-like and FCI-like algorithms to construct the summary causal graph. There algorithm are evaluated on several datasets, which shows both their efficacy and efficiency.

Dates et versions

hal-03765359 , version 1 (31-08-2022)

Identifiants

Citer

Charles Assaad, Emilie Devijver, Eric Gaussier. Entropy-Based Discovery of Summary Causal Graphs in Time Series. Entropy, 2022, 24 (8), pp.1156. ⟨10.3390/e24081156⟩. ⟨hal-03765359⟩
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