Discovery of extended summary graphs in time series

Charles K. Assaad, Emilie Devijver, Eric Gaussier
Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, PMLR 180:96-106, 2022.

Abstract

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 well-known algorithms, namely PC and FCI. The behaviour of our method is illustrated through several experiments.

Cite this Paper

BibTeX
@InProceedings{pmlr-v180-assaad22a, title = {Discovery of extended summary graphs in time series}, author = {Assaad, Charles K. and Devijver, Emilie and Gaussier, Eric}, booktitle = {Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence}, pages = {96--106}, year = {2022}, editor = {Cussens, James and Zhang, Kun}, volume = {180}, series = {Proceedings of Machine Learning Research}, month = {01--05 Aug}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v180/assaad22a/assaad22a.pdf}, url = {https://proceedings.mlr.press/v180/assaad22a.html}, abstract = {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 well-known algorithms, namely PC and FCI. The behaviour of our method is illustrated through several experiments.} }
Endnote
%0 Conference Paper %T Discovery of extended summary graphs in time series %A Charles K. Assaad %A Emilie Devijver %A Eric Gaussier %B Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2022 %E James Cussens %E Kun Zhang %F pmlr-v180-assaad22a %I PMLR %P 96--106 %U https://proceedings.mlr.press/v180/assaad22a.html %V 180 %X 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 well-known algorithms, namely PC and FCI. The behaviour of our method is illustrated through several experiments.
APA
Assaad, C.K., Devijver, E. & Gaussier, E.. (2022). Discovery of extended summary graphs in time series. Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 180:96-106 Available from https://proceedings.mlr.press/v180/assaad22a.html.

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