TY - GEN
T1 - Causal and Interpretable Rules for Time Series Analysis
AU - Dhaou, Amin
AU - Bertoncello, Antoine
AU - Gourvénec, Sébastien
AU - Garnier, Josselin
AU - Le Pennec, Erwan
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/8/14
Y1 - 2021/8/14
N2 - The number of complex infrastructures in an industrial setting is growing and is not immune to unexplained recurring events such as breakdowns or failure that can have an economic and environmental impact. To understand these phenomena, sensors have been placed on the different infrastructures to track, monitor, and control the dynamics of the systems. The causal study of these data allows predictive and prescriptive maintenance to be carried out. It helps to understand the appearance of a problem and find counterfactual outcomes to better operate and defuse the event. In this paper, we introduce a novel approach combining the case-crossover design which is used to investigate acute triggers of diseases in epidemiology, and the Apriori algorithm which is a data mining technique allowing to find relevant rules in a dataset. The resulting time series causal algorithm extracts interesting rules in our application case which is a non-linear time series dataset. In addition, a predictive rule-based algorithm demonstrates the potential of the proposed method.
AB - The number of complex infrastructures in an industrial setting is growing and is not immune to unexplained recurring events such as breakdowns or failure that can have an economic and environmental impact. To understand these phenomena, sensors have been placed on the different infrastructures to track, monitor, and control the dynamics of the systems. The causal study of these data allows predictive and prescriptive maintenance to be carried out. It helps to understand the appearance of a problem and find counterfactual outcomes to better operate and defuse the event. In this paper, we introduce a novel approach combining the case-crossover design which is used to investigate acute triggers of diseases in epidemiology, and the Apriori algorithm which is a data mining technique allowing to find relevant rules in a dataset. The resulting time series causal algorithm extracts interesting rules in our application case which is a non-linear time series dataset. In addition, a predictive rule-based algorithm demonstrates the potential of the proposed method.
KW - case-crossover design
KW - causality
KW - data mining
KW - predictive maintenance
KW - time series
U2 - 10.1145/3447548.3467161
DO - 10.1145/3447548.3467161
M3 - Conference contribution
AN - SCOPUS:85114951912
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 2764
EP - 2772
BT - KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
T2 - 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021
Y2 - 14 August 2021 through 18 August 2021
ER -