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Communication Dans Un Congrès Année : 2022

Automata-based explainable representation for a complex system of multivariate times series

Résumé

Complex systems represented by multivariate time series are ubiquitous in many applications, especially in industry. Understanding a complex system, its states and their evolution over time is a challenging task. This is due to the permanent change of contextual events internal and external to the system. We are interested in representing the evolution of a complex system in an intelligible and explainable way based on knowledge extraction. We propose XR-CSB (eXplainable Representation of Complex System Behavior) based on three steps: (i) a time series vertical clustering to detect system states, (ii) an explainable visual representation using unfolded finite-state automata and (iii) an explainable pre-modeling based on an enrichment via exploratory metrics. Four representations adapted to the expertise level of domain experts for acceptability issues are proposed. Experiments show that XR-CSB is scalable. Qualitative evaluation by experts of different expertise levels shows that XR-CSB meets their expectations in terms of explainability, intelligibility and acceptability.
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Dates et versions

hal-03896639 , version 1 (13-12-2022)

Identifiants

Citer

Ikram Chraibi Kaadoud, Lina Fahed, Tian Tian, Yannis Haralambous, Philippe Lenca. Automata-based explainable representation for a complex system of multivariate times series. IC3K 2022: 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR, Oct 2022, Valletta, Malta. pp.170-179, ⟨10.5220/0011363400003335⟩. ⟨hal-03896639⟩
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