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Affinity Workshop: Women in Machine Learning

Explaining complex system of multivariate times series behavior

Ikram Chraibi Kaadoud · Lina Fahed · Tian Tian · Yannis Haralambous · Philippe Lenca


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 areproposed. 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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