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Communication Dans Un Congrès Année : 2022

PROMINENT DISCORD DISCOVERY WITH MATRIX PROFILE : APPLICATION TO CLIMATE DATA INSIGHTS

Résumé

The definition and extraction of actionable anomalous discords, i.e. pattern outliers, is a challenging problem in data analysis. It raises the crucial issue of identifying criteria that would render a discord more insightful than another one. In this paper, we propose an approach to address this by introducing the concept of prominent discord. The core idea behind this new concept is to identify dependencies among discords of varying lengths. How can we identify a discord that would be prominent? We propose an ordering relation, that ranks discords and we seek a set of prominent discords with respect to this ordering. Our contributions are 1) a formal definition, ordering relation and methods to derive prominent discords based on Matrix Profile techniques, and 2) their evaluation over large contextual climate data, covering 110 years of monthly data. The approach is generic and its pertinence shown over historical climate data.
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Dates et versions

hal-03676025 , version 1 (23-05-2022)

Identifiants

  • HAL Id : hal-03676025 , version 1

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Hussein El Khansa, Carmen Gervet, Audrey Brouillet. PROMINENT DISCORD DISCOVERY WITH MATRIX PROFILE : APPLICATION TO CLIMATE DATA INSIGHTS. 14th International Conference on Computer Networks & Communications (CoNeCo 2022), May 2022, Zurich, Switzerland. ⟨hal-03676025⟩
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