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Communication Dans Un Congrès Année : 2015

Symbolic representation of time series : a hierarchical coclustering formalization

Alexis Bondu
  • Fonction : Auteur
Marc Boullé
  • Fonction : Auteur

Résumé

The choice of an appropriate representation remains crucial for mining time series, particularly to reach a good trade-off between the dimensionality reduction and the stored information. Symbolic representations constitute a simple way of reducing the dimensionality by turning time series into sequences of symbols. SAXO is a data-driven symbolic representation of time series which encodes typical distributions of data points. This approach was first introduced as a heuristic algorithm based on a regularized coclustering approach. The main contribution of this article is to formalize SAXO as a hierarchical coclustering approach. The search for the best symbolic representation given the data is turned into a model selection problem. Comparative experiments demonstrate the benefit of the new formalization, which results in representations that drastically improve the compression of data.
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Dates et versions

hal-01559719 , version 1 (03-06-2020)

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Identifiants

  • HAL Id : hal-01559719 , version 1
  • PRODINRA : 396743

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Alexis Bondu, Marc Boullé, Antoine Cornuéjols. Symbolic representation of time series : a hierarchical coclustering formalization. Workshop "Advanced Analytics and Learning on Temporal Data ", ECML-PKDD-2015, Sep 2015, porto, Portugal. ⟨hal-01559719⟩
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