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End-to-end Learning for Early Classification of Time Series

Marc Rußwurm 1 Sébastien Lefevre 1 Nicolas Courty 1, 2 Rémi Emonet 3 Marco Körner 4 Romain Tavenard 5
1 OBELIX - Environment observation with complex imagery
UBS - Université de Bretagne Sud, IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
2 PANAMA - Parcimonie et Nouveaux Algorithmes pour le Signal et la Modélisation Audio
Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
5 LETG - Rennes - Littoral, Environnement, Télédétection, Géomatique
LETG - Littoral, Environnement, Télédétection, Géomatique UMR 6554
Abstract : Classification of time series is a topical issue in machine learning. While accuracy stands for the most important evaluation criterion, some applications require decisions to be made as early as possible. Optimization should then target a compromise between earliness, i.e., a capacity of providing a decision early in the sequence, and accuracy. In this work, we propose a generic, end-to-end trainable framework for early classification of time series. This framework embeds a learnable decision mechanism that can be plugged into a wide range of already existing models. We present results obtained with deep neural networks on a diverse set of time series classification problems. Our approach compares well to state-of-the-art competitors while being easily adaptable by any existing neural network topology that evaluates a hidden state at each time step.
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https://hal.archives-ouvertes.fr/hal-02174314
Contributor : Nicolas Courty <>
Submitted on : Friday, July 5, 2019 - 9:14:10 AM
Last modification on : Wednesday, June 24, 2020 - 4:19:47 PM

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  • HAL Id : hal-02174314, version 1
  • ARXIV : 1901.10681

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Marc Rußwurm, Sébastien Lefevre, Nicolas Courty, Rémi Emonet, Marco Körner, et al.. End-to-end Learning for Early Classification of Time Series. 2019. ⟨hal-02174314⟩

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