#ECLAD
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February 16, 2026 at 1:12 AM
impact on the prediction process. We evaluate our method on synthetic and natural datasets. Furthermore, we assess the advantages and limitations of CE in time series through empirical results. Our results show that ECLAD-ts effectively explains [6/7 of https://arxiv.org/abs/2504.05024v1]
April 8, 2025 at 6:27 AM
leaving a gap in the time series domain. In this work, we present a CE and localization method tailored to the time series domain, based on the ideas of CE methods for images. We propose the novel method ECLAD-ts, which provides post-hoc global [4/7 of https://arxiv.org/abs/2504.05024v1]
April 8, 2025 at 6:27 AM
Antonia Holzapfel, Andres Felipe Posada-Moreno, Sebastian Trimpe: Concept Extraction for Time Series with ECLAD-ts https://arxiv.org/abs/2504.05024 https://arxiv.org/pdf/2504.05024 https://arxiv.org/html/2504.05024
April 8, 2025 at 6:27 AM
''' Act 0 Scene 2 (by Monkey):

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May 3, 2025 at 8:16 AM