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Journal Articles Proceedings of the VLDB Endowment (PVLDB) Year : 2021

A Demonstration of the Exathlon Benchmarking Platform for Explainable Anomaly Detection

Abstract

In this demo, we introduce Exathlon - a new benchmarking platform for explainable anomaly detection over high-dimensional time series. We designed Exathlon to support data scientists and researchers in developing and evaluating learned models and algorithms for detecting anomalous patterns as well as discovering their explanations. This demo will showcase Exathlon's curated anomaly dataset, novel benchmarking methodology, and end-to-end data science pipeline in action via example usage scenarios.
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Dates and versions

hal-03383535 , version 1 (18-10-2021)

Identifiers

  • HAL Id : hal-03383535 , version 1

Cite

Vincent Jacob, Fei Song, Arnaud Stiegler, Bijan Rad, Yanlei Diao, et al.. A Demonstration of the Exathlon Benchmarking Platform for Explainable Anomaly Detection. Proceedings of the VLDB Endowment (PVLDB), 2021. ⟨hal-03383535⟩
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