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Volume 14, No. 12
A Demonstration of the Exathlon Benchmarking Platform for Explainable Anomaly Detection
Authors:
Vincent Jacob (Ecole Polytechnique), Fei Song (Ecole Polytechnique), Arnaud Stiegler (Ecole Polytechnique), Bijan Rad (Ecole Polytechnique), Yanlei Diao (Ecole Polytechnique), Nesime Tatbul (Intel Labs and MIT)
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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