Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates

• URL: http://arxiv.org/abs/2410.21712v2
• Date: Sun, 01 Dec 2024 03:54:57 GMT
• Title: Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates
• Authors: Julien Pallage, Bertrand Scherrer, Salma Naccache, Christophe Bélanger, Antoine Lesage-Landry,
• Abstract summary: We present a new unsupervised anomaly (outlier) detection (AD) method using the sliced-Wasserstein metric.<n>This filtering technique is conceptually interesting for MLOps pipelines deploying machine learning models in critical sectors.
• Score: 25.452449432754698
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we present a new unsupervised anomaly (outlier) detection (AD) method using the sliced-Wasserstein metric. This filtering technique is conceptually interesting for MLOps pipelines deploying machine learning models in critical sectors, e.g., energy, as it offers a conservative data selection. Additionally, we open the first dataset showcasing localized critical peak rebate demand response in a northern climate. We demonstrate the capabilities of our method on synthetic datasets as well as standard AD datasets and use it in the making of a first benchmark for our open-source localized critical peak rebate dataset.

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