用切片瓦瑟斯坦距离实现无监督异常检测，适合关键领域数据筛选。
Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates
- 基于切片瓦瑟斯坦距离设计无监督异常检测方法
- 在合成数据与标准数据集上验证有效，支持关键峰值补贴响应分析
- 开源首个北方气候下局部峰值补贴数据集，支持基准测试
本文提出一种基于切片瓦瑟斯坦距离的新型无监督异常检测（AD）方法。该方法在部署机器学习模型于能源等关键领域的MLOps流程中具有重要意义，因其能实现保守的数据选择。此外，我们公开了首个展示北方气候下局部关键峰值补贴需求响应的数据集。我们在合成数据集和标准异常检测数据集上展示了该方法的有效性，并用于构建首个针对该开源数据集的基准测试。
原文摘要 · Abstract (English)
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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