正态性
异常检测
稳健性(进化)
推论
计算机科学
系列(地层学)
时间序列
异常(物理)
人工智能
机器学习
模式识别(心理学)
数学
统计
古生物学
生物化学
化学
物理
生物
基因
凝聚态物理
作者
Dongmin Kim,Sunghyun Park,Jaegul Choo
出处
期刊:Cornell University - arXiv
日期:2023-01-01
被引量:1
标识
DOI:10.48550/arxiv.2312.11976
摘要
Time-series anomaly detection deals with the problem of detecting anomalous timesteps by learning normality from the sequence of observations. However, the concept of normality evolves over time, leading to a "new normal problem", where the distribution of normality can be changed due to the distribution shifts between training and test data. This paper highlights the prevalence of the new normal problem in unsupervised time-series anomaly detection studies. To tackle this issue, we propose a simple yet effective test-time adaptation strategy based on trend estimation and a self-supervised approach to learning new normalities during inference. Extensive experiments on real-world benchmarks demonstrate that incorporating the proposed strategy into the anomaly detector consistently improves the model's performance compared to the baselines, leading to robustness to the distribution shifts.
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