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.

翻译：时间序列异常检测旨在通过学习观测序列中的常态模式来检测异常时间步。然而，常态概念会随时间的推移而演变，导致"新常态问题"——即由于训练数据与测试数据之间的分布偏移，常态分布可能发生改变。本文揭示了无监督时间序列异常检测研究中新常态问题的普遍性。为解决该问题，我们提出了一种基于趋势估计的简洁高效的测试时自适应策略，并结合自监督方法在推理过程中学习新常态。在真实世界基准上的大量实验表明，将该策略融入异常检测器后，相比基线方法能持续提升模型性能，从而增强对分布偏移的鲁棒性。