标题： CrossAD：基于跨尺度关联和跨窗口建模的时间序列异常检测 显示英文标题

标题： CrossAD: Time Series Anomaly Detection with Cross-scale Associations and Cross-window Modeling

摘要： 时间序列异常检测在各种现实应用中起着至关重要的作用。 考虑到时间序列数据在不同采样粒度下可能表现出不同的模式，多尺度建模已被证明有助于揭示在单一尺度下可能不明显的潜在异常模式。 然而，现有方法通常独立地对多尺度信息进行建模，或依赖于简单的特征融合策略，忽略了异常发生时跨尺度关联的动态变化。 此外，大多数方法基于固定的滑动窗口进行多尺度建模，这限制了它们捕捉全面上下文信息的能力。 在本工作中，我们提出了CrossAD，一种新的时间序列异常检测框架，该框架考虑了跨尺度关联和跨窗口建模。 我们提出了一种跨尺度重建，从更粗粒度的序列中重建细粒度序列，显式地捕捉跨尺度关联。 此外，我们设计了一个查询库，并引入全局多尺度上下文，以克服固定窗口大小带来的限制。 在多个真实数据集上使用九个评估指标进行的大量实验验证了CrossAD的有效性，展示了在异常检测中的最先进性能。 显示英文摘要

摘要： Time series anomaly detection plays a crucial role in a wide range of real-world applications. Given that time series data can exhibit different patterns at different sampling granularities, multi-scale modeling has proven beneficial for uncovering latent anomaly patterns that may not be apparent at a single scale. However, existing methods often model multi-scale information independently or rely on simple feature fusion strategies, neglecting the dynamic changes in cross-scale associations that occur during anomalies. Moreover, most approaches perform multi-scale modeling based on fixed sliding windows, which limits their ability to capture comprehensive contextual information. In this work, we propose CrossAD, a novel framework for time series Anomaly Detection that takes Cross-scale associations and Cross-window modeling into account. We propose a cross-scale reconstruction that reconstructs fine-grained series from coarser series, explicitly capturing cross-scale associations. Furthermore, we design a query library and incorporate global multi-scale context to overcome the limitations imposed by fixed window sizes. Extensive experiments conducted on multiple real-world datasets using nine evaluation metrics validate the effectiveness of CrossAD, demonstrating state-of-the-art performance in anomaly detection.