CrossAD: Time Series Anomaly Detection with Cross-scale Associations and Cross-window Modeling
This work addresses the challenge of detecting anomalies in time series data for applications like monitoring and forecasting, though it appears incremental by building on existing multi-scale methods.
The paper tackled the problem of time series anomaly detection by proposing CrossAD, a framework that incorporates cross-scale associations and cross-window modeling, achieving state-of-the-art performance across multiple real-world datasets as validated by nine evaluation metrics.
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.