CrossAD: Time Series Anomaly Detection with Cross-scale Associations and Cross-window Modeling
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911209389817856 |
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| author | Li, Beibu Shentu, Qichao Shu, Yang Zhang, Hui Li, Ming Jin, Ning Yang, Bin Guo, Chenjuan |
| author_facet | Li, Beibu Shentu, Qichao Shu, Yang Zhang, Hui Li, Ming Jin, Ning Yang, Bin Guo, Chenjuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_12489 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CrossAD: Time Series Anomaly Detection with Cross-scale Associations and Cross-window Modeling Li, Beibu Shentu, Qichao Shu, Yang Zhang, Hui Li, Ming Jin, Ning Yang, Bin Guo, Chenjuan Machine Learning 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. |
| title | CrossAD: Time Series Anomaly Detection with Cross-scale Associations and Cross-window Modeling |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.12489 |