Abstract
Time series anomaly detection (TSAD) plays a crucial role in healthcare, finance, industrial monitoring, and other sectors. Within and between these settings, anomalies span vastly different temporal scales, from sub-second point spikes to multi-hour drift patterns. However, most existing TSAD methods commit to a single temporal granularity, and multi-scale designs either analyze different scales in isolation or are constrained to a predefined coarse-to-fine hierarchy, both failing to sufficiently capture multi-scale interactions. To resolve this limitation, we propose Multi-Scale Autoencoder with Cross-Scale Attention for TSAD (MSCAD), a simple yet powerful semi-supervised TSAD framework founded on parallel autoencoder branches corresponding to different patch sizes. A stack of symmetric bidirectional cross-scale attention blocks enables every pair of scales to exchange information before reconstruction without allowing any single scale to be privileged. On the comprehensive TSB-AD benchmark (40 datasets, 530 series), MSCAD achieves large performance gains against 50 baselines across multiple metrics, with VUS-PR of 0.57(+9.6%) on the univariate split and 0.47(+9.3%) on the multivariate split compared to the state-of-the-art.
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Publication details
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- Open access
- Green open access
Cite this article
APA 7
Guo, J., Zhang, H., Huang, Y. C., Duan, J., Konz, N., & Chen, T. (2026). No Scale Left Behind: Multi-Scale Autoencoder with Bi-directional Attention for Time Series Anomaly Detection. https://omanscience.com/en/articles/no-scale-left-behind-multi-scale-autoencoder-with-bi-directional-attention-for-time-series-anomaly-detection
MLA 9
Guo, Jiaheng, et al. "No Scale Left Behind: Multi-Scale Autoencoder with Bi-directional Attention for Time Series Anomaly Detection." https://omanscience.com/en/articles/no-scale-left-behind-multi-scale-autoencoder-with-bi-directional-attention-for-time-series-anomaly-detection.
Chicago (author–date)
Guo, Jiaheng, Haochen Zhang, Yu-Chao Huang, Jinhao Duan, Nicholas Konz, and Tianlong Chen. 2026. "No Scale Left Behind: Multi-Scale Autoencoder with Bi-directional Attention for Time Series Anomaly Detection." https://omanscience.com/en/articles/no-scale-left-behind-multi-scale-autoencoder-with-bi-directional-attention-for-time-series-anomaly-detection.
Harvard
Guo, J., Zhang, H., Huang, Y. C., Duan, J., Konz, N. and Chen, T. (2026) 'No Scale Left Behind: Multi-Scale Autoencoder with Bi-directional Attention for Time Series Anomaly Detection', Available at: https://omanscience.com/en/articles/no-scale-left-behind-multi-scale-autoencoder-with-bi-directional-attention-for-time-series-anomaly-detection.
Vancouver
Guo J, Zhang H, Huang YC, Duan J, Konz N, Chen T. No Scale Left Behind: Multi-Scale Autoencoder with Bi-directional Attention for Time Series Anomaly Detection. https://omanscience.com/en/articles/no-scale-left-behind-multi-scale-autoencoder-with-bi-directional-attention-for-time-series-anomaly-detection
IEEE
J. Guo, H. Zhang, Y. C. Huang, J. Duan, N. Konz, and T. Chen, "No Scale Left Behind: Multi-Scale Autoencoder with Bi-directional Attention for Time Series Anomaly Detection," https://omanscience.com/en/articles/no-scale-left-behind-multi-scale-autoencoder-with-bi-directional-attention-for-time-series-anomaly-detection.