arXiv Machine Learning By Youji Zhu, Hongbing Wang, Wenchao Liu, Xiaodong Liu, Xiangguang Xiong

PaAno+: Multiscale Encoding and Cross-Variable Attention for Time Series Anomaly Detection

Read the original on arXiv Machine Learning →

arXiv:2606. 20055v1 Announce Type: new Abstract: Time-series anomaly detection has significant practical value for industrial and medical monitoring, as well as other critical domains.

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arXiv AI
Jul 2

PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection

arXiv:2602. 01359v3 Announce Type: replace-cross Abstract: Although recent studies on time-series anomaly detection have increasingly adopted ever-larger neural network architectures such as transformers and foundation models, they incur high computational costs and memory usage, making them impractical for real-time and resource-constrained scenarios.

By Jinju Park, Seokho Kang