arXiv:2602. 08638v2 Announce Type: replace-cross Abstract: As a fundamental data mining task, unsupervised time series anomaly detection (TSAD) aims to build a model for identifying abnormal timestamps without assuming the availability of annotations.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
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.
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.
arXiv:2607. 00720v1 Announce Type: cross Abstract: Despite the increasing sophistication of industrial AI systems, the ability to reliably detect subtle and noisy anomalies in complex time series data remains a critical yet unresolved challenge.
arXiv:2508. 00909v2 Announce Type: replace Abstract: Time series anomaly detection plays a critical role in a wide range of real-world applications.
arXiv:2603. 26842v3 Announce Type: replace-cross Abstract: Time series anomaly detection (TSAD) is essential for maintaining the reliability and security of IoT-enabled service systems.
arXiv:2604. 13924v3 Announce Type: replace-cross Abstract: Time-series anomaly detection (TSAD) is critical in domains such as industrial monitoring, healthcare, and cybersecurity, but it remains challenging due to rare and heterogeneous anomalies and the scarcity of labelled data.