Time-series anomaly detection is increasingly important in IoT systems, sensor networks, and edge monitoring applications, where models must operate under strict constraints on memory, latency, and power consumption. While recent deep-learning approaches have improved detection accuracy, many remain computationally expensive and often fail to capture subtle anomalies due to limited multi-scale sensitivity.
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
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.
By PengYu Chen, Shang Wan, Xiaohou Shi, Yuan Chang, Yan Sun, Sajal K. Das
arXiv:2609.38004v1 Announce Type: cross
Abstract: Time series anomaly detection (TSAD) plays a crucial role in healthcare, finance, industrial monitoring, and other sectors. Within and between these...
By Jiaheng Guo, Haochen Zhang, Yu-Chao Huang, Jinhao Duan, Nicholas Konz, Tianlong Chen
arXiv:2511. 22078v2 Announce Type: replace Abstract: Many real-world scenarios involving streaming information can be represented as temporal graphs, where data flows through dynamic changes in edges over time.
By Simone Mungari, Albert Bifet, Giuseppe Manco, Bernhard Pfahringer
arXiv:2608. 01885v1 Announce Type: new Abstract: While deep learning models have achieved state-of-the-art performance in time series anomaly detection, their complex architectures incur substantial inference overhead.
By Zemin Chao, Qianhui Xu, Jianhe Cen, Guangzhi Ge, Xiao Chen, Hoangzhi Wang
The paper introduces a novel zero‑shot anomaly detection framework for multivariate IoT traffic data that combines adversarial learning and contrastive loss within a sequence‑based Variational Autoencoder. It achieves domain‑invariant latent representations and semantically structured embeddings without labeled data, using encoder/decoder adaptor layers to align feature distributions and a destination‑based segmentation strategy to model real‑world communication patterns. The method is evaluated on six diverse datasets across 44 transfer scenarios, showing strong zero‑shot generalization and competitive performance against a contrastive domain‑adaptation baseline in heterogeneous, privacy‑constrained IoT environments.
By Mahshid Rezakhani, Tolunay Seyfi, Fatemeh Afghah
arXiv:2606. 13754v1 Announce Type: new Abstract: Anomaly detection is a fundamental component of intelligent systems with applications in healthcare, cybersecurity, smart grids, and IoT environments.
By Ghazal Ghajari, Elaheh Ghajari, Ashutosh Ghimire, Saeid Ataei, Faris Alsulami, Fathi Amsaad
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.
By Youji Zhu, Hongbing Wang, Wenchao Liu, Xiaodong Liu, Xiangguang Xiong
While deep learning models have achieved state-of-the-art performance in time series anomaly detection, their complex architectures incur substantial inference overhead. Existing methods typically apply a uniform inference strategy across all data points, which is inefficient given that anomalies are inherently scarce and the vast majority of temporal data consists of predictable normal patterns.
arXiv:2607. 08978v1 Announce Type: new Abstract: Distributed IoT systems generate multivariate time-series streams for monitoring physical assets, servers, and embedded sensing platforms.
By Tung-Anh Nguyen, Van-Phuc Bui, Anh Tuyen Le, Kim Hue Ta, Minh Thuy Le, J. Andrew Zhang, Xiaojing Huang
arXiv:2607. 02981v1 Announce Type: cross Abstract: Recent advancements in the Internet of Things (IoT) emphasize the urgent need for advanced network security, as IoT networks feature dynamic topologies, imbalanced traffic, and complex attack patterns.
By Long Zhao, Shixun Ji, Bin Cheng, Bin He