Matrix Profile (MP) methods are an interpretable and scalable family of distance-based methods for time-series anomaly detection, but strong benchmark performance still depends on design choices beyond a vanilla nearest-neighbor profile. This technical report documents an open-source Matrix Profile for Anomaly Detection (MMPAD) submission to TSB-AD, a benchmark that covers both univariate and multivariate time series. The submitted system combines pre-sorted multidimensional aggregation, efficient exclusion-zone-aware k-nearest-neighbor (kNN) retrieval for repeated anomalies, and moving-average post-processing. To serve as a reproducible reference for MP-based anomaly detection on TSB-AD, we detail the released implementation, the hyperparameter settings for the univariate and multivariate tracks, and the corresponding benchmark results. We further analyze how the system performs on the aggregate leaderboard and across specific dataset characteristics.The open-source implementation is available at https://github.com/mcyeh/mmpad_tsb.
PaAno+: Multiscale Encoding and Cross-Variable Attention for Time Series Anomaly Detection
School of Mathematical Sciences, Guizhou Normal University（贵州师范大学数学科学学院）；School of Big Data and Computer Science, Guizhou Normal University（贵州师范大学大数据与计算机科学学院）
VACE: Learning Geometrically Structured Representations for Time Series Anomaly Detection
Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI)（安达卢西亚数据科学与计算智能研究 institute）；Department of Computer Science and Artificial Intelligence (DECSAI), University of Granada（格拉纳达大学计算机科学与人工智能系）
CCE: Confidence-Consistency Evaluation for Time Series Anomaly Detection
Pengcheng Laboratory, Shenzhen, Guangdong, 518066, China（鹏城实验室，深圳，广东，518066，中国）；School of Future Technology, South China University of Technology, Guangzhou, Guangdong 510650, China（未来技术学院，华南理工大学，广州，广东 510650，中国）；School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong 510650, China（计算机科学与工程学院，华南理工大学，广州，广东 510650，中国）；Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China（计算机科学系，香港 Baptist 大学，香港特别行政区，中国）
GNNs for Time Series Anomaly Detection: An Open-Source Framework and a Critical Evaluation
Facultad de Ingeniería, Universidad de la República（拉普拉塔大学工程学院）；Pento；Centro Interdisciplinario en Ciencia de Datos y Aprendizaje Automático (CICADA), Universidad de la República（跨学科数据科学与自动学习中心（CICADA），拉普拉塔大学）
Advancements in ultra-low-power tiny machine learning (TinyML) systems promise to unlock an entirely new class of smart applications. However, continued progress is limited by the lack of a widely accepted and easily reproducible benchmark for these systems. To meet this need, we present MLPerf Tiny, the first industry-standard benchmark suite for ultra-low-power tiny machine learning systems. The benchmark suite is the collaborative effort of more than 50 organizations from industry and academia and reflects the needs of the community. MLPerf Tiny measures the accuracy, latency, and energy of…
Towards eco friendly cybersecurity: machine learning based anomaly detection with carbon and energy metrics
Gannon University（甘农大学）；Western Illinois University（西伊利诺伊大学）；University of South Wales（南威尔士大学）；Washington University of Science and Technology（华盛顿科技大学）；International American University（国际美国大学）；California State University, Dominguez Hills（加州州立大学多明格斯山分校）；University of New Haven（纽黑文大学）
Quantum Machine Learning for Cyber-Physical Anomaly Detection in Unmanned Aerial Vehicles: A Leakage-Free Evaluation with Proxy-Audited Feature Sets
Corporation for Aerospace Initiatives, Research and Innovation (CASIRI)（航空航天研究与创新公司）；Department of Electronics Engineering, Universidad Nacional de Colombia（国立哥伦比亚大学电子工程系）；Department of Electronics Engineering, Universidad del Cauca（卡利学院电子工程系）；Department of Physics, Universidad del Cauca（卡利大学物理系）
LLM-Based Threat Detection and Prevention Framework for IoT Ecosystems
School of Computer Science and Technology, Algoma University（计算机科学与技术学院，阿尔戈马大学）；School of Electrical Engineering and Computer Science, University of Ottawa（电气工程与计算机科学学院，渥太华大学）
Panasonic AI Lab（松下人工智能实验室）；Panasonic Technology Division（松下技术事业部）
Unsupervised anomaly detection with localization has many practical applications when labeling is infeasible and, moreover, when anomaly examples are completely missing in the train data. While recently proposed models for such data setup achieve high accuracy metrics, their complexity is a limiting factor for real-time processing. In this paper, we propose a real-time model and analytically derive its relationship to prior methods. Our CFLOW-AD model is based on a conditional normalizing flow framework adopted for anomaly detection with localization. In particular, CFLOW-AD consists of a disc…
