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Ali Karami

Alumni
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Publications

Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection
Ali Karami
Thi Kieu Khanh Ho
Narges Armanfard
2025-03-05
2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (publié)
doi.org
arxiv.org
Graph Anomaly Detection in Time Series: A Survey
Thi Kieu Khanh Ho
Ali Karami
Narges Armanfard
With the recent advances in technology, a wide range of systems continue to collect a large amount of data over time and thus generate time … (voir plus)series. Time-Series Anomaly Detection (TSAD) is an important task in various time-series applications such as e-commerce, cybersecurity, vehicle maintenance, and healthcare monitoring. However, this task is very challenging as it requires considering both the intra-variable dependency (relationships within a variable over time) and the inter-variable dependency (relationships between multiple variables) existing in time-series data. Recent graph-based approaches have made impressive progress in tackling the challenges of this field. In this survey, we conduct a comprehensive and up-to-date review of TSAD using graphs, referred to as G-TSAD. First, we explore the significant potential of graph representation for time-series data and and its contributions to facilitating anomaly detection. Then, we review state-of-the-art graph anomaly detection techniques, mostly leveraging deep learning architectures, in the context of time series. For each method, we discuss its strengths, limitations, and the specific applications where it excels. Finally, we address both the technical and application challenges currently facing the field, and suggest potential future directions for advancing research and improving practical outcomes.
2023-01-30
ArXiv (prépublication)
doi.org
arxiv.org