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Ali Karami

ali.karami@mila.quebec
Maîtrise recherche - McGill University
Superviseur⋅e principal⋅e
Narges Armanfard
Github
Google Scholar

Publications

Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection
Ali Karami
Thi Kieu Khanh Ho
Narges Armanfard
2024-03-18
ArXiv (prépublication)
doi.org
arxiv.org
Graph-based Time-Series Anomaly Detection: 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 and the inter-variable dependency, where a variable can be defined as an observation 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 Graph-based TSAD (G-TSAD). First, we explore the significant potential of graph representation learning for time-series data. Then, we review state-of-the-art graph anomaly detection techniques in the context of time series and discuss their strengths and drawbacks. Finally, we discuss the technical challenges and potential future directions for possible improvements in this research field.
2023-01-31
ArXiv (prépublication)
doi.org
arxiv.org