| KEYWORD |
Unraveling Anomaly Detection for Multivariate Time Series
Parole chiave ANOMALY DETECTION, DEEP LEARNING, MACHINE LEARNING, DEEP LEARNING, OPTIMIZATION, TIME SERIES
Riferimenti SANTA DI CATALDO
Riferimenti esterni francesco.ponzio@polito.it, alessio.mascolini@polito.it
Gruppi di ricerca DAUIN - GR-06 - ELECTRONIC DESIGN AUTOMATION - EDA
Descrizione Research in the development of novel methodologies for detecting anomalies in time series is of great impact and has attracted a huge body of research in the last years. Nonetheless, progress is hampered for the following three main reasons: (1) public benchmarks are biased (e.g., due to potentially erroneous anomaly labels), (2) there is no widely accepted standard evaluation metric, and (3) evaluation protocols are mostly inconsistent.
Starting from the longitudinal study by Wagner et al.[1], the current thesis aims at 1) evaluating the current state of the art panorama for time series anomaly detection (AD) on a new dataset recently published; 2) evaluating the current state of the art panorama for time series anomaly detection (AD) on a real world industrial case study (namely, Electron beam melting); 3) exploring on such datasets a new paradigm for anomaly detection based on variational inference.
[1] Wagner, Dennis, et al. "TimeSeAD: Benchmarking Deep Multivariate Time-Series Anomaly Detection." Transactions on Machine Learning Research (2023).
Conoscenze richieste Solid programming skills (Python), prior knowledge of Machine Learning/Deep Learning design frameworks.
Scadenza validita proposta 28/07/2024
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