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A review on outlier/anomaly detection in time series data
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A review on outlier/anomaly detection in time series data
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Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series. Mining this data has become an important task for researchers and practitioners in the past few years, including the detection of outliers or anomalies that may represent errors or events of interest. This review aims to provide a structured and comprehensive state-of-the-art on outlier detection techniques in the context of time series. To this end, a taxonomy is presented based on the main aspects that characterize an outlier detection technique.
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Cited by 1 Pith paper
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Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems
A VaDE-based latent clustering detector that drops reconstruction wins a fair, difficulty-stratified anomaly detection protocol on three CPS datasets, with margins tracking dataset multimodality.
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