Chung-Ang University Researchers Review Deep Learning-Based Methods to Detect Time Series Data Anomaly
Monitoring financial security, industrial safety, medical conditions, climate, and pollution require analysis of large volumes of time series data. A crucial step in this analysis involves identification of unusual points, patterns, or events that deviate from a dataset. This is known as "anomaly detection" and is performed using data mining techniques. Although deep learning methods have been extensively applied in anomaly detection, there is no one-size-fits-all technique that works for multip
Monitoring financial security, industrial safety, medical conditions, climate, and pollution require analysis of large volumes of time series data. A crucial step in this analysis involves identification of unusual points, patterns, or events that deviate from a dataset. This is known as "anomaly detection" and is performed using data mining techniques. Although deep learning methods have been extensively applied in anomaly detection, there is no one-size-fits-all technique that works for multip
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