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Memory-augmented Adversarial Autoencoders for Multivariate Time-series Anomaly Detection with Deep Reconstruction and Prediction
Recent progress of unsupervised time-series anomaly detection mainly use deep autoencoders to solve this problem, i. e. training on normal samples and producing significant reconstruction error on abnormal inputs.
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