A wavelet-augmented autoencoder with a reinforcement-learning boundary tuner is proposed for multivariate time-series anomaly detection, but the reported performance figures rest on circular labels.
A novel anomaly detection method based on adaptive mahalanobis-squared distance and one-class kNN rule for structural health monitoring under environmental effects,
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Calibrated Unsupervised Anomaly Detection in Multivariate Time-series using Reinforcement Learning
A wavelet-augmented autoencoder with a reinforcement-learning boundary tuner is proposed for multivariate time-series anomaly detection, but the reported performance figures rest on circular labels.