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The training of anomaly detection models usually requires labeled data. We present in this work a novel approach for anomaly detection in time series which trains unsupervised using a convolutional approach coupled to an autoencoder framework. After training, only a small amount of labeled data is needed to adjust the anomaly threshold. We show ...
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