Beginning Anomaly Detection Using


supervised  anomaly detection



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Beginning Anomaly Detection Using Python-Based Deep Learning

supervised 

anomaly detection, meaning you had the anomalies and the normal data labeled. You 

won’t always have this luxury, and you shouldn’t expect it either because of the massive 

volumes of data that can be involved. For your next example, you will be implementing 

the encoder-decoder based temporal convolutional network (ED-TCN), but it will also 

be an instance of supervised anomaly detection so that it can be compared to the dilated 

TCN model given a similar task. However, keep in mind that since it is based on an 

autoencoder framework, the ED-TCN should also be able to perform 


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