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Implement the affiliation based metric [1]. This metric will consider the anomalous events (subsequences of consecutive anomalous observations). For each ground truth anomaly is a so-called affliation zone created, in which the distance of the ground truth anomaly to the detected anomalies forms the basis of the performance.
[1] Huet, Alexis, Jose Manuel Navarro, and Dario Rossi. "Local evaluation of time series anomaly detection algorithms." Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2022.
The text was updated successfully, but these errors were encountered:
Implement the affiliation based metric [1]. This metric will consider the anomalous events (subsequences of consecutive anomalous observations). For each ground truth anomaly is a so-called affliation zone created, in which the distance of the ground truth anomaly to the detected anomalies forms the basis of the performance.
[1] Huet, Alexis, Jose Manuel Navarro, and Dario Rossi. "Local evaluation of time series anomaly detection algorithms." Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2022.
The text was updated successfully, but these errors were encountered: