<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Time-series are ordered sequences of discrete-time data. Due to their temporal dimension, anomaly detection techniques used in time-series have to take into consideration time correlations and other time-related particularities. Generally, in order to evaluate the quality of an anomaly detection technique, the confusion matrix and its derived metrics such as precision and recall are used. These metrics, however, do not take this temporal dimension into consideration. In this paper, we propose three metrics that can be used to evaluate the quality of a classification, while accounting for the temporal dimension found in time-series data.</p></abstract>

Time-series are ordered sequences of discrete-time data. Due to their temporal dimension, anomaly detection techniques used in time-series have to take into consideration time correlations and other time-related particularities. Generally, in order to evaluate the quality of an anomaly detection technique, the confusion matrix and its derived metrics such as precision and recall are used. These metrics, however, do not take this temporal dimension into consideration. In this paper, we propose three metrics that can be used to evaluate the quality of a classification, while accounting for the temporal dimension found in time-series data.

Evaluation metrics for anomaly detection algorithms in time-series

Acta Universitatis Sapientiae, Informatica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Evaluation metrics for anomaly detection algorithms in time-series"}

Time-series are ordered sequences of discrete-time data. Due to their temporal dimension, anomaly detection techniques used in time-series have to take into...

Evaluation metrics for anomaly detection algorithms in time-series

Online veröffentlicht: 21. Jan. 2020

Seitenbereich: 113 - 130

Eingereicht: 17. Sept. 2019

DOI: https://doi.org/10.2478/ausi-2019-0008

Schlüsselwörter
anomaly detection, classification, evaluation metrics

© 2019 György Kovács et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Evaluation metrics for anomaly detection algorithms in time-series

Online veröffentlicht: 21. Jan. 2020

Seitenbereich: 113 - 130

Eingereicht: 17. Sept. 2019

DOI: https://doi.org/10.2478/ausi-2019-0008

Schlüsselwörteranomaly detection, classification, evaluation metrics

© 2019 György Kovács et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
anomaly detection, classification, evaluation metrics