Unsupervised Anomaly Detection in Time-series: An Extensive Evaluation and Analysis of State-of-the-art Methods
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arXiv
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| Format: | Preprint |
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2022
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| author | Mejri, Nesryne Lopez-Fuentes, Laura Roy, Kankana Chernakov, Pavel Ghorbel, Enjie Aouada, Djamila |
| author_facet | Mejri, Nesryne Lopez-Fuentes, Laura Roy, Kankana Chernakov, Pavel Ghorbel, Enjie Aouada, Djamila |
| contents | Unsupervised anomaly detection in time-series has been extensively investigated in the literature. Notwithstanding the relevance of this topic in numerous application fields, a comprehensive and extensive evaluation of recent state-of-the-art techniques taking into account real-world constraints is still needed. Some efforts have been made to compare existing unsupervised time-series anomaly detection methods rigorously. However, only standard performance metrics, namely precision, recall, and F1-score are usually considered. Essential aspects for assessing their practical relevance are therefore neglected. This paper proposes an in-depth evaluation study of recent unsupervised anomaly detection techniques in time-series. Instead of relying solely on standard performance metrics, additional yet informative metrics and protocols are taken into account. In particular, (i) more elaborate performance metrics specifically tailored for time-series are used; (ii) the model size and the model stability are studied; (iii) an analysis of the tested approaches with respect to the anomaly type is provided; and (iv) a clear and unique protocol is followed for all experiments. Overall, this extensive analysis aims to assess the maturity of state-of-the-art time-series anomaly detection, give insights regarding their applicability under real-world setups and provide to the community a more complete evaluation protocol. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2212_03637 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Unsupervised Anomaly Detection in Time-series: An Extensive Evaluation and Analysis of State-of-the-art Methods Mejri, Nesryne Lopez-Fuentes, Laura Roy, Kankana Chernakov, Pavel Ghorbel, Enjie Aouada, Djamila Machine Learning Unsupervised anomaly detection in time-series has been extensively investigated in the literature. Notwithstanding the relevance of this topic in numerous application fields, a comprehensive and extensive evaluation of recent state-of-the-art techniques taking into account real-world constraints is still needed. Some efforts have been made to compare existing unsupervised time-series anomaly detection methods rigorously. However, only standard performance metrics, namely precision, recall, and F1-score are usually considered. Essential aspects for assessing their practical relevance are therefore neglected. This paper proposes an in-depth evaluation study of recent unsupervised anomaly detection techniques in time-series. Instead of relying solely on standard performance metrics, additional yet informative metrics and protocols are taken into account. In particular, (i) more elaborate performance metrics specifically tailored for time-series are used; (ii) the model size and the model stability are studied; (iii) an analysis of the tested approaches with respect to the anomaly type is provided; and (iv) a clear and unique protocol is followed for all experiments. Overall, this extensive analysis aims to assess the maturity of state-of-the-art time-series anomaly detection, give insights regarding their applicability under real-world setups and provide to the community a more complete evaluation protocol. |
| title | Unsupervised Anomaly Detection in Time-series: An Extensive Evaluation and Analysis of State-of-the-art Methods |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2212.03637 |