Automated Anomaly Detection in Large Sequences. Boniol, P., Linardi, M., Roncallo, F., & Palpanas, T. In 2020 IEEE 36th International Conference on Data Engineering (ICDE), pages 1834–1837, April, 2020. ISSN: 2375-026Xdoi abstract bibtex Subsequence anomaly (or outlier) detection in long sequences is an important problem with applications in a wide range of domains. However, current approaches have severe limitations: they either require prior domain knowledge, or become cumbersome and expensive to use in situations with recurrent anomalies of the same type. In this work, we address these problems, and propose NorM, a novel approach, suitable for domain-agnostic anomaly detection. NorM is based on a new data series primitive, which permits to detect anomalies based on their (dis)similarity to a model that represents normal behavior. The experimental results on several real datasets demonstrate that the proposed approach outperforms by a large margin the current state-of-the art algorithms in terms of accuracy, while being orders of magnitude faster.
@inproceedings{boniol_automated_2020,
title = {Automated {Anomaly} {Detection} in {Large} {Sequences}},
doi = {10.1109/ICDE48307.2020.00182},
abstract = {Subsequence anomaly (or outlier) detection in long sequences is an important problem with applications in a wide range of domains. However, current approaches have severe limitations: they either require prior domain knowledge, or become cumbersome and expensive to use in situations with recurrent anomalies of the same type. In this work, we address these problems, and propose NorM, a novel approach, suitable for domain-agnostic anomaly detection. NorM is based on a new data series primitive, which permits to detect anomalies based on their (dis)similarity to a model that represents normal behavior. The experimental results on several real datasets demonstrate that the proposed approach outperforms by a large margin the current state-of-the art algorithms in terms of accuracy, while being orders of magnitude faster.},
booktitle = {2020 {IEEE} 36th {International} {Conference} on {Data} {Engineering} ({ICDE})},
author = {Boniol, Paul and Linardi, Michele and Roncallo, Federico and Palpanas, Themis},
month = apr,
year = {2020},
note = {ISSN: 2375-026X},
keywords = {Anomaly detection, Anomaly discovery, Computational modeling, Conferences, Data Series, Data engineering, Data models, Electrocardiography, Time Series, Time series analysis},
pages = {1834--1837},
}
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