Abstract
Anomaly detection in time-series is an important issue in many applications. It is particularly hard to accurately detect multiple anomalies in time-series. Pattern discovery and rule extraction are effective solutions for allowing multiple anomaly detection. In this paper, we define a Composition-based Decision Tree algorithm that automatically discovers and generates human-understandable classification rules for multiple anomaly detection in time-series. To evaluate our solution, our algorithm is compared to other anomaly detection algorithms on real datasets and benchmarks.
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Let us notice that a and b were previously denoted \(\alpha \) and \(\beta \) in [2].
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Acknowledgment
This PhD. was supported by the Management and Exploitation Service (SGE) of the Rangueil campus attached to the Rectorate of Toulouse and the research is made in the context of the neOCampus project (Paul Sabatier University, Toulouse). The authors thank the SGE for providing access to actual sensor data.
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Ben Kraiem, I., Ghozzi, F., Peninou, A., Roman-Jimenez, G., Teste, O. (2020). Automatic Classification Rules for Anomaly Detection in Time-Series. In: Dalpiaz, F., Zdravkovic, J., Loucopoulos, P. (eds) Research Challenges in Information Science. RCIS 2020. Lecture Notes in Business Information Processing, vol 385. Springer, Cham. https://doi.org/10.1007/978-3-030-50316-1_19
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DOI: https://doi.org/10.1007/978-3-030-50316-1_19
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