Abstract
Time series data are generated from almost every domain and anomaly detection becomes extremely important in the last decade. It consists in detecting anomalous patterns through identifying some new and unknown behaviors that are abnormal or inconsistent relative to most of the data. An efficient anomaly detection algorithm has to adapt the detection process for each system condition and each time series behavior. In this paper, we propose an adaptive threshold able to detect anomalies in univariate time series. Our algorithm is based on segmentation and local means and standard deviations. It allows us to simplify time series visualization and to detect new abnormal data as time series jumps within different time series behavior. On synthetic and real datasets the proposed approach shows good ability in detecting abnormalities.
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Dani, MC., Jollois, FX., Nadif, M., Freixo, C. (2015). Adaptive Threshold for Anomaly Detection Using Time Series Segmentation. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9491. Springer, Cham. https://doi.org/10.1007/978-3-319-26555-1_10
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DOI: https://doi.org/10.1007/978-3-319-26555-1_10
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