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Book Sections Year : 2019

Anomaly Detection for Bivariate Signals

Abstract

The anomaly detection problem for univariate or multivariate time series is a critical question in many practical applications as industrial processes control, biological measures, engine monitoring, supervision of all kinds of behavior. In this paper we propose an empirical approach to detect anomalies in the behavior of multivariate time series. The approach is based on the empirical estimation of conditional quantiles. The method is tested on artificial data and its effectiveness is proven in the real framework of aircraft-engines monitoring.
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Dates and versions

hal-02874017 , version 1 (14-10-2020)

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Cite

Marie Cottrell, Cynthia Faure, Jérôme Lacaille, Madalina Olteanu. Anomaly Detection for Bivariate Signals. Rojas I., Joya G., Catala A. (eds). Advances in Computational Intelligence, part 1, IWANN 2019, vol 11506, Springer, Cham, pp.162-173, 2019, Lecture Notes in Computer Science,, ⟨10.1007/978-3-030-20521-8_14⟩. ⟨hal-02874017⟩
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