Time series are ubiquitous in aerospace engineering and represent a large part of the data with highest business value potential. In this talk we focus on automatic anomaly detection tasks for aircraft sensors. We present the newest results on our project using semi-supervised Deep Learning approach for automatic discovery of contextual and collective anomalies in a large dataset describing one aircraft subsystem.

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