A CUSUM approach for online change-point detection on curve sequences

Anomaly detection on sequential data is common in many domains such as fraud detection for credit cards, intrusion detection for cyber-security or military surveillance. This paper addresses a new CUSUM- like method for change point detection on curves sequences in a context of preventive maintenance of transit buses door systems. The proposed approach is derived from a specific generative modeling of curves. The system is considered out of control when the parameters of the curves density change. Experimental studies performed on realistic world data demonstrate the promising behavior of the proposed method.