Statistical Methods for Outlier Detection in Space Telemetries

Abstract

Satellites monitoring is an important task to prevent the failure of satellites. For this purpose, a large number of time series are analyzed in order to detect anomalies. In this paper, we provide a review of such analysis focusing on methods that rely on features extraction. In particular, we set up features based on fixed functional bases (Fourier, wavelets, kernel bases…) and databased bases (PCA, KPCA). The outlier detection methods we apply on those features can be distance-or density-based. Those algorithms will be tested on real telemetry data.