Asynchronous Autoregressive Prediction for Satellite Anomaly Detection

This paper proposes an ASynchronous Autoregressive Prediction (ASAP) method for satellite anomaly detection. We empirically observe that a single classification model can hardly detect unknown anomalous situations and neglect the Markov nature of temporal satellite data. To address this, we adopt an autoregressive model to deal with the prediction of unknown anomaly for satellite data. We further propose a non-uniform temporal encoding method for asynchronous data and a median filtering method for more accurate detection. To reduce the effect of outliers, we employ an adaptive threshold selection method to achieve a more robust classification boundary. Experiments on real satellite data demonstrate that the proposed ASAP method outperforms the baseline classification method by 55.79%.