Saliency-Aware Time Series Anomaly Detection for Space Applications

Detecting anomalies in real-world multivariate time series data is challenging due to the deviation between the distributions of normal and anomalous data. Previous studies focused on capturing time and spatial features but lacked an effective criterion to measure differentiation from normal data. Our proposed method utilizes saliency detection, similar to anomaly detection, to identify the most significant region and effectively detect abnormal data. In this work, We propose a novel framework, Saliency-aware Anomaly Detection (SalAD), for detecting anomalies in multivariate time series data. SalAD comprises three main components: 1) a saliency detection module to remove redundant data, 2) an unsupervised saliency-aware forecasting model, and 3) a saliency-aware anomaly score to differentiate anomalies. We evaluate our model using the real-world Korea Aerospace Research Institute (KARI) orbital element dataset, which includes six orbital elements and unexpected disturbances from satellites, as well as conducting extensive experiments on four benchmark datasets to demonstrate its effectiveness and superiority over other baselines. The SalAD framework has been deployed on the K3A and K5 satellites.