A Multi-parameter Prediction Method for Satellite Telemetry Data
In response to the current problem of insufficient accuracy in multi-parameter predic-tion of telemetry data,a prediction method based on graph attention networks and time-domain convolutional networks is proposed in the paper.Firstly,a multi-scale time-domain convolutional residual network component is used to extract the time dependencies of telemetry temporal data under different time spans,in order to capture temporal patterns.Subsequently,a graph struc-ture learning component is used to automatically acquire spatial dependencies between telemetry data variables,in order to capture spatial patterns.Finally,the graph node feature representation is fused with the data embedding representation to enhance the learning capability of the graph at-tention networks in the process of information aggregation and message transmission.The appli-cation results on a certain satellite telemetry dataset show that the method reduces the MAE by 62.38%compared to the LSTM model,significantly improving the prediction accuracy of multi-ple parameters in the telemetry data,and providing more decision-making and analysis support for guaranteeing the normal operation of satellites in orbit.