Research on the Application of ARIMA-SVR Combination Model in Satellite Telemetry Parameter Prediction

In order to provide decision analysis support for assisting the in-orbit operation of satellite, combining with the time series characteristics of satellite telemetry parameters, an ARIMA-SVR combination prediction method is used to judge whether the actual telemetry data is in the normal range through the prediction of satellite telemetry parameters. In this model, ARIMA model is used to linearly fit the preprocessed data, and SVR model is used to compensate the nonlinear part of the data. Based on the telemetry data of temperature of star sensor A in KX09 satellite, the combination model was used to predict the short-term and medium-term temperature of star sensor A, and the Root Mean Square Error (RMSE) of the short-term and medium-term temperature of star sensor A was 0.768 and 0.968, respectively. Compared with the single ARIMA model, the RMSE of the short and medium-term temperature of star sensor A was 46.2% and 16.4% higher than that of the single ARIMA model respectively. In addition, the x-axis angular velocity of gyro B on the satellite is predicted in short and medium term. In the short term prediction, the RMSE of the combined model is increased by 71.2% compared with that of the single ARIMA model. In the medium prediction, the RMSE of the combination model is 64.2% higher than that of the single ARIMA model. Experimental results show that the ARIMA-SVR combination model provides effective decision analysis support for ensuring the healthy in-orbit operation of satellites.