A hybrid Monte Carlo quantile EMD-LSTM method for satellite in-orbit temperature prediction and data uncertainty quantification

Yingchun Xu, Wen Yao, Xiaohu Zheng, Jingyi Chen

Abstract: Highlights•Proposing hybrid Monte Carlo quantile based empirical mode decomposition with LSTM.•Addressing the non-stationary characteristic and quantifying data uncertainty.•Obtaining more accurate and credible predictions of satellite temperature.•Solving the practical satellite in-orbit temperature prediction issue.•Providing benefits for identifying abnormal states in advance.