For the random error of inertial devices, a contrastive analysis was made to the applicability and real-time performance of the traditional ARMA modeling method and the popular deep learning LSTM network. A simulation example was designed by aobtaining the output data of a specific inertial device. The study showed that:1) Without the real-time performance requirement, both ARMA modeling and LSTM network can achieve accurate prediction results, and the LSTM network is advantageous since it doesnt need to extract the signal trend and cycle terms;and 2) In real-time prediction, the LSTM network has obvious advantages, but the prediction accuracy decreases with the shortening of time series, whereas the output can still reflect the trend of noise change and can be used for optimizing the filtering algorithm of the whole control system.