Objectives Length of day variation (ΔLOD) predictions play an important role in tracking and navigation of deep-space detector, precise determination of artificial satellite orbit and climate forecasting. In full consideration of the time-varying characteristics of ΔLOD, we present the application of a hybrid technique for predicting ΔLOD.
Methods The ΔLOD predictions are generated by means of the combination of singular spectrum analysis (SSA) extrapolation for the linear trend, annual and semiannual oscillations in ΔLOD based on an iterative interpolation strategy, and autoregressive integrated moving average (ARIMA) stochastic prediction of SSA remaining residuals, referred to as SSA+ARIMA. In order to evaluate the effectiveness of this approach, the ΔLOD predictions up to 365 d into the future are calculated year-by-year for the 2-year period from Jan. 1, 2000 to Dec. 31, 2001 using the data covering the previous 10 years from International Earth Rotation and Reference Systems Service C04 series. The prediction results are analyzed and compared with those obtained by machine learning methods such as back propagation neural network (BPNN), general regression neural network (GRNN) and Gaussian process (GP).
Results It is shown that the accuracy of the predictions are better than that by machine learning methods in terms of the mean absolute error (MAE) of predictions, especially for medium and long-term predictions. Compared with the predictions obtained by the BPNN, GRNN and GP, the MAE of the proposed SSA+ARIMA predictions up to 30 d and 365 d in future is reduced by 39% and 61%, respectively.
Conclusions The proposed method provides a new solution for ΔLOD prediction.
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