The short-term prediction of Polar Motion using the combination of SSA and the Multivariate Multi-step 1D- Convolutional Neural Networks with Multioutput strategy.

Polar Motion is the movement of the Earth's rotational axis relative to its crust, reflecting the influence of the material exchange and mess redistribution of each layer of the Earth on the Earth's rotation axis.
The real-time estimation of Polar Motion (PM) is needed for the navigation of Earth satellites and interplanetary spacecraft. However, it is impossible to have real-time information due to the complexity of the measurement model and data processing.

Various prediction methods have been developed. However, the accuracy of PM prediction is still not satisfactory even for a few days in the future. Therefore, a new technique or a combination of the existing methods needs to be investigated for improving the accuracy of the prediction PM.
In this study, we combine the 1D  Convolutional Neural Network with the Singular Spectrum Analysis (SSA).
 The computational strategy follows multiple steps, first, we model the predominant trend of the PM time series using SSA. Then, the difference between the PM time series and its SSA estimation is modeled using the 1D Convolution Neural Network. However, we developed a Multivariate Multi step 1D-CNN Model with a Multi-output strategy to predict at the same time both components (Xp, Yp)  of the PM.  . We introduce to the  Model: the Ocean Angular Momentum, Atmospheric Angular Momentum, and Hydrological Angular Momentum (OAM+AAM+HAM) to improve the results. Multiple sets of PM predictions which range between 1 and 10 days have been performed based on an IERS 14 C04 time series to assess the capability of our hybrid Model. Our results illustrate that the proposed method can efficiently predict both (Xp, Yp) of PM.