Improvement of the prediction accuracy of polar motion using empirical mode decomposition

Abstract

Previous studies revealed that the error of pole coordinate prediction will significantly increase for a prediction period longer than 100 days, and this is mainly caused by short period oscillations. Empirical mode decomposition (EMD), which is increasingly popular and has advantages over classical wavelet decomposition, can be used to remove short period variations from observed time series of pole coordinates. A hybrid model combing EMD and extreme learning machine (ELM), where high frequency signals are removed and processed time series is then modeled and predicted, is summarized in this paper. The prediction performance of the hybrid model is compared with that of the ELM-only method created from original time series. The results show that the proposed hybrid model outperforms the pure ELM method for both short-term and long-term prediction of pole coordinates. The improvement of prediction accuracy up to 360 days in the future is found to be 24.91% and 26.79% on average in terms of mean absolute error (MAE) for the xp and yp components of pole coordinates, respectively.

Keywords

• Polar motion
• Prediction model
• Empirical mode decomposition (EMD)
• Neural networks (NN)
• Extreme learning machine (ELM)