Abstract
As one of the main components of the Earth orientation parameters, short-term prediction of the geodetic polar motion series is crucial in the field of deep-space exploration, high-precision positioning, and timing services, which require high real-time performance. Additionally, its middle- and long-term prediction is equally important in climate forecasting and geodynamics research. In this study, we propose the combined BiLSTM+ARIMA model, which is based on bidirectional long- and short-term memory (BiLSTM) and autoregression integrated moving average (ARIMA). First, ensemble empirical mode decomposition (EEMD) is performed as a filter to decompose the polar motion time series to obtain low- and high-frequency signals. The EOP14 C04 time series provided by International Earth Rotation and Reference Systems Service and decomposed by EEMD includes low-frequency signals like the long-term trend, decadal oscillation, Chandler wobble, and prograde annual wobble, along with shorter-period high-frequency signals. Second, low- and high-frequency signals are predicted using BiLSTM and ARIMA models, respectively. Finally, the low- and high-frequency signal forecast components are reconstructed to obtain geodetic polar motion predictions. In middle- and long-term polar motion prediction, the results show that the proposed model can improve the prediction accuracy by up to 42% and 17%, respectively. This demonstrated that the BiLSTM+ARIMA model can effectively improve the accuracy of polar motion prediction.
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Acknowledgments
The scholars and reviewers of this study are to be thanked for their expert recommendations and insightful comments. We are also grateful to the IERS for providing the polar motion time series data. This research was supported and funded by Beijing Key Laboratory of Urban Spatial Information Engineering (Grant No. 20230111), the National Natural Science Foundation of China (Grant Nos. 41574011, 42174026 and 42374015), and the 2023 Graduate Innovation Fund Project of China University of Geosciences, Beijing (Grant No. ZD2023YC055).
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Yu, K., Shi, H., Sun, M. et al. Combined BiLSTM and ARIMA models in middle- and long-term polar motion prediction. Stud Geophys Geod 68, 25–40 (2024). https://doi.org/10.1007/s11200-023-0134-y
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DOI: https://doi.org/10.1007/s11200-023-0134-y