Version 1
: Received: 23 October 2023 / Approved: 23 October 2023 / Online: 24 October 2023 (04:06:59 CEST)
How to cite:
Chen, H.; He, X.; Lu, T.; Huan, J.; Sun, X. An Improved VMD–EEMD–LSTM Time Series Hybrid Prediction Model for Sea Surface Height. Preprints2023, 2023101457. https://doi.org/10.20944/preprints202310.1457.v1
Chen, H.; He, X.; Lu, T.; Huan, J.; Sun, X. An Improved VMD–EEMD–LSTM Time Series Hybrid Prediction Model for Sea Surface Height. Preprints 2023, 2023101457. https://doi.org/10.20944/preprints202310.1457.v1
Chen, H.; He, X.; Lu, T.; Huan, J.; Sun, X. An Improved VMD–EEMD–LSTM Time Series Hybrid Prediction Model for Sea Surface Height. Preprints2023, 2023101457. https://doi.org/10.20944/preprints202310.1457.v1
APA Style
Chen, H., He, X., Lu, T., Huan, J., & Sun, X. (2023). An Improved VMD–EEMD–LSTM Time Series Hybrid Prediction Model for Sea Surface Height. Preprints. https://doi.org/10.20944/preprints202310.1457.v1
Chicago/Turabian Style
Chen, H., Jiahui Huan and Xiwen Sun. 2023 "An Improved VMD–EEMD–LSTM Time Series Hybrid Prediction Model for Sea Surface Height" Preprints. https://doi.org/10.20944/preprints202310.1457.v1
Abstract
Changes in sea level exhibit nonlinearity, nonstationarity, and multivariable characteristics, making traditional time series forecasting methods less effective in producing satisfactory results. To enhance the accuracy of the predictions of changes in sea level, this study introduced an improved VMD–EEMD–LSTM hybrid model. This model decomposes satellite altimetry data from near the Dutch coast using VMD, resulting in components of the Intrinsic Mode Function (IMF) with various frequencies and a residual sequence. EEMD further dissects the residual sequence obtained from VMD into second-order components. These IMFs decomposed by VMD and EEMD are utilized as features in the LSTM model for making predictions, culminating in the final forecasted results. The experimental results demonstrated significant improvements in the predictive performance compared with the VMD–LSTM model. The RMSE (root mean square error) decreased by an average of 58.68%, the MAE (mean absolute error) reduced by an average of 59.96%, and the R2 (coefficient of determination) increased by an average of 49.85% compared with the VMD–LSTM model. In comparison with the EEMD–LSTM model, the RMSE decreased by an average of 26.95%, the MAE decreased by an average of 28.00%, and the R2 increased by an average of 6.53%. The VMD–EEMD–LSTM model exhibited significantly improved predictive performance.
Keywords
sea level change; deep learning; time series prediction; VMD; EEMD; LSTM
Subject
Environmental and Earth Sciences, Oceanography
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
