28 citations to https://www.mathnet.ru/rus/at2244
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Jumin Zhao, Qimei Wang, Fanming Wu, Hairong Jiang, Dengao Li, “FVMD-HTLD: Prediction of TEC based on Signal decomposition and integrated models”, Advances in Space Research, 2025
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Nabi Taheri, Mauro Tucci, “Enhancing Regional Wind Power Forecasting through Advanced Machine-Learning and Feature-Selection Techniques”, Energies, 17:21 (2024), 5431
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Ran Dong, Soichiro Ikuno, “Biomechanical Analysis of Golf Swing Motion Using Hilbert–Huang Transform”, Sensors, 23:15 (2023), 6698
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Xiaodi Liang, Suofang Wang, Wenjie Shen, “Random Forest Model of Flow Pattern Identification in Scavenge Pipe Based on EEMD and Hilbert Transform”, Energies, 16:16 (2023), 6084
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Pampa Sinha, Kaushik Paul, Chidurala Saiprakash, Almoataz Y. Abdelaziz, Ahmed I. Omar, Chun-Lien Su, Mahmoud Elsisi, “Identification of Cross-Country Fault with High Impedance Syndrome in Transmission Line Using Tunable Q Wavelet Transform”, Mathematics, 11:3 (2023), 586
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Dang S., Peng L., Zhao J., Li J., Kong Zh., “A Quantile Regression Random Forest-Based Short-Term Load Probabilistic Forecasting Method”, Energies, 15:2 (2022), 663
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Anfeng Zhu, Qiancheng Zhao, Xian Wang, Ling Zhou, “Ultra-Short-Term Wind Power Combined Prediction Based on Complementary Ensemble Empirical Mode Decomposition, Whale Optimisation Algorithm, and Elman Network”, Energies, 15:9 (2022), 3055
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Shen Ya., Wang P., Wang X., Sun K., “Application of Empirical Mode Decomposition and Extreme Learning Machine Algorithms on Prediction of the Surface Vibration Signal”, Energies, 14:22 (2021), 7519
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Leung T., Zhao T., “Financial Time Series Analysis and Forecasting With Hilbert-Huang Transform Feature Generation and Machine Learning”, Appl. Stoch. Models. Bus. Ind., 37:6 (2021), 993–1016
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Jiabao Du, Changxi Yue, Ying Shi, Jicheng Yu, Fan Sun, Changjun Xie, Tao Su, “A Frequency Decomposition-Based Hybrid Forecasting Algorithm for Short-Term Reactive Power”, Energies, 14:20 (2021), 6606