25 citations to https://www.mathnet.ru/rus/at9099
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Yichen Lv, Mingyun Gao, Xinping Xiao, “Unbiased forecasting of seasonal wind power generation based on a novel seasonal multivariable grey model”, Renewable Energy, 258 (2026), 124952
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Yuanyuan Yao, Yuhan Shi, Lu Chen, Ziquan Fang, Yunjun Gao, Leong Hou U, Yushuai Li, Tianyi Li, “Moon: A Modality Conversion-Based Efficient Multivariate Time Series Anomaly Detection”, IEEE Trans. Knowl. Data Eng., 38:1 (2026), 457
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Yuxuan Luo, Xianglan Meng, Yutong Zhai, Dongqing Zhang, Kaiping Ma, “Prediction of Water Quality in Agricultural Watersheds Based on VMD-GA-LSTM Model”, Mathematics, 13:12 (2025), 1951
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Yunxuan Dong, Binggui Zhou, Hongcai Zhang, Guanghua Yang, Shaodan Ma, “A deep time-frequency augmented wind power forecasting model”, Renewable Energy, 2025, 123550
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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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He M., Li W., Via B.K., Zhang Ya., “P Nowcasting of Lumber Futures Price With Google Trends Index Using Machine Learning and Deep Learning Models”, For. Prod. J., 72:1 (2022), 11–20
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Tim Leung, Theodore Zhao, “Adaptive complementary ensemble EMD and energy-frequency spectra of cryptocurrency prices”, J. Finan. Eng., 09:01 (2022)
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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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Acaroglu H., Garcia Marquez F.P., “Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy”, Energies, 14:22 (2021), 7473
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Pourdaryaei A., Mohammadi M., Karimi M., Mokhlis H., Illias H.A., Kaboli Seyed Hamidreza Aghay, Ahmad Sh., “Recent Development in Electricity Price Forecasting Based on Computational Intelligence Techniques in Deregulated Power Market”, Energies, 14:19 (2021), 6104