Nickel Price Forecast Based on the LSTM Neural Network Optimized by the Improved PSO Algorithm
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DOI: 10.1155/2019/1934796
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Cited by:
- He, Wenhua & Liu, Pei & Lin, Borong & Zhou, Hao & Chen, Xuesheng, 2022. "Green finance support for development of green buildings in China: Effect, mechanism, and policy implications," Energy Policy, Elsevier, vol. 165(C).
- Ozdemir, Ali Can & Buluş, Kurtuluş & Zor, Kasım, 2022. "Medium- to long-term nickel price forecasting using LSTM and GRU networks," Resources Policy, Elsevier, vol. 78(C).
- Miranda, Matheus H.R. & Silva, Fabrício L. & Lourenço, Maria A.M. & Eckert, Jony J. & Silva, Ludmila C.A., 2023. "Particle swarm optimization of Elman neural network applied to battery state of charge and state of health estimation," Energy, Elsevier, vol. 285(C).
- Changxia Sun & Menghao Pei & Bo Cao & Saihan Chang & Haiping Si, 2023. "A Study on Agricultural Commodity Price Prediction Model Based on Secondary Decomposition and Long Short-Term Memory Network," Agriculture, MDPI, vol. 14(1), pages 1-22, December.
- Che, Zhongyuan & Peng, Chong & Yue, Chenxiao, 2024. "Optimizing LSTM with multi-strategy improved WOA for robust prediction of high-speed machine tests data," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
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