These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

143 related articles for article (PubMed ID: 38321281)

  • 1. Forecasting China carbon price using an error-corrected secondary decomposition hybrid model integrated fuzzy dispersion entropy and deep learning paradigm.
    Yun P; Zhou Y; Liu C; Wu Y; Pan D
    Environ Sci Pollut Res Int; 2024 Mar; 31(11):16530-16553. PubMed ID: 38321281
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Predicting regional carbon price in China based on multi-factor HKELM by combining secondary decomposition and ensemble learning.
    Hu B; Cheng Y
    PLoS One; 2023; 18(12):e0285311. PubMed ID: 38085727
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Breaking through the limitation of carbon price forecasting: A novel hybrid model based on secondary decomposition and nonlinear integration.
    Lan Y; Huangfu Y; Huang Z; Zhang C
    J Environ Manage; 2024 Jun; 362():121253. PubMed ID: 38823294
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Carbon price prediction for China's ETS pilots using variational mode decomposition and optimized extreme learning machine.
    Chai S; Zhang Z; Zhang Z
    Ann Oper Res; 2021 Nov; ():1-22. PubMed ID: 34812214
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Carbon price forecasting using multiscale nonlinear integration model coupled optimal feature reconstruction with biphasic deep learning.
    Wang J; Cheng Q; Sun X
    Environ Sci Pollut Res Int; 2022 Dec; 29(57):85988-86004. PubMed ID: 34453680
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A carbon price hybrid forecasting model based on data multi-scale decomposition and machine learning.
    Yang P; Wang Y; Zhao S; Chen Z; Li Y
    Environ Sci Pollut Res Int; 2023 Jan; 30(2):3252-3269. PubMed ID: 35943654
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Carbon price prediction using multiple hybrid machine learning models optimized by genetic algorithm.
    Nadirgil O
    J Environ Manage; 2023 Sep; 342():118061. PubMed ID: 37201388
    [TBL] [Abstract][Full Text] [Related]  

  • 8. An innovative random forest-based nonlinear ensemble paradigm of improved feature extraction and deep learning for carbon price forecasting.
    Wang J; Sun X; Cheng Q; Cui Q
    Sci Total Environ; 2021 Mar; 762():143099. PubMed ID: 33127140
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Multi level perspectives in stock price forecasting: ICE2DE-MDL.
    Akşehir ZD; Kılıç E
    PeerJ Comput Sci; 2024; 10():e2125. PubMed ID: 38983197
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Carbon price prediction based on multiple decomposition and XGBoost algorithm.
    Xu K; Xia Z; Cheng M; Tan X
    Environ Sci Pollut Res Int; 2023 Aug; 30(38):89165-89179. PubMed ID: 37442936
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition.
    Zhang X; Zhang Q; Zhang G; Nie Z; Gui Z; Que H
    Int J Environ Res Public Health; 2018 May; 15(5):. PubMed ID: 29883381
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Forecasting carbon futures price: a hybrid method incorporating fuzzy entropy and extreme learning machine.
    Chen P; Vivian A; Ye C
    Ann Oper Res; 2022; 313(1):559-601. PubMed ID: 35002000
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Carbon price forecasting: a novel deep learning approach.
    Zhang F; Wen N
    Environ Sci Pollut Res Int; 2022 Aug; 29(36):54782-54795. PubMed ID: 35306656
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A novel hybrid learning paradigm with feature extraction for carbon price prediction based on Bi-directional long short-term memory network optimized by an improved sparrow search algorithm.
    Zhou J; Xu Z; Wang S
    Environ Sci Pollut Res Int; 2022 Sep; 29(43):65585-65598. PubMed ID: 35488159
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Point and interval forecasting for carbon trading price: a case of 8 carbon trading markets in China.
    Zhao Y; Zhao H; Li B; Wu B; Guo S
    Environ Sci Pollut Res Int; 2023 Apr; 30(17):49075-49096. PubMed ID: 36763267
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Forecasting carbon prices in China's pilot carbon market: A multi-source information approach with conditional generative adversarial networks.
    Huang Z; Zhang W
    J Environ Manage; 2024 May; 359():120967. PubMed ID: 38723494
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A CNN-LSTM based deep learning model with high accuracy and robustness for carbon price forecasting: A case of Shenzhen's carbon market in China.
    Shi H; Wei A; Xu X; Zhu Y; Hu H; Tang S
    J Environ Manage; 2024 Feb; 352():120131. PubMed ID: 38266520
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A novel carbon price forecasting method based on model matching, adaptive decomposition, and reinforcement learning ensemble strategy.
    Cao Z; Liu H
    Environ Sci Pollut Res Int; 2023 Mar; 30(13):36044-36067. PubMed ID: 36539662
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Forecasting Carbon Price in China: A Multimodel Comparison.
    Li H; Huang X; Zhou D; Cao A; Su M; Wang Y; Guo L
    Int J Environ Res Public Health; 2022 May; 19(10):. PubMed ID: 35627753
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A Deep Learning Model with Signal Decomposition and Informer Network for Equipment Vibration Trend Prediction.
    Wang H; Guo M; Tian L
    Sensors (Basel); 2023 Jun; 23(13):. PubMed ID: 37447674
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.