Surrogate-assisted multi-objective evolutionary feature selection of generation-based fixed evolution control for time series forecasting with LSTM networks-Reference-Cited by-同舟云学术

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Surrogate-assisted multi-objective evolutionary feature selection of generation-based fixed evolution control for time series forecasting with LSTM networks

Published:2024-07 Issue: Volume:88 Page:101587
ISSN:2210-6502
Container-title:Swarm and Evolutionary Computation
language:en
Short-container-title:Swarm and Evolutionary Computation

Author:

Espinosa Raquel^ORCID,Jiménez Fernando^ORCID,Palma José^ORCID

Publisher

Elsevier BV

Reference68 articles.

1. Theory and Applications of Time Series Analysis and Forecasting,2023

2. Accurate medium-range global weather forecasting with 3D neural networks;Bi;Nature,2023

3. Optimal prediction intervals for macroeconomic time series using chaos and evolutionary multi-objective optimization algorithms;Sarveswararao;Swarm Evol. Comput.,2022

4. Time series prediction using deep learning methods in healthcare;Morid;ACM Trans. Manage. Inf. Syst.,2023

5. Analysis of environmental factors using AI and ML methods;Haq;Sci. Rep.,2022

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. UniBFS: A novel uniform-solution-driven binary feature selection algorithm for high-dimensional data;Swarm and Evolutionary Computation;2024-12

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