118 related articles for article (PubMed ID: 32518424)
1. A novel hybrid approach to forecast crude oil futures using intraday data.
Manickavasagam J; Visalakshmi S; Apergis N
Technol Forecast Soc Change; 2020 Sep; 158():120126. PubMed ID: 32518424
[TBL] [Abstract][Full Text] [Related]
2. Point and interval prediction of crude oil futures prices based on chaos theory and multiobjective slime mold algorithm.
Sun W; Chen H; Liu F; Wang Y
Ann Oper Res; 2022 Jun; ():1-31. PubMed ID: 35755829
[TBL] [Abstract][Full Text] [Related]
3. Crude oil prices and volatility prediction by a hybrid model based on kernel extreme learning machine.
Niu H; Zhao Y
Math Biosci Eng; 2021 Sep; 18(6):8096-8122. PubMed ID: 34814291
[TBL] [Abstract][Full Text] [Related]
4. A New Hybrid Forecasting Model Based on SW-LSTM and Wavelet Packet Decomposition: A Case Study of Oil Futures Prices.
Wang J; Wang J
Comput Intell Neurosci; 2021; 2021():7653091. PubMed ID: 34335724
[TBL] [Abstract][Full Text] [Related]
5. Hybrid IPSO-IAGA-BPNN algorithm-based rapid multi-objective optimization of a fully parameterized spaceborne primary mirror.
Qin T; Guo J; Jing Z; Han P; Qi B
Appl Opt; 2021 Apr; 60(11):3031-3043. PubMed ID: 33983197
[TBL] [Abstract][Full Text] [Related]
6. Do Gas Price and Uncertainty Indices Forecast Crude Oil Prices? Fresh Evidence Through XGBoost Modeling.
Tissaoui K; Zaghdoudi T; Hakimi A; Nsaibi M
Comput Econ; 2022 Sep; ():1-25. PubMed ID: 36157277
[TBL] [Abstract][Full Text] [Related]
7. Crude oil price forecasting based on hybridizing wavelet multiple linear regression model, particle swarm optimization techniques, and principal component analysis.
Shabri A; Samsudin R
ScientificWorldJournal; 2014; 2014():854520. PubMed ID: 24895666
[TBL] [Abstract][Full Text] [Related]
8. A New Forecasting Approach for Oil Price Using the Recursive Decomposition-Reconstruction-Ensemble Method with Complexity Traits.
Wang F; Li M; Wang R
Entropy (Basel); 2023 Jul; 25(7):. PubMed ID: 37509997
[TBL] [Abstract][Full Text] [Related]
9. Crude Oil Prices Forecast Based on Mixed-Frequency Deep Learning Approach and Intelligent Optimization Algorithm.
Lu W; Huang Z
Entropy (Basel); 2024 Apr; 26(5):. PubMed ID: 38785607
[TBL] [Abstract][Full Text] [Related]
10. A parallel integrated learning technique of improved particle swarm optimization and BP neural network and its application.
Li J; Dong X; Ruan S; Shi L
Sci Rep; 2022 Nov; 12(1):19325. PubMed ID: 36369241
[TBL] [Abstract][Full Text] [Related]
11. Forecasting carbon emissions future prices using the machine learning methods.
Shahzad U; Sengupta T; Rao A; Cui L
Ann Oper Res; 2023 Feb; ():1-32. PubMed ID: 36777411
[TBL] [Abstract][Full Text] [Related]
12. Fuzzy-based prediction of solar PV and wind power generation for microgrid modeling using particle swarm optimization.
Teferra DM; Ngoo LMH; Nyakoe GN
Heliyon; 2023 Jan; 9(1):e12802. PubMed ID: 36704286
[TBL] [Abstract][Full Text] [Related]
13. The Bi-Directional Prediction of Carbon Fiber Production Using a Combination of Improved Particle Swarm Optimization and Support Vector Machine.
Xiao C; Hao K; Ding Y
Materials (Basel); 2014 Dec; 8(1):117-136. PubMed ID: 28787927
[TBL] [Abstract][Full Text] [Related]
14. A Novel Forecasting Approach by the GA-SVR-GRNN Hybrid Deep Learning Algorithm for Oil Future Prices.
Wang L; Xia Y; Lu Y
Comput Intell Neurosci; 2022; 2022():4952215. PubMed ID: 36045986
[TBL] [Abstract][Full Text] [Related]
15. Hybrid IRBM-BPNN Approach for Error Parameter Estimation of SINS on Aircraft.
Guo W; Xian Y; Zhang D; Li B; Ren L
Sensors (Basel); 2019 Aug; 19(17):. PubMed ID: 31450626
[TBL] [Abstract][Full Text] [Related]
16. Dynamic optimization of fed-batch bioprocesses using flower pollination algorithm.
Mutturi S
Bioprocess Biosyst Eng; 2018 Nov; 41(11):1679-1696. PubMed ID: 30062602
[TBL] [Abstract][Full Text] [Related]
17. Volatility forecasting of crude oil futures based on a genetic algorithm regularization online extreme learning machine with a forgetting factor: The role of news during the COVID-19 pandemic.
Weng F; Zhang H; Yang C
Resour Policy; 2021 Oct; 73():102148. PubMed ID: 34539033
[TBL] [Abstract][Full Text] [Related]
18. A New Hybrid Model FPA-SVM Considering Cointegration for Particular Matter Concentration Forecasting: A Case Study of Kunming and Yuxi, China.
Li W; Kong D; Wu J
Comput Intell Neurosci; 2017; 2017():2843651. PubMed ID: 28932237
[TBL] [Abstract][Full Text] [Related]
19. Spatial pattern analysis and prediction of forest fire using new machine learning approach of Multivariate Adaptive Regression Splines and Differential Flower Pollination optimization: A case study at Lao Cai province (Viet Nam).
Tien Bui D; Hoang ND; Samui P
J Environ Manage; 2019 May; 237():476-487. PubMed ID: 30825780
[TBL] [Abstract][Full Text] [Related]
20. Prediction of crude oil prices in COVID-19 outbreak using real data.
Öztunç Kaymak Ö; Kaymak Y
Chaos Solitons Fractals; 2022 May; 158():111990. PubMed ID: 35291221
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
