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.
128 related articles for article (PubMed ID: 38027671)
1. Incorporating Russo-Ukrainian war in Brent crude oil price forecasting: A comparative analysis of ARIMA, TARMA and ENNReg models. Mati S; Radulescu M; Saqib N; Samour A; Ismael GY; Aliyu N Heliyon; 2023 Nov; 9(11):e21439. PubMed ID: 38027671 [TBL] [Abstract][Full Text] [Related]
2. The impact of Russo-Ukrainian war, COVID-19, and oil prices on global food security. Al-Rousan N; Al-Najjar H; Al-Najjar D Heliyon; 2024 Apr; 10(8):e29279. PubMed ID: 38638981 [TBL] [Abstract][Full Text] [Related]
3. Forecasting value-at-risk of crude oil futures using a hybrid ARIMA-SVR-POT model. Zhang C; Zhou X Heliyon; 2024 Jan; 10(1):e23358. PubMed ID: 38163246 [TBL] [Abstract][Full Text] [Related]
4. Brent Crude Oil Price Forecast Utilizing Deep Neural Network Architectures. Daneshvar A; Ebrahimi M; Salahi F; Rahmaty M; Homayounfar M Comput Intell Neurosci; 2022; 2022():6140796. PubMed ID: 35571715 [TBL] [Abstract][Full Text] [Related]
5. Time series prediction of under-five mortality rates for Nigeria: comparative analysis of artificial neural networks, Holt-Winters exponential smoothing and autoregressive integrated moving average models. Adeyinka DA; Muhajarine N BMC Med Res Methodol; 2020 Dec; 20(1):292. PubMed ID: 33267817 [TBL] [Abstract][Full Text] [Related]
6. Impacts of crude oil market on global economy: Evidence from the Ukraine-Russia conflict via fuzzy models. Bilal M; Aamir M; Abdullah S; Khan F Heliyon; 2024 Jan; 10(1):e23874. PubMed ID: 38223738 [TBL] [Abstract][Full Text] [Related]
7. 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]
8. A CEEMD-ARIMA-SVM model with structural breaks to forecast the crude oil prices linked with extreme events. Cheng Y; Yi J; Yang X; Lai KK; Seco L Soft comput; 2022; 26(17):8537-8551. PubMed ID: 35818583 [TBL] [Abstract][Full Text] [Related]
9. 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]
10. Do OPEC+ policies help predict the oil price: A novel news-based predictor. Li J; Hong Z; Yu L; Zhang C; Ren J Heliyon; 2024 Jul; 10(14):e34437. PubMed ID: 39114019 [TBL] [Abstract][Full Text] [Related]
11. Fertility, mortality, migration, and population scenarios for 195 countries and territories from 2017 to 2100: a forecasting analysis for the Global Burden of Disease Study. Vollset SE; Goren E; Yuan CW; Cao J; Smith AE; Hsiao T; Bisignano C; Azhar GS; Castro E; Chalek J; Dolgert AJ; Frank T; Fukutaki K; Hay SI; Lozano R; Mokdad AH; Nandakumar V; Pierce M; Pletcher M; Robalik T; Steuben KM; Wunrow HY; Zlavog BS; Murray CJL Lancet; 2020 Oct; 396(10258):1285-1306. PubMed ID: 32679112 [TBL] [Abstract][Full Text] [Related]
12. Recurrent neural network architecture for forecasting banana prices in Gujarat, India. Kumari P; Goswami V; N H; Pundir RS PLoS One; 2023; 18(6):e0275702. PubMed ID: 37319281 [TBL] [Abstract][Full Text] [Related]
13. Forecasting oil commodity spot price in a data-rich environment. Boubaker S; Liu Z; Zhang Y Ann Oper Res; 2022 Oct; ():1-18. PubMed ID: 36217322 [TBL] [Abstract][Full Text] [Related]
14. 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]
15. The Russia-Saudi Arabia oil price war during the COVID-19 pandemic. Ma RR; Xiong T; Bao Y Energy Econ; 2021 Oct; 102():105517. PubMed ID: 34898736 [TBL] [Abstract][Full Text] [Related]
16. Machine learning techniques for forecasting agricultural prices: A case of brinjal in Odisha, India. Paul RK; Yeasin M; Kumar P; Kumar P; Balasubramanian M; Roy HS; Paul AK; Gupta A PLoS One; 2022; 17(7):e0270553. PubMed ID: 35793366 [TBL] [Abstract][Full Text] [Related]
17. Using LSTM and ARIMA to Simulate and Predict Limestone Price Variations. Mbah TJ; Ye H; Zhang J; Long M Min Metall Explor; 2021; 38(2):913-926. PubMed ID: 33426475 [TBL] [Abstract][Full Text] [Related]
18. Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network. Wang KW; Deng C; Li JP; Zhang YY; Li XY; Wu MC Epidemiol Infect; 2017 Apr; 145(6):1118-1129. PubMed ID: 28115032 [TBL] [Abstract][Full Text] [Related]
19. Are geopolitical threats powerful enough to predict global oil price volatility? Lee CC; Olasehinde-Williams G; Akadiri SS Environ Sci Pollut Res Int; 2021 Jun; 28(22):28720-28731. PubMed ID: 33547604 [TBL] [Abstract][Full Text] [Related]
20. Hybrid support vector regression and autoregressive integrated moving average models improved by particle swarm optimization for property crime rates forecasting with economic indicators. Alwee R; Shamsuddin SM; Sallehuddin R ScientificWorldJournal; 2013; 2013():951475. PubMed ID: 23766729 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]