152 related articles for article (PubMed ID: 26539722)
21. Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans.
Zhou L; Xia J; Yu L; Wang Y; Shi Y; Cai S; Nie S
Int J Environ Res Public Health; 2016 Mar; 13(4):355. PubMed ID: 27023573
[TBL] [Abstract][Full Text] [Related]
22. An ensemble approach based on transformation functions for natural gas price forecasting considering optimal time delays.
Saghi F; Jahangoshai Rezaee M
PeerJ Comput Sci; 2021; 7():e409. PubMed ID: 33954228
[TBL] [Abstract][Full Text] [Related]
23. A comparative study of autoregressive neural network hybrids.
Taskaya-Temizel T; Casey MC
Neural Netw; 2005; 18(5-6):781-9. PubMed ID: 16085389
[TBL] [Abstract][Full Text] [Related]
24. Artificial intelligence approach with the use of artificial neural networks for the creation of a forecasting model of Plasmopara viticola infection.
Bugliosi R; Spera G; La Torre A; Campoli L; Scaglione M
Commun Agric Appl Biol Sci; 2006; 71(3 Pt A):859-65. PubMed ID: 17390832
[TBL] [Abstract][Full Text] [Related]
25. Multiscaled Neural Autoregressive Distributed Lag: A New Empirical Mode Decomposition Model for Nonlinear Time Series Forecasting.
Saâdaoui F; Messaoud OB
Int J Neural Syst; 2020 Aug; 30(8):2050039. PubMed ID: 32588684
[TBL] [Abstract][Full Text] [Related]
26. Forecasting daily patient volumes in the emergency department.
Jones SS; Thomas A; Evans RS; Welch SJ; Haug PJ; Snow GL
Acad Emerg Med; 2008 Feb; 15(2):159-70. PubMed ID: 18275446
[TBL] [Abstract][Full Text] [Related]
27. Time series autoregressive integrated moving average modeling of test-day milk yields of dairy ewes.
Macciotta NP; Cappio-Borlino A; Pulina G
J Dairy Sci; 2000 May; 83(5):1094-103. PubMed ID: 10821585
[TBL] [Abstract][Full Text] [Related]
28. Prediction of programmed-temperature retention values of naphthas by artificial neural networks.
Qi JH; Zhang XY; Zhang RS; Liu MC; Hu ZD; Xue HF; Fan BT
SAR QSAR Environ Res; 2000; 11(2):117-31. PubMed ID: 10877473
[TBL] [Abstract][Full Text] [Related]
29. Two Different Points of View through Artificial Intelligence and Vector Autoregressive Models for Ex Post and Ex Ante Forecasting.
Aydin AD; Caliskan Cavdar S
Comput Intell Neurosci; 2015; 2015():409361. PubMed ID: 26550010
[TBL] [Abstract][Full Text] [Related]
30. Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends.
Lu WZ; Wang WJ
Chemosphere; 2005 Apr; 59(5):693-701. PubMed ID: 15792667
[TBL] [Abstract][Full Text] [Related]
31. A Hybrid Model for Forecasting Sunspots Time Series Based on Variational Mode Decomposition and Backpropagation Neural Network Improved by Firefly Algorithm.
Li G; Ma X; Yang H
Comput Intell Neurosci; 2018; 2018():3713410. PubMed ID: 30405707
[TBL] [Abstract][Full Text] [Related]
32. Forecasting stock prices with long-short term memory neural network based on attention mechanism.
Qiu J; Wang B; Zhou C
PLoS One; 2020; 15(1):e0227222. PubMed ID: 31899770
[TBL] [Abstract][Full Text] [Related]
33. Intercomparison of air quality data using principal component analysis, and forecasting of PM₁₀ and PM₂.₅ concentrations using artificial neural networks, in Thessaloniki and Helsinki.
Voukantsis D; Karatzas K; Kukkonen J; Räsänen T; Karppinen A; Kolehmainen M
Sci Total Environ; 2011 Mar; 409(7):1266-76. PubMed ID: 21276603
[TBL] [Abstract][Full Text] [Related]
34. Using the R-MAPE index as a resistant measure of forecast accuracy.
Montaño Moreno JJ; Palmer Pol A; Sesé Abad A; Cajal Blasco B
Psicothema; 2013; 25(4):500-6. PubMed ID: 24124784
[TBL] [Abstract][Full Text] [Related]
35. Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks.
Maca P; Pech P
Comput Intell Neurosci; 2016; 2016():3868519. PubMed ID: 26880875
[TBL] [Abstract][Full Text] [Related]
36. 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]
37. A perturbative approach for enhancing the performance of time series forecasting.
de Mattos Neto PS; Ferreira TA; Lima AR; Vasconcelos GC; Cavalcanti GD
Neural Netw; 2017 Apr; 88():114-124. PubMed ID: 28236678
[TBL] [Abstract][Full Text] [Related]
38. 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]
39. Application of empirical mode decomposition with local linear quantile regression in financial time series forecasting.
Jaber AM; Ismail MT; Altaher AM
ScientificWorldJournal; 2014; 2014():708918. PubMed ID: 25140343
[TBL] [Abstract][Full Text] [Related]
40. Self-organising mixture autoregressive model for non-stationary time series modelling.
Ni H; Yin H
Int J Neural Syst; 2008 Dec; 18(6):469-80. PubMed ID: 19145663
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]