José Luis Aznarte
José Luis Aznarte
Associate Professor. Artificial Intelligence Department, UNED.
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Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models
JL Aznarte, D Nieto-Lugilde, C de Linares Fernández, CD de la Guardia, ...
Expert Systems with Applications 32 (4), 1218-1225, 2007
Empirical study of feature selection methods based on individual feature evaluation for classification problems
A Arauzo-Azofra, JL Aznarte, JM Benítez
Expert Systems with Applications 38 (7), 8170-8177, 2011
Dynamic line rating using numerical weather predictions and machine learning: A case study
JL Aznarte, N Siebert
IEEE Transactions on Power Delivery 32 (1), 335-343, 2016
Smooth transition autoregressive models and fuzzy rule-based systems: Functional equivalence and consequences
JL Aznarte, JM Benítez, JL Castro
Fuzzy sets and systems 158 (24), 2734-2745, 2007
Photovoltaic Forecasting: A state of the art
B Espinar, JL Aznarte, R Girard, AM Moussa, G Kariniotakis
5th European PV-Hybrid and Mini-Grid Conference, Pages 250-255-ISBN 978-3 …, 2010
Financial time series forecasting with a bio-inspired fuzzy model
JL Aznarte, J Alcalá-Fdez, A Arauzo-Azofra, JM Benítez
Expert Systems with Applications 39 (16), 12302-12309, 2012
SatDNA Analyzer: a computing tool for satellite-DNA evolutionary analysis
R Navajas-Pérez, C Rubio-Escudero, JL Aznarte, MR Rejón, ...
Bioinformatics 23 (6), 767-768, 2007
Equivalences between neural-autoregressive time series models and fuzzy systems
JL Aznarte, JM Benítez
IEEE transactions on neural networks 21 (9), 1434-1444, 2010
Earthquake magnitude prediction based on artificial neural networks: A survey
E Florido, JL Aznarte, A Morales-Esteban, F Martínez-Álvarez
Croatian Operational Research Review, 159-169, 2016
Time series modeling and forecasting using memetic algorithms for regime-switching models
C Bergmeir, I Triguero, D Molina, JL Aznarte, JM Benítez
IEEE transactions on neural networks and learning systems 23 (11), 1841-1847, 2012
Probabilistic forecasting for extreme NO2 pollution episodes
JL Aznarte
Environmental Pollution 229, 321-328, 2017
Predicting the Poaceae pollen season: six month-ahead forecasting and identification of relevant features
R Navares, JL Aznarte
International journal of biometeorology 61 (4), 647-656, 2017
Improving classification of pollen grain images of the POLEN23E dataset through three different applications of deep learning convolutional neural networks
V Sevillano, JL Aznarte
PloS one 13 (9), e0201807, 2018
Comparing ARIMA and computational intelligence methods to forecast daily hospital admissions due to circulatory and respiratory causes in Madrid
R Navares, J Díaz, C Linares, JL Aznarte
Stochastic Environmental Research and Risk Assessment 32 (10), 2849-2859, 2018
Linearity testing for fuzzy rule-based models
JL Aznarte, MC Medeiros, JM Benítez
Fuzzy Sets and Systems 161 (13), 1836-1851, 2010
A novel tree-based algorithm to discover seismic patterns in earthquake catalogs
E Florido, G Asencio–Cortés, JL Aznarte, C Rubio-Escudero, ...
Computers & Geosciences 115, 96-104, 2018
What are the most important variables for Poaceae airborne pollen forecasting?
R Navares, JL Aznarte
Science of The Total Environment 579, 1161-1169, 2017
A test for the homoscedasticity of the residuals in fuzzy rule-based forecasters
JL Aznarte, D Molina, AM Sánchez, JM Benítez
Applied Intelligence 34 (3), 386-393, 2011
Testing for remaining autocorrelation of the residuals in the framework of fuzzy rule-based time series modelling
JL Aznarte, MC Medeiros, JM Benitez
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems …, 2010
Modelling time series through fuzzy rule-based models: a statistical approach
JL Aznarte
Ph. D. thesis, Universidad de Granada, 2008
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