A novel tree-based algorithm to discover seismic patterns in earthquake catalogs - Florido, E.; Asencio-Cortés, G.; Aznarte, J.L.; Rubio-Escudero, C.; Martínez-Álvarez, F. (Computers and Geosciences, 2018)

Comparing ARIMA and computational intelligence methods to forecast daily hospital admissions due to circulatory and respiratory causes in Madrid - Navares, R.; Díaz, J.; Linares, C.; Aznarte, J.L. (Stochastic Environmental Research and Risk Assessment, 2018)

Direct assessment of health impacts on hospital admission from traffic intensity in Madrid - Navares, R.; Diaz, J.; Aznarte, J.L.; Linares, C. (Environmental Research, 2020)

Modelling time series through fuzzy rule-based models: a statistical approach - Jose Luis Aznarte Mellado; José Manuel Benítez Sánchez (dir. tes.); Francisco Herrera Triguero (pres.); Francesco Marcelloni (voc.); Marcelo C, Medeiros (voc.); Óscar Cordón García (secr.); G.I. Sáinz (voc.) (2008)

Extensión mínima - Jose Luis Aznarte Mellado (2007)

Modelling time series through fuzzy rule-based models: a statiscal approach - Jose Luis Aznarte Mellado (2008)

Consideraciones éticas en torno al uso de tecnologías basadas en datos masivos en la UNED - Jose Luis Aznarte Mellado (RIED: revista iberoamericana de educación a distancia, 2020)

A spatio-temporal attention-based spot-forecasting framework for urban traffic prediction - de Medrano, R.; Aznarte, J.L. (Applied Soft Computing Journal, 2020)

Deep learning architecture to predict daily hospital admissions - Navares, R.; Aznarte, J.L. (Neural Computing and Applications, 2020)

Forecasting hourly NO 2 concentrations by ensembling neural networks and mesoscale models - Valput, D.; Navares, R.; Aznarte, J.L. (Neural Computing and Applications, 2020)

Deep learning improves taphonomic resolution: High accuracy in differentiating tooth marks made by lions and jaguars: Deep learning improves taphonomic resolution: High accuracy in differentiating tooth marks made by lions and jaguars - Jiménez-García, B.; Aznarte, J.; Abellán, N.; Baquedano, E.; Domínguez-Rodrigo, M. (Journal of the Royal Society Interface, 2020)

Precise automatic classification of 46 different pollen types with convolutional neural networks - Sevillano, V.; Holt, K.; Aznarte, J.L. (PLoS ONE, 2020)

Shapley additive explanations for NO2 forecasting - Vega García, M.; Aznarte, J.L. (Ecological Informatics, 2020)

Predicting air quality with deep learning LSTM: Towards comprehensive models - Navares, R.; Aznarte, J.L. (Ecological Informatics, 2020)

Forecasting Plantago pollen: improving feature selection through random forests, clustering, and Friedman tests - Navares, R.; Aznarte, J.L. (Theoretical and Applied Climatology, 2020)

Geographical imputation of missing poaceae pollen data via convolutional neural networks - Navares, R.; Aznarte, J.L. (Atmosphere, 2019)

Improving classification of pollen grain images of the POLEN23E dataset through three different applications of deep learning convolutional neural networks - Sevillano, V.; Aznarte, J.L. (PLoS ONE, 2018)

Predicting the Poaceae pollen season: six month-ahead forecasting and identification of relevant features - Navares, R.; Aznarte, J.L. (International Journal of Biometeorology, 2017)

What are the most important variables for Poaceae airborne pollen forecasting? - Navares, R.; Aznarte, J.L. (Science of the Total Environment, 2017)

Dynamic Line Rating Using Numerical Weather Predictions and Machine Learning: A Case Study - Aznarte, J.L.; Siebert, N. (IEEE Transactions on Power Delivery, 2017)

Probabilistic forecasting for extreme NO2 pollution episodes - Aznarte, J.L. (Environmental Pollution, 2017)

The Links between Statistical and Fuzzy Models for Time Series Analysis and Forecasting - Aznarte, J.L.; Benítez, J.M. (Intelligent Systems Reference Library, 2013)

Time series modeling and forecasting using memetic algorithms for regime-switching models - Bergmeir, C.; Triguero, I.; Molina, D.; Aznarte, J.L.; Benitez, J.M. (IEEE Transactions on Neural Networks and Learning Systems, 2012)

Financial time series forecasting with a bio-inspired fuzzy model - Aznarte, J.L.; Alcalá-Fdez, J.; Arauzo-Azofra, A.; Benítez, J.M. (Expert Systems with Applications, 2012)

Empirical study of feature selection methods based on individual feature evaluation for classification problems - Arauzo-Azofra, A.; Aznarte, J.L.; Benítez, J.M. (Expert Systems with Applications, 2011)

A test for the homoscedasticity of the residuals in fuzzy rule-based forecasters - Aznarte, J.L.; Molina, D.; Sánchez, A.M.; Benítez, J.M. (Applied Intelligence, 2011)

Fuzzy autoregressive rules: Towards linguistic time series modeling - Aznarte, J.L.; Alcalá-Fdez, J.; Arauzo, A.; Benítez, J.M. (Econometric Reviews, 2011)

Testing for heteroskedasticity of the residuals in fuzzy rule-based models - Aznarte M., J.L.; Benítez, J.M. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010)

Equivalences between neural-autoregressive time series models and fuzzy systems - Aznarte, J.L.; Benítez, J.M. (IEEE Transactions on Neural Networks, 2010)

Testing for remaining autocorrelation of the residuals in the framework of fuzzy rule-based time series modelling - Aznarte, J.L.; Medeiros, M.C.; Benítez, J.M. (International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems, 2010)

Linearity testing for fuzzy rule-based models - Aznarte, J.L.; Medeiros, M.C.; Benítez, J.M. (Fuzzy Sets and Systems, 2010)

Testing for serial independence of the residuals in the framework of fuzzy rule-based time series modeling - Aznarte M, J.L.; Arauzo, A.; Sánchez, J.M.B. (ISDA 2009 - 9th International Conference on Intelligent Systems Design and Applications, 2009)

Empirical study of individual feature evaluators and cutting criteria for feature selection in classification - Arauzo-Azofra, A.; Aznarte, J.L.M.; Benítez, J.M. (ISDA 2009 - 9th International Conference on Intelligent Systems Design and Applications, 2009)

Smooth transition autoregressive models and fuzzy rule-based systems: Functional equivalence and consequences - Aznarte M., J.L.; Manuel Benítez, J.; Luis Castro, J. (Fuzzy Sets and Systems, 2007)

Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models - Aznarte M., J.L.; Benítez Sánchez, J.M.; Lugilde, D.N.; de Linares Fernández, C.; de la Guardia, C.D.; Sánchez, F.A. (Expert Systems with Applications, 2007)

SatDNA Analyzer: A computing tool for satellite-DNA evolutionary analysis - Navajas-Pérez, R.; Rubio-Escudero, C.; Aznarte, J.L.; Rejón, M.R.; Garrido-Ramos, M.A. (Bioinformatics, 2007)

satDNA analyzer 1.2 as a valuable computing tool for evolutionary analysis of satellite-DNA families: Revisiting Y-linked satellite-DNA sequences of Rumex (Polygonaceae) - Navajas-Pérez, R.; Rejón, M.R.; Garrido-Ramos, M.; Aznarte, J.L.; Rubio-Escudero, C. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2007)

On the identifiability of TSK additive fuzzy rule-based models - Aznarte, J.L.; Benítez, J.M. (Advances in Soft Computing, 2006)

Deep Neural Architectures and Time Series: A scalable system for air quality prediction and its application - Ricardo Navares Echegaray; Jose Luis Aznarte Mellado (dir. tes.); Francisco Javier Díez Vega (pres.); Francisco Martínez Álvarez (secr.); Héctor Pomares Cintas (voc.) (2020)

Deep learning classification of tooth scores made by different carnivores: achieving high accuracy when comparing African carnivore taxa and testing the hominin shift in the balance of power - Abellán, N.; Jiménez-García, B.; Aznarte, J.; Baquedano, E.; Domínguez-Rodrigo, M. (Archaeological and Anthropological Sciences, 2021)

Reconocimiento y clasificación automatizada de especies de polen alergénicas - Ramón Gallardo Caballero; Carlos Javier García Orellana (dir. tes.); Antonio García Manso (dir. tes.); Miguel Macías Macías (pres.); Jose Luis Aznarte Mellado (secr.); Francisco Luna Valero (voc.) (2021)

Neuro-fuzzy prediction of airborne pollen concentrations - Aznarte M., J.L.; Lugilde, D.N.; Benítez, J.M.; De Linares Fernández, C. (Proceedings - 4th Conference of the European Society for Fuzzy Logic and Technology and 11th French Days on Fuzzy Logic and Applications, EUSFLAT-LFA 2005 Joint Conference, 2005)

Comparing quantile regression methods for probabilistic forecasting of NO2 pollution levels - Vasseur, S.P.; Aznarte, J.L. (Scientific Reports, 2021)

SOCAIRE: Forecasting and monitoring urban air quality in Madrid - de Medrano, R.; de Buen Remiro, V.; Aznarte, J.L. (Environmental Modelling and Software, 2021)

A New Spatio-Temporal Neural Network Approach for Traffic Accident Forecasting - de Medrano, R.; Aznarte, J.L. (Applied Artificial Intelligence, 2021)

On the inclusion of spatial information for spatio-temporal neural networks - Medrano, R.; Aznarte, J.L. (Neural Computing and Applications, 2021)

Sobre el uso de tecnologías de reconocimiento facial en la universidad: el caso de la UNED - Jose Luis Aznarte Mellado; Mariano Melendo Pardos; Juan Manuel Lacruz López (RIED: revista iberoamericana de educación a distancia, 2022)

Predicciones de tres modelos matemáticos en relación a la estrategia de vacunación frente a la COVID-19 en España. Junio de 2021 - Olmedo Luceron, Carmen; Diez Domingo, Javier; Exposito Singh, David; Moriña Soler, David; Aznarte, Jose Luis; Almagro Pedreño, Jose; Limia Sanchez, Aurora (Revista espanola de salud publica, 2022)

Spatio-temporal neural models for sustainable mobility and air quality - Rodrigo de Medrano López; Jose Luis Aznarte Mellado (dir. tes.); Luis Manuel Sarro (pres.); Cristina Rubio Escudero (secr.); Juan Gómez Romero (voc.) (2021)

Avoiding e-protoring, ensuring trust: UNED?s AvEx - Jose Luis Aznarte Mellado; Ángeles Sánchez-Elvira Paniagua; Miguel Santamaría Lancho; Llanos Tobarra; Jesús G. Boticario (Designing online assessment. Solutions that are rigorous, trusted, flexible and scalable, 2022)

Forecasting the Start and End of Pollen Season in Madrid - Navares, Ricardo; Luis Aznarte, Jose (ADVANCES IN TIME SERIES ANALYSIS AND FORECASTING, 2017)

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations - Sherratt, K.; Gruson, H.; Grah, R.; Johnson, H.; Niehus, R.; Prasse, B.; Sandmann, F.; Deuschel, J.; Wolffram, D.; Abbott, S.; Ullrich, A.; Gibson, G.; et al. (eLife, 2023)