Neuro-Fuzzy prediction of airborne pollen concentrations. - José Luis Aznarte; Diego Nieto Lugilde; José Manuel Benítez 001; Concepción de Linares Fernández (EUSFLAT Conf., 2005)

On the Identifiability of TSK Additive Fuzzy Rule-Based Models. - José Luis Aznarte; José Manuel Benítez 001 (SMPS, 2006)

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). - Rafael Navajas-Pérez; Manuel Ruiz Rejón; Manuel A. Garrido-Ramos; José Luis Aznarte; Cristina Rubio-Escudero (BIRD, 2007)

SatDNA Analyzer: a computing tool for satellite-DNA evolutionary analysis. - Rafael Navajas-Pérez; Cristina Rubio-Escudero; José Luis Aznarte; Manuel Ruiz Rejón; Manuel A. Garrido-Ramos (Bioinform., 2007)

Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models. - José Luis Aznarte; José Manuel Benítez Sánchez; Diego Nieto Lugilde; Concepción de Linares Fernández; Consuelo Díaz de la Guardia; Francisca Alba Sánchez (Expert Syst. Appl., 2007)

Smooth transition autoregressive models and fuzzy rule-based systems: Functional equivalence and consequences. - José Luis Aznarte; José Manuel Benítez 001; Juan Luis Castro (Fuzzy Sets Syst., 2007)

Empirical Study of Individual Feature Evaluators and Cutting Criteria for Feature Selection in Classification. - Antonio Arauzo-Azofra; José Luis Aznarte-Mellado; José Manuel Benítez 001 (ISDA, 2009)

Testing for Serial Independence of the Residuals in the Framework of Fuzzy Rule-Based Time Series Modeling. - José Luis Aznarte-Mellado; Antonio Arauzo; José Manuel Benítez Sánchez (ISDA, 2009)

Testing for Heteroskedasticity of the Residuals in Fuzzy Rule-Based Models. - José Luis Aznarte; José Manuel Benítez 001 (IEA/AIE (2), 2010)

Linearity testing for fuzzy rule-based models. - José Luis Aznarte; Marcelo C. Medeiros; José Manuel Benítez 001 (Fuzzy Sets Syst., 2010)

Testing for Remaining Autocorrelation of the residuals in the Framework of Fuzzy Rule-Based Time Series Modelling. - José Luis Aznarte; Marcelo C. Medeiros; José Manuel Benítez 001 (Int. J. Uncertain. Fuzziness Knowl. Based Syst., 2010)

Equivalences between neural-autoregressive time series models and fuzzy systems. - José Luis Aznarte; José Manuel Benítez 001 (IEEE Trans. Neural Networks, 2010)

A test for the homoscedasticity of the residuals in fuzzy rule-based forecasters. - José Luis Aznarte; Daniel Molina; Ana M. Sánchez; José Manuel Benítez 001 (Appl. Intell., 2011)

Empirical study of feature selection methods based on individual feature evaluation for classification problems. - Antonio Arauzo-Azofra; José Luis Aznarte; José Manuel Benítez 001 (Expert Syst. Appl., 2011)

Financial time series forecasting with a bio-inspired fuzzy model. - José Luis Aznarte; Jesús Alcalá-Fdez; Antonio Arauzo-Azofra; José Manuel Benítez 001 (Expert Syst. Appl., 2012)

Time Series Modeling and Forecasting Using Memetic Algorithms for Regime-Switching Models. - Christoph Bergmeir; Isaac Triguero; Daniel Molina; José Luis Aznarte; José Manuel Benítez 001 (IEEE Trans. Neural Networks Learn. Syst., 2012)

The Links between Statistical and Fuzzy Models for Time Series Analysis and Forecasting. - José Luis Aznarte; José Manuel Benítez 001 (Time Series Analysis, Modeling and Applications, 2013)

Detecting precursory patterns to enhance earthquake prediction in Chile. - Emilio Florido; Francisco Martínez-Álvarez; Antonio Morales-Esteban; Jorge Reyes; José Luis Aznarte-Mellado (Comput. Geosci., 2015)

A novel tree-based algorithm to discover seismic patterns in earthquake catalogs. - Emilio Florido; Gualberto Asencio-Cortés; José Luis Aznarte; Cristina Rubio-Escudero; Francisco Martínez-Álvarez (Comput. Geosci., 2018)

A spatio-temporal attention-based spot-forecasting framework for urban traffic prediction. - Rodrigo de Medrano; José Luis Aznarte (Appl. Soft Comput., 2020)

Probabilistic forecasting approaches for extreme NO2 episodes: a comparison of models. - Sebastián Pérez Vasseur; José Luis Aznarte (CoRR, 2020)

A Spatio-Temporal Spot-Forecasting Framework forUrban Traffic Prediction. - Rodrigo de Medrano; José Luis Aznarte (CoRR, 2020)

Shapley additive explanations for NO2 forecasting. - María Vega García; José Luis Aznarte (Ecol. Informatics, 2020)

Predicting air quality with deep learning LSTM: Towards comprehensive models. - Ricardo Navares; José Luis Aznarte (Ecol. Informatics, 2020)

Deep learning architecture to predict daily hospital admissions. - Ricardo Navares; José Luis Aznarte (Neural Comput. Appl., 2020)

Forecasting hourly ${ hbox {NO}_{2}}$ concentrations by ensembling neural networks and mesoscale models. - Damir Valput; Ricardo Navares; José Luis Aznarte (Neural Comput. Appl., 2020)

A New Spatio-Temporal Neural Network Approach for Traffic Accident Forecasting. - Rodrigo de Medrano; José Luis Aznarte (Appl. Artif. Intell., 2021)

SOCAIRE: Forecasting and monitoring urban air quality in Madrid. - Rodrigo de Medrano; Víctor de Buen Remiro; José Luis Aznarte (Environ. Model. Softw., 2021)

On the inclusion of spatial information for spatio-temporal neural networks. - Rodrigo de Medrano; José Luis Aznarte (Neural Comput. Appl., 2021)