J. Aznarte

发表

Direct assessment of health impacts on hospital admission from traffic intensity in Madrid.

C. Linares, Julio Díaz, Ricardo Navares, 2020, Environmental research.

Discovery of patterns preceding earthquakes in Chilean time series

F. Martínez-Álvarez, A. Morales-Esteban, J. Reyes, 2014 .

Forecasting Plantago pollen: improving feature selection through random forests, clustering, and Friedman tests

Ricardo Navares, J. Aznarte, 2019, Theoretical and Applied Climatology.

Linearity Testing Against a Fuzzy Rule-based Model

M. Medeiros, J. Aznarte, J. M. Sanchez, 2010 .

Fuzzy Autoregressive Rules: Towards Linguistic Time Series Modeling

J. M. Benítez, J. Alcalá-Fdez, J. Aznarte, 2011 .

COVID-19 forecasting with deep learning: a distressing survey

J. Aznarte, Luis Gutiérrez, Rodrigo de Medrano, 2021 .

What are the most important variables for Poaceae airborne pollen forecasting?

Ricardo Navares, J. Aznarte, 2017, The Science of the total environment.

Predicting the Poaceae pollen season: six month-ahead forecasting and identification of relevant features

Ricardo Navares, José Luis Aznarte, Ricardo Navares, 2017, International Journal of Biometeorology.

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)

Cristina Rubio-Escudero, José Luis Aznarte, Rafael Navajas-Pérez, 2007, BIRD.

SatDNA Analyzer: a computing tool for satellite-DNA evolutionary analysis

Cristina Rubio-Escudero, José Luis Aznarte, Rafael Navajas-Pérez, 2007, Bioinform..

Dynamic Line Rating Using Numerical Weather Predictions and Machine Learning: A Case Study

Nils Siebert, José L. Aznarte, N. Siebert, 2017, IEEE Transactions on Power Delivery.

Time Series Modeling and Forecasting Using Memetic Algorithms for Regime-Switching Models

Daniel Molina, José Manuel Benítez, Isaac Triguero, 2012, IEEE Transactions on Neural Networks and Learning Systems.

Financial time series forecasting with a bio-inspired fuzzy model

Jesús Alcalá-Fdez, José Manuel Benítez, Antonio Arauzo-Azofra, 2012, Expert Syst. Appl..

A test for the homoscedasticity of the residuals in fuzzy rule-based forecasters

Ana M. Sánchez, Daniel Molina, José Manuel Benítez, 2011, Applied Intelligence.

A Spatio-Temporal Spot-Forecasting Framework forUrban Traffic Prediction

Rodrigo de Medrano, Jos'e L. Aznarte, J. Aznarte, 2020, ArXiv.

Empirical study of feature selection methods based on individual feature evaluation for classification problems

José Manuel Benítez, Antonio Arauzo-Azofra, José Luis Aznarte, 2011, Expert Syst. Appl..

Shapley additive explanations for NO2 forecasting

José Luis Aznarte, María Vega García, J. Aznarte, 2020, Ecol. Informatics.

Improving classification of pollen grain images of the POLEN23E dataset through three different applications of deep learning convolutional neural networks

Víctor Sevillano, José L Aznarte, J. Aznarte, 2018, PloS one.

Predicting air quality with deep learning LSTM: Towards comprehensive models

Ricardo Navares, José Luis Aznarte, Ricardo Navares, 2020, Ecol. Informatics.

Comparing quantile regression methods for probabilistic forecasting of NO2 pollution levels

J. Aznarte, Sebastien Pérez Vasseur, 2021, Scientific Reports.

Probabilistic forecasting approaches for extreme NO2 episodes: a comparison of models pdf

Sebasti'an P'erez Vasseur, Jos'e L. Aznarte, J. Aznarte, 2020, ArXiv.

Deep learning architecture to predict daily hospital admissions

Ricardo Navares, J. Aznarte, 2020, Neural Computing and Applications.

Deep learning architecture to predict daily hospital admissions

Ricardo Navares, José L. Aznarte, Ricardo Navares, 2020, Neural Computing and Applications.

On the Identifiability of TSK Additive Fuzzy Rule-Based Models

José Manuel Benítez, José Luis Aznarte, J. M. Benítez, 2006, SMPS.

Precise automatic classification of 46 different pollen types with convolutional neural networks

José Luis Aznarte, Katherine A. Holt, Víctor Sevillano, 2020, bioRxiv.

Testing for Remaining Autocorrelation of the residuals in the Framework of Fuzzy Rule-Based Time Series Modelling

José Manuel Benítez, Marcelo C. Medeiros, José Luis Aznarte, 2010, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

On the inclusion of spatial information for spatio-temporal neural networks

Rodrigo de Medrano, Jos'e L. Aznarte, J. Aznarte, 2020, Neural Computing and Applications.

Deep learning improves taphonomic resolution: high accuracy in differentiating tooth marks made by lions and jaguars

Manuel Domínguez-Rodrigo, Blanca Jiménez-García, José Aznarte, 2020, Journal of the Royal Society Interface.

Geographical Imputation of Missing Poaceae Pollen Data via Convolutional Neural Networks

Ricardo Navares, José Luis Aznarte, Ricardo Navares, 2019, Atmosphere.

A novel tree-based algorithm to discover seismic patterns in earthquake catalogs

Francisco Martínez-Álvarez, Gualberto Asencio-Cortés, José Luis Aznarte, 2018, Comput. Geosci..

Linearity testing for fuzzy rule-based models

José Manuel Benítez, Marcelo C. Medeiros, José Luis Aznarte, 2010, Fuzzy Sets Syst..

Smooth transition autoregressive models and fuzzy rule-based systems: Functional equivalence and consequences

José Manuel Benítez, Juan Luis Castro, José Luis Aznarte, 2007, Fuzzy Sets Syst..

Earthquake magnitude prediction based on artificial neural networks: A survey

Francisco Martínez-Álvarez, Antonio Morales-Esteban, José Luis Aznarte, 2016 .

Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models

José Manuel Benítez, José Luis Aznarte, Diego Nieto Lugilde, 2007, Expert Syst. Appl..

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

E. Baquedano, M. Domínguez‐Rodrigo, J. Aznarte, 2021, Archaeological and Anthropological Sciences.

Comparing ARIMA and computational intelligence methods to forecast daily hospital admissions due to circulatory and respiratory causes in Madrid

Ricardo Navares, Julio Díaz, Cristina Linares, 2018, Stochastic Environmental Research and Risk Assessment.

A New Spatio-Temporal Neural Network Approach for Traffic Accident Forecasting

José Luis Aznarte, Rodrigo de Medrano, J. Aznarte, 2021, Appl. Artif. Intell..

A spatio-temporal attention-based spot-forecasting framework for urban traffic prediction

Rodrigo de Medrano, J. Aznarte, 2020, Appl. Soft Comput..

Photovoltaic Forecasting: A state of the art

Robin Girard, Georges Kariniotakis, Bella Espinar, 2010 .

SOCAIRE: Forecasting and Monitoring Urban Air Quality in Madrid pdf

Jos'e L. Aznarte, Rodrigo de Medrano, V'ictor de Buen Remiro, 2020, Environ. Model. Softw..

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Pierfrancesco Alaimo Di Loro, Alai, Sebastian B. Mohr, 2022, medRxiv.