Electricity Consumption Time Series Forecasting Using Temporal Convolutional Networks. - José F. Torres; M. J. Jiménez-Navarro; Francisco Martínez-Álvarez; Alicia Troncoso (CAEPIA, 2021)

A Model-Based Deep Transfer Learning Algorithm for Phenology Forecasting Using Satellite Imagery. - M. Á. Molina; M. J. Jiménez-Navarro; Francisco Martínez-Álvarez; Gualberto Asencio-Cortés (HAIS, 2021)

HLNet: A Novel Hierarchical Deep Neural Network for Time Series Forecasting. - M. J. Jiménez-Navarro; Francisco Martínez-Álvarez; Alicia Troncoso; Gualberto Asencio-Cortés (SOCO, 2021)

Feature-Aware Drop Layer (FADL): A Nonparametric Neural Network Layer for Feature Selection. - Manuel Jesús Jiménez-Navarro; María Martínez-Ballesteros; Isabel Sofia Sousa Brito; Francisco Martínez-Álvarez; Gualberto Asencio-Cortés (SOCO, 2022)

DIAFAN-TL: An instance weighting-based transfer learning algorithm with application to phenology forecasting. - Miguel Angel Molina-Cabanillas; M. J. Jiménez-Navarro; Ricardo Arjona; Francisco Martínez-Álvarez; Gualberto Asencio-Cortés (Knowl. Based Syst., 2022)

Association Rule Analysis of Student Satisfaction Surveys for Teaching Quality Evaluation. - Manuel Jesús Jiménez-Navarro; Belén Vega-Márquez; José María Luna-Romera; Manuel Carranza-García; María Martínez-Ballesteros (CISIS-ICEUTE, 2023)

Embedded Temporal Feature Selection for Time Series Forecasting Using Deep Learning. - M. J. Jiménez-Navarro; María Martínez-Ballesteros; Francisco Martínez-Álvarez; Gualberto Asencio-Cortés (IWANN (2), 2023)

A bioinspired ensemble approach for multi-horizon reference evapotranspiration forecasting in Portugal. - Manuel Jesús Jiménez-Navarro; María Martínez-Ballesteros; Isabel Sofia Brito; Francisco Martínez-Álvarez; Gualberto Asencio-Cortés (SAC, 2023)

Feature Selection Guided by CVOA Metaheuristic for Deep Neural Networks: Application to Multivariate Time Series Forecasting. - Manuel Jesús Jiménez-Navarro; Camilo Restrepo Estrada; Laura Melgar-García; David Gutiérrez-Avilés (SOCO (1), 2023)

Explaining Learned Patterns in Deep Learning by Association Rules Mining. - M. J. Jiménez-Navarro; María Martínez-Ballesteros; Francisco Martínez-Álvarez; Gualberto Asencio-Cortés (SOCO (2), 2023)

PHILNet: A novel efficient approach for time series forecasting using deep learning. - M. J. Jiménez-Navarro; María Martínez-Ballesteros; Francisco Martínez-Álvarez; Gualberto Asencio-Cortés (Inf. Sci., 2023)

A new deep learning architecture with inductive bias balance for transformer oil temperature forecasting. - Manuel Jesús Jiménez-Navarro; María Martínez-Ballesteros; Francisco Martínez-Álvarez; Gualberto Asencio-Cortés (J. Big Data, 2023)

Toward Explaining Competitive Success in League of Legends: A Machine Learning Analysis. - Francisco Javier Galán-Sales; Maria Lourdes Linares-Barrera; Pablo Reina-Jiménez; Ana Rodríguez-López; Manuel Jesús Jiménez-Navarro (CAEPIA, 2024)

Multi-Objective Lagged Feature Selection Based on Dependence Coefficient for Time-Series Forecasting. - Maria Lourdes Linares-Barrera; Manuel Jesús Jiménez-Navarro; José C. Riquelme; María Martínez-Ballesteros (CAEPIA, 2024)

Ground-Level Ozone Forecasting Using Explainable Machine Learning. - Angela del Robledo Troncoso-García; Manuel Jesús Jiménez-Navarro; Francisco Martínez-Álvarez; Alicia Troncoso (CAEPIA, 2024)

Evolutionary Feature Selection for Time-Series Forecasting. - Maria Lourdes Linares-Barrera; Manuel Jesús Jiménez-Navarro; Isabel Sofia Brito; José C. Riquelme; María Martínez-Ballesteros (SAC, 2024)

Time Series Forecasting in Agriculture: Explainable Deep Learning with Lagged Feature Selection. - A. R. Troncoso-García; M. J. Jiménez-Navarro; Maria Lourdes Linares-Barrera; Isabel Sofia Brito; Francisco Martínez-Álvarez; María Martínez-Ballesteros (SOCO (1), 2024)

Explaining deep learning models for ozone pollution prediction via embedded feature selection. - M. J. Jiménez-Navarro; María Martínez-Ballesteros; Francisco Martínez-Álvarez; Gualberto Asencio-Cortés (Appl. Soft Comput., 2024)

From simple to complex: a sequential method for enhancing time series forecasting with deep learning. - M. J. Jiménez-Navarro; María Martínez-Ballesteros; Francisco Martínez-Álvarez; Alicia Troncoso; Gualberto Asencio-Cortés (Log. J. IGPL, 2024)

MetaGen: A framework for metaheuristic development and hyperparameter optimization in machine and deep learning. - David Gutiérrez-Avilés; Manuel Jesús Jiménez-Navarro; José Francisco Torres; Francisco Martínez-Álvarez (Neurocomputing, 2025)

A real-time machine learning-based methodology for short-term frequency nadir prediction in low-inertia power systems. - Manuel Jesús Jiménez-Navarro; Jose Miguel Riquelme-Dominguez; Manuel Carranza-García; Francisco M. González-Longatt (Neurocomputing, 2025)