@article{10.1016/j.asoc.2024.111504,

title = {Explaining deep learning models for ozone pollution prediction via embedded feature selection},

author = {M. J. Jiménez-Navarro and M. Martínez-Ballesteros and F. Martínez-Álvarez and G. Asencio-Cortés},

url = {https://doi.org/10.1016/j.asoc.2024.111504},

doi = {10.1016/j.asoc.2024.111504},

year  = {2024},

date = {2024-01-01},

journal = {Applied Soft Computing},

volume = {157},

pages = {111504},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1109/sst61991.2024.10755283,

title = {Explainable Deep Learning with Embedded Feature Selection for Electricity Demand Forecasting},

author = {M. J. Jiménez-Navarro and A. R. Troncoso-García and A. Troncoso and F. Martínez-Álvarez and M. Martínez-Ballesteros},

url = {https://doi.org/10.1109/sst61991.2024.10755283},

doi = {10.1109/sst61991.2024.10755283},

year  = {2024},

date = {2024-01-01},

journal = {2024 International Conference on Smart Systems and Technologies (SST)},

volume = {N/A},

pages = {153-158},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1016/j.rineng.2024.103290,

title = {Explainable deep learning on multi-target time series forecasting: An air pollution use case},

author = {Manuel J. Jiménez-Navarro and Mario Lovrić and Simonas Kecorius and Emmanuel Karlo Nyarko and María Martínez-Ballesteros},

url = {https://doi.org/10.1016/j.rineng.2024.103290},

doi = {10.1016/j.rineng.2024.103290},

year  = {2024},

date = {2024-01-01},

journal = {Results in Engineering},

volume = {24},

pages = {103290},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1145/3605098.3636191,

title = {Evolutionary Feature Selection for Time-Series Forecasting},

author = {Maria Lourdes Linares-Barrera and Manuel J. Jimenez-Navarro and Isabel Sofia Brito and José C. Riquelme and María Martínez-Ballesteros},

url = {https://doi.org/10.1145/3605098.3636191},

doi = {10.1145/3605098.3636191},

year  = {2024},

date = {2024-01-01},

journal = {Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing},

volume = {N/A},

pages = {395-399},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1007/978-3-031-62799-6_8,

title = {Ground-Level Ozone Forecasting Using Explainable Machine Learning},

author = {Angela Robledo Troncoso-García and Manuel Jesús Jiménez-Navarro and Francisco Martínez-Álvarez and Alicia Troncoso},

url = {https://doi.org/10.1007/978-3-031-62799-6_8},

doi = {10.1007/978-3-031-62799-6_8},

year  = {2024},

date = {2024-01-01},

journal = {Lecture Notes in Computer Science},

volume = {N/A},

pages = {71-80},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1007/978-3-031-62799-6_19,

title = {Toward Explaining Competitive Success in League of Legends: A Machine Learning Analysis},

author = {Francisco Javier Galán-Sales and María Lourdes Linares-Barrera and Pablo Reina-Jiménez and Ana Rodríguez-López and Manuel Jesús Jiménez-Navarro},

url = {https://doi.org/10.1007/978-3-031-62799-6_19},

doi = {10.1007/978-3-031-62799-6_19},

year  = {2024},

date = {2024-01-01},

journal = {Lecture Notes in Computer Science},

volume = {N/A},

pages = {184-193},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1007/978-3-031-62799-6_9,

title = {Multi-Objective Lagged Feature Selection Based on Dependence Coefficient for Time-Series Forecasting},

author = {María Lourdes Linares-Barrera and Manuel J. Jiménez Navarro and José C. Riquelme and María Martínez-Ballesteros},

url = {https://doi.org/10.1007/978-3-031-62799-6_9},

doi = {10.1007/978-3-031-62799-6_9},

year  = {2024},

date = {2024-01-01},

journal = {Lecture Notes in Computer Science},

volume = {N/A},

pages = {81-90},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1093/jigpal/jzae062,

title = {Embedded feature selection for neural networks via learnable drop layer},

author = {M J JimÉnez-Navarro and M MartÍnez-Ballesteros and I S Brito and F MartÍnez-Álvarez and G Asencio-CortÉs},

url = {https://doi.org/10.1093/jigpal/jzae062},

doi = {10.1093/jigpal/jzae062},

year  = {2024},

date = {2024-01-01},

journal = {Logic Journal of the IGPL},

volume = {N/A},

pages = {N/A},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1093/jigpal/jzae030,

title = {From simple to complex: a sequential method for enhancing time series forecasting with deep learning},

author = {M J Jiménez-Navarro and M Martínez-Ballesteros and F Martínez-Álvarez and A Troncoso and G Asencio-Cortés},

url = {https://doi.org/10.1093/jigpal/jzae030},

doi = {10.1093/jigpal/jzae030},

year  = {2024},

date = {2024-01-01},

journal = {Logic Journal of the IGPL},

volume = {N/A},

pages = {N/A},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{JIMENEZNAVARRO2023815,

title = {PHILNet: A novel efficient approach for time series forecasting using deep learning},

author = {M. J. Jiménez-Navarro and M. Martínez-Ballesteros and F. Martínez-Álvarez and G. Asencio-Cortés},

url = {https://www.sciencedirect.com/science/article/pii/S0020025523003183},

doi = {https://doi.org/10.1016/j.ins.2023.03.021},

issn = {0020-0255},

year  = {2023},

date = {2023-01-01},

journal = {Information Sciences},

volume = {632},

pages = {815-832},

abstract = {Time series is one of the most common data types in the industry nowadays. Forecasting the future of a time series behavior can be useful in planning ahead, saving time, resources, and helping avoid undesired scenarios. To make the forecasting, historical data is utilized due to the causal nature of the time series. Several deep learning algorithms have been presented in this area, where the input is processed through a series of non-linear functions to produce the output. We present a novel strategy to improve the performance of deep learning models in time series forecasting in terms of efficiency while reaching similar effectiveness. This approach separates the model into levels, starting with the easiest and continuing to the most difficult. The simpler levels deal with smoothed versions of the input, whereas the most sophisticated level deals with the raw data. This strategy seeks to mimic the human learning process, in which basic tasks are completed initially, followed by more precise and sophisticated ones. Our method achieved promising results, obtaining a 35% improvement in mean squared error and a 2.6 time decrease in training time compared with the best models found in a variety of time series.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1007/978-3-031-43078-7_2,

title = {Embedded Temporal Feature Selection for Time Series Forecasting Using Deep Learning},

author = {M. J. Jiménez-Navarro and M. Martínez-Ballesteros and F. Martínez-Álvarez and G. Asencio-Cortés},

url = {https://doi.org/10.1007/978-3-031-43078-7_2},

doi = {10.1007/978-3-031-43078-7_2},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes in Computer Science},

volume = {N/A},

pages = {15-26},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1007/978-3-031-42536-3_13,

title = {Explaining Learned Patterns in Deep Learning by Association Rules Mining},

author = {M. J. Jiménez-Navarro and M. Martínez-Ballesteros and F. Martínez-Álvarez and G. Asencio-Cortés},

url = {https://doi.org/10.1007/978-3-031-42536-3_13},

doi = {10.1007/978-3-031-42536-3_13},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes in Networks and Systems},

volume = {N/A},

pages = {132-141},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1007/978-3-031-42519-6_30,

title = {Association Rule Analysis of Student Satisfaction Surveys for Teaching Quality Evaluation},

author = {Manuel J. Jiménez-Navarro and Belén Vega-Márquez and José María Luna-Romera and Manuel Carranza-García and María Martínez-Ballesteros},

url = {https://doi.org/10.1007/978-3-031-42519-6_30},

doi = {10.1007/978-3-031-42519-6_30},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes in Networks and Systems},

volume = {N/A},

pages = {319-328},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1186/s40537-023-00745-0,

title = {A new deep learning architecture with inductive bias balance for transformer oil temperature forecasting},

author = {Manuel J. Jiménez-Navarro and María Martínez-Ballesteros and Francisco Martínez-Álvarez and Gualberto Asencio-Cortés},

url = {https://doi.org/10.1186/s40537-023-00745-0},

doi = {10.1186/s40537-023-00745-0},

year  = {2023},

date = {2023-01-01},

journal = {Journal of Big Data},

volume = {10},

pages = {N/A},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1007/978-3-031-42529-5_20,

title = {Feature Selection Guided by CVOA Metaheuristic for Deep Neural Networks: Application to Multivariate Time Series Forecasting},

author = {Manuel Jesús Jiménez-Navarro and Camilo Restrepo-Estrada and Laura Melgar-García and David Gutierrez-Aviles},

url = {https://doi.org/10.1007/978-3-031-42529-5_20},

doi = {10.1007/978-3-031-42529-5_20},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes in Networks and Systems},

volume = {N/A},

pages = {209-218},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1007/978-3-031-18050-7_54,

title = {Feature-Aware Drop Layer (FADL): A Nonparametric Neural Network Layer for Feature Selection},

author = {Manuel Jesús Jiménez-Navarro and María Martínez-Ballesteros and Isabel Sofia Sousa Brito and Francisco Martínez-Álvarez and Gualberto Asencio-Cortés},

url = {https://doi.org/10.1007/978-3-031-18050-7_54},

doi = {10.1007/978-3-031-18050-7_54},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes in Networks and Systems},

volume = {N/A},

pages = {557-566},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1145/3555776.3578634,

title = {A bioinspired ensemble approach for multi-horizon reference evapotranspiration forecasting in Portugal},

author = {Manuel Jesus Jimenez Navarro and Maria Martinez Ballesteros and Isabel Sofia Brito and Francisco Martinez Alvarez and Gualberto Asencio Cortes},

url = {https://doi.org/10.1145/3555776.3578634},

doi = {10.1145/3555776.3578634},

year  = {2023},

date = {2023-01-01},

journal = {Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing},

volume = {N/A},

pages = {441-448},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1016/j.knosys.2022.109644,

title = {DIAFAN-TL: An instance weighting-based transfer learning algorithm with application to phenology forecasting},

author = {M. A. Molina-Cabanillas and M. J. Jiménez-Navarro and R. Arjona and F. Martínez-Álvarez and G. Asencio-Cortés},

url = {https://doi.org/10.1016/j.knosys.2022.109644},

doi = {10.1016/j.knosys.2022.109644},

year  = {2022},

date = {2022-01-01},

journal = {Knowledge-Based Systems},

volume = {254},

pages = {109644},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1007/978-3-030-87869-6_68,

title = {HLNet: A Novel Hierarchical Deep Neural Network for Time Series Forecasting},

author = {M. J. Jiménez-Navarro and F. Martínez-Alvarez and A. Troncoso and G. Asencio-Cortés},

url = {https://doi.org/10.1007/978-3-030-87869-6_68},

doi = {10.1007/978-3-030-87869-6_68},

year  = {2022},

date = {2022-01-01},

journal = {Advances in Intelligent Systems and Computing},

volume = {N/A},

pages = {717-727},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1007/978-3-030-86271-8_43,

title = {A Model-Based Deep Transfer Learning Algorithm for Phenology Forecasting Using Satellite Imagery},

author = {M. Á. Molina and M. J. Jiménez-Navarro and F. Martínez-Álvarez and G. Asencio-Cortés},

url = {https://doi.org/10.1007/978-3-030-86271-8_43},

doi = {10.1007/978-3-030-86271-8_43},

year  = {2021},

date = {2021-01-01},

journal = {Lecture Notes in Computer Science},

volume = {N/A},

pages = {511-523},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1007/978-3-030-85713-4_21,

title = {Electricity Consumption Time Series Forecasting Using Temporal Convolutional Networks},

author = {J. F. Torres and M. J. Jiménez-Navarro and F. Martínez-Álvarez and A. Troncoso},

url = {https://doi.org/10.1007/978-3-030-85713-4_21},

doi = {10.1007/978-3-030-85713-4_21},

year  = {2021},

date = {2021-01-01},

journal = {Lecture Notes in Computer Science},

volume = {N/A},

pages = {216-225},

keywords = {},

pubstate = {published},

tppubtype = {article}

}