Publicaciones

@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{10.1016/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{10.1145/3605098.3636191,

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_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{10.1007/978-3-031-62799-6_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{10.1016/j.engappai.2024.109088,

title = {Advancing unsupervised anomaly detection with normalizing flow and multi-scale ensemble learning},

author = {Miguel Campos-Romero and Manuel Carranza-García and José C. Riquelme},

url = {https://doi.org/10.1016/j.engappai.2024.109088},

doi = {10.1016/j.engappai.2024.109088},

year  = {2024},

date = {2024-01-01},

journal = {Engineering Applications of Artificial Intelligence},

volume = {137},

pages = {109088},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1016/j.ijepes.2023.109730,

title = {A machine learning-based methodology for short-term kinetic energy forecasting with real-time application: Nordic Power System case},

author = {Jose Miguel Riquelme-Dominguez and Manuel Carranza-García and Pedro Lara-Benítez and Francisco M. González-Longatt},

url = {https://doi.org/10.1016/j.ijepes.2023.109730},

doi = {10.1016/j.ijepes.2023.109730},

year  = {2024},

date = {2024-01-01},

journal = {International Journal of Electrical Power & Energy Systems},

volume = {156},

pages = {109730},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1016/j.heliyon.2024.e25838,

title = {An empirical analysis of the relationship among price, demand and CO2 emissions in the Spanish electricity market},

author = {José María Luna-Romera and Manuel Carranza-García and Ángel Arcos-Vargas and José C. Riquelme-Santos},

url = {https://doi.org/10.1016/j.heliyon.2024.e25838},

doi = {10.1016/j.heliyon.2024.e25838},

year  = {2024},

date = {2024-01-01},

journal = {Heliyon},

volume = {10},

pages = {e25838},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.3233/ica-230726,

title = {Enhancing smart home appliance recognition with wavelet and scalogram analysis using data augmentation},

author = {José L. Salazar-González and José María Luna-Romera and Manuel Carranza-García and Juan A. Álvarez-García and Luis M. Soria-Morillo},

url = {https://doi.org/10.3233/ica-230726},

doi = {10.3233/ica-230726},

year  = {2024},

date = {2024-01-01},

journal = {Integrated Computer-Aided Engineering},

volume = {31},

pages = {307-326},

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{10.1093/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{10.1145/3605098.3636034,

title = {An evolutionary triclustering approach to discover electricity consumption patterns in France},

author = {David Gutierrez-Aviles and Jose F. Torres and Francisco Martinez-Alvarez and Jairo Cugliari},

url = {https://doi.org/10.1145/3605098.3636034},

doi = {10.1145/3605098.3636034},

year  = {2024},

date = {2024-01-01},

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

volume = {N/A},

pages = {386-394},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1007/978-3-031-62799-6_13,

title = {An Experimental Comparison of Qiskit and Pennylane for Hybrid Quantum-Classical Support Vector Machines},

author = {Francesc Rodríguez-Díaz and José Francisco Torres and David Gutiérrez-Avilés and Alicia Troncoso and Francisco Martínez-Álvarez},

url = {https://doi.org/10.1007/978-3-031-62799-6_13},

doi = {10.1007/978-3-031-62799-6_13},

year  = {2024},

date = {2024-01-01},

journal = {Lecture Notes in Computer Science},

volume = {N/A},

pages = {121-130},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1016/j.ijepes.2024.109947,

title = {Semi-real-time decision tree ensemble algorithms for very short-term solar irradiance forecasting},

author = {Jose E. Sanchez-Lopez and Javier Solís-García and Jose C. Riquelme},

url = {https://doi.org/10.1016/j.ijepes.2024.109947},

doi = {10.1016/j.ijepes.2024.109947},

year  = {2024},

date = {2024-01-01},

journal = {International Journal of Electrical Power & Energy Systems},

volume = {158},

pages = {109947},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1016/j.heliyon.2024.e25821,

title = {Addressing energy challenges in Iraq: Forecasting power supply and demand using artificial intelligence models},

author = {Morteza Aldarraji and Belén Vega-Márquez and Beatriz Pontes and Basim Mahmood and José C. Riquelme},

url = {https://doi.org/10.1016/j.heliyon.2024.e25821},

doi = {10.1016/j.heliyon.2024.e25821},

year  = {2024},

date = {2024-01-01},

journal = {Heliyon},

volume = {10},

pages = {e25821},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{TRONCOSOGARCIA2023169,

title = {A new approach based on association rules to add explainability to time series forecasting models},

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

url = {https://www.sciencedirect.com/science/article/pii/S1566253523000295},

doi = {https://doi.org/10.1016/j.inffus.2023.01.021},

issn = {1566-2535},

year  = {2023},

date = {2023-01-01},

journal = {Information Fusion},

volume = {94},

pages = {169-180},

abstract = {Machine learning and deep learning have become the most useful and powerful tools in the last years to mine information from large datasets. Despite the successful application to many research fields, it is widely known that some of these solutions based on artificial intelligence are considered black-box models, meaning that most experts find difficult to explain and interpret the models and why they generate such outputs. In this context, explainable artificial intelligence is emerging with the aim of providing black-box models with sufficient interpretability. Thus, models could be easily understood and further applied. This work proposes a novel method to explain black-box models, by using numeric association rules to explain and interpret multi-step time series forecasting models. Thus, a multi-objective algorithm is used to discover quantitative association rules from the target model. Then, visual explanation techniques are applied to make the rules more interpretable. Data from Spanish electricity energy consumption has been used to assess the suitability of the proposal.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{articleh,

title = {A Novel Distributed Forecasting Method Based on Information Fusion and Incremental Learning for Streaming Time Series},

author = {Laura Melgar-García and David Gutiérrez-Avilés and Cristina Rubio-Escudero and Alicia Troncoso},

doi = {10.2139/ssrn.4326600},

year  = {2023},

date = {2023-01-01},

journal = {SSRN Electronic Journal},

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}

}