UNIVERSIDAD PABLO DE OLAVIDE
Publications
2025
D. Gutiérrez-Avilés and M. J. Jiménez-Navarro and J. F. Torres and F. Martínez-Álvarez
MetaGen: A framework for metaheuristic development and hyperparameter optimization in machine and deep learning Journal Article Forthcoming
In: Neurocomputing, Forthcoming.
@article{GUTIERREZ-AVILES25,
title = {MetaGen: A framework for metaheuristic development and hyperparameter optimization in machine and deep learning},
author = {D. Gutiérrez-Avilés and M. J. Jiménez-Navarro and J. F. Torres and F. Martínez-Álvarez},
year = {2025},
date = {2025-03-15},
urldate = {2025-03-15},
journal = {Neurocomputing},
abstract = {Hyperparameter optimization is a pivotal step in enhancing model performance within machine learning. Traditionally, this challenge is addressed through metaheuristics, which efficiently explore large search spaces to uncover optimal solutions. However, implementing these techniques can be complex without adequate development tools, which is the primary focus of this paper. Hence, we introduce MetaGen, a novel Python package designed to provide a comprehensive framework for developing and evaluating metaheuristic algorithms. MetaGen follows best practices in Python design, ensuring minimalistic code implementation, intuitive comprehension, and full flexibility in solution representation. The package defines two distinct user roles: Developers, responsible for algorithm implementation for hyperparameter optimization, and Solvers, who leverage pre-implemented metaheuristics to address optimization problems. Beyond algorithm implementation, MetaGen facilitates benchmarking through built-in test functions, ensuring standardized performance comparisons. It also provides automated reporting and visualization tools to analyze optimization progress and outcomes effectively. Furthermore, its modular design allows distribution
and integration into existing machine learning workflows. Several illustrative use cases are presented to demonstrate its adaptability and efficacy. The package, along with code, a user manual, and supplementary materials, is available at: https://github.com/Data-Science-Big-Data-Research-Lab/MetaGen.},
keywords = {},
pubstate = {forthcoming},
tppubtype = {article}
}
Hyperparameter optimization is a pivotal step in enhancing model performance within machine learning. Traditionally, this challenge is addressed through metaheuristics, which efficiently explore large search spaces to uncover optimal solutions. However, implementing these techniques can be complex without adequate development tools, which is the primary focus of this paper. Hence, we introduce MetaGen, a novel Python package designed to provide a comprehensive framework for developing and evaluating metaheuristic algorithms. MetaGen follows best practices in Python design, ensuring minimalistic code implementation, intuitive comprehension, and full flexibility in solution representation. The package defines two distinct user roles: Developers, responsible for algorithm implementation for hyperparameter optimization, and Solvers, who leverage pre-implemented metaheuristics to address optimization problems. Beyond algorithm implementation, MetaGen facilitates benchmarking through built-in test functions, ensuring standardized performance comparisons. It also provides automated reporting and visualization tools to analyze optimization progress and outcomes effectively. Furthermore, its modular design allows distribution
and integration into existing machine learning workflows. Several illustrative use cases are presented to demonstrate its adaptability and efficacy. The package, along with code, a user manual, and supplementary materials, is available at: https://github.com/Data-Science-Big-Data-Research-Lab/MetaGen.
and integration into existing machine learning workflows. Several illustrative use cases are presented to demonstrate its adaptability and efficacy. The package, along with code, a user manual, and supplementary materials, is available at: https://github.com/Data-Science-Big-Data-Research-Lab/MetaGen.
E. T. Habtemariam and M. Martínez-Ballesteros and A. Troncoso and F. Martínez-Álvarez
A novel approach based on clustering and optimized ensemble deep learning for energy consumption forecasting in Ethiopia Journal Article Forthcoming
In: Neurocomputing, Forthcoming.
@article{HABTEMARIAM25,
title = {A novel approach based on clustering and optimized ensemble deep learning for energy consumption forecasting in Ethiopia},
author = {E. T. Habtemariam and M. Martínez-Ballesteros and A. Troncoso and F. Martínez-Álvarez},
year = {2025},
date = {2025-03-13},
journal = {Neurocomputing},
abstract = {Predicting energy consumption accurately is crucial for optimizing energy management strategies and achieving sustainability goals. Traditional methods often struggle with the complexity of energy consumption patterns, particularly in developing regions such as Ethiopia, where unique challenges exist. This study proposes an ensemble deep learning approach that integrates multiple models to enhance prediction accuracy.
Additionally, as a previous step, a clustering process has been applied to discover different groups of customers. Our method combines deep learning architectures, including Gated Recurrent Units, Long Short-Term Memory, and Convolutional Neural Networks, within an optimized ensemble with weights computed with the Coronavirus Optimization Algorithm. This approach aims to leverage the strengths of each model
to produce robust and reliable predictions. We demonstrate that our ensemble approach yields competitive results, outperforming individual models within the ensemble. By integrating diverse models, our framework captures nuanced patterns in energy consumption data more effectively, contributing to improved prediction accuracy. Furthermore, we validate the effectiveness of our approach using three distinct datasets from Ethiopia for three different customer clusters. These datasets represent different regions and consumption profiles within the country, ensuring the robustness and generalizability of our proposed methodology.},
keywords = {},
pubstate = {forthcoming},
tppubtype = {article}
}
Predicting energy consumption accurately is crucial for optimizing energy management strategies and achieving sustainability goals. Traditional methods often struggle with the complexity of energy consumption patterns, particularly in developing regions such as Ethiopia, where unique challenges exist. This study proposes an ensemble deep learning approach that integrates multiple models to enhance prediction accuracy.
Additionally, as a previous step, a clustering process has been applied to discover different groups of customers. Our method combines deep learning architectures, including Gated Recurrent Units, Long Short-Term Memory, and Convolutional Neural Networks, within an optimized ensemble with weights computed with the Coronavirus Optimization Algorithm. This approach aims to leverage the strengths of each model
to produce robust and reliable predictions. We demonstrate that our ensemble approach yields competitive results, outperforming individual models within the ensemble. By integrating diverse models, our framework captures nuanced patterns in energy consumption data more effectively, contributing to improved prediction accuracy. Furthermore, we validate the effectiveness of our approach using three distinct datasets from Ethiopia for three different customer clusters. These datasets represent different regions and consumption profiles within the country, ensuring the robustness and generalizability of our proposed methodology.
Additionally, as a previous step, a clustering process has been applied to discover different groups of customers. Our method combines deep learning architectures, including Gated Recurrent Units, Long Short-Term Memory, and Convolutional Neural Networks, within an optimized ensemble with weights computed with the Coronavirus Optimization Algorithm. This approach aims to leverage the strengths of each model
to produce robust and reliable predictions. We demonstrate that our ensemble approach yields competitive results, outperforming individual models within the ensemble. By integrating diverse models, our framework captures nuanced patterns in energy consumption data more effectively, contributing to improved prediction accuracy. Furthermore, we validate the effectiveness of our approach using three distinct datasets from Ethiopia for three different customer clusters. These datasets represent different regions and consumption profiles within the country, ensuring the robustness and generalizability of our proposed methodology.
A. R. Troncoso-García and M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso
A new metric based on association rules to assess explainability techniques for time series forecasting Journal Article Forthcoming
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, Forthcoming.
Abstract | BibTeX | Tags: association rules, forecasting, time series, XAI
@article{TRONCOSO-GARCIA25,
title = {A new metric based on association rules to assess explainability techniques for time series forecasting},
author = {A. R. Troncoso-García and M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso},
year = {2025},
date = {2025-02-05},
urldate = {2025-02-05},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
abstract = {This paper introduces a new, model-independent, metric, called RExQUAL, for quantifying the quality of explanations provided by attribution-based explainable artificial intelligence techniques and compare them. The underlying idea is based on feature attribution, using a subset of the ranking of the attributes highlighted by a model-agnostic explainable method in a forecasting task. Then, association rules are generated using these key attributes as input data. Novel metrics, including global support and confidence, are proposed to assess the joint quality of generated rules. Finally, the quality of the explanations is calculated based on a wise and comprehensive combination of the association rules global metrics. The proposed method integrates local explanations through attribution-based approaches for evaluation and feature selection with global explanations for the entire dataset. This paper rigorously evaluates the new metric by comparing three explainability techniques: the widely used SHAP and LIME, and the novel methodology RULEx. The experimental design includes predicting time series of different natures, including univariate and multivariate, through deep learning models. The results underscore the efficacy and versatility of the proposed methodology as a quantitative framework for evaluating and comparing explainable techniques.},
keywords = {association rules, forecasting, time series, XAI},
pubstate = {forthcoming},
tppubtype = {article}
}
This paper introduces a new, model-independent, metric, called RExQUAL, for quantifying the quality of explanations provided by attribution-based explainable artificial intelligence techniques and compare them. The underlying idea is based on feature attribution, using a subset of the ranking of the attributes highlighted by a model-agnostic explainable method in a forecasting task. Then, association rules are generated using these key attributes as input data. Novel metrics, including global support and confidence, are proposed to assess the joint quality of generated rules. Finally, the quality of the explanations is calculated based on a wise and comprehensive combination of the association rules global metrics. The proposed method integrates local explanations through attribution-based approaches for evaluation and feature selection with global explanations for the entire dataset. This paper rigorously evaluates the new metric by comparing three explainability techniques: the widely used SHAP and LIME, and the novel methodology RULEx. The experimental design includes predicting time series of different natures, including univariate and multivariate, through deep learning models. The results underscore the efficacy and versatility of the proposed methodology as a quantitative framework for evaluating and comparing explainable techniques.
R. Scitovski and K. Sabo and D. Grahovac and F. Martínez-Álvarez and S. Ungar
A partitioning incremental algorithm using adaptive Mahalanobis fuzzy clustering and identifying the most appropriate partition Journal Article
In: Pattern Analysis and Applications, vol. 28, pp. 3, 2025.
Abstract | Links | BibTeX | Tags: clustering
@article{SCITOVSKI25,
title = {A partitioning incremental algorithm using adaptive Mahalanobis fuzzy clustering and identifying the most appropriate partition},
author = {R. Scitovski and K. Sabo and D. Grahovac and F. Martínez-Álvarez and S. Ungar},
url = {https://link.springer.com/article/10.1007/s10044-024-01360-2},
doi = {https://doi.org/10.1007/s10044-024-01360-2},
year = {2025},
date = {2025-01-02},
journal = {Pattern Analysis and Applications},
volume = {28},
pages = {3},
abstract = {This paper deals with the problem of determining the most appropriate number of clusters in a fuzzy Mahalanobis partition.
First, a new fuzzy Mahalanobis incremental algorithm is constructed to search for an optimal fuzzy Mahalanobis
partition with 2, 3, ... clusters. Among these partitions, selecting the one with the most appropriate number of clusters
is based on appropriately modified existing fuzzy indexes. In addition, the Fuzzy Mahalanobis Minimal Distance index
is defined as a natural extension of the recently proposed Mahalanobis Minimal Distance index for non-fuzzy clustering.
The new fuzzy Mahalanobis incremental algorithm was tested on several artificial data sets and the color image segmentation
problems from real-world applications: art images, nature photography images, and medical images. The algorithm
includes multiple usage of the global optimization algorithm DIRECT. But unlike previously known fuzzy Mahalanobis
indexes, the proposed Fuzzy Mahalanobis Minimal Distance index ensures accurate results even when applied to complex
real-world applications. A possible disadvantage could be the need for longer CPU time. Furthermore, besides effective
identification of the partition with the most appropriate number of clusters, it is shown how to use the proposed Fuzzy
Mahalanobis Minimal Distance index to search for an acceptable partition, which proved particularly useful in the abovementioned
real-world applications.},
keywords = {clustering},
pubstate = {published},
tppubtype = {article}
}
This paper deals with the problem of determining the most appropriate number of clusters in a fuzzy Mahalanobis partition.
First, a new fuzzy Mahalanobis incremental algorithm is constructed to search for an optimal fuzzy Mahalanobis
partition with 2, 3, ... clusters. Among these partitions, selecting the one with the most appropriate number of clusters
is based on appropriately modified existing fuzzy indexes. In addition, the Fuzzy Mahalanobis Minimal Distance index
is defined as a natural extension of the recently proposed Mahalanobis Minimal Distance index for non-fuzzy clustering.
The new fuzzy Mahalanobis incremental algorithm was tested on several artificial data sets and the color image segmentation
problems from real-world applications: art images, nature photography images, and medical images. The algorithm
includes multiple usage of the global optimization algorithm DIRECT. But unlike previously known fuzzy Mahalanobis
indexes, the proposed Fuzzy Mahalanobis Minimal Distance index ensures accurate results even when applied to complex
real-world applications. A possible disadvantage could be the need for longer CPU time. Furthermore, besides effective
identification of the partition with the most appropriate number of clusters, it is shown how to use the proposed Fuzzy
Mahalanobis Minimal Distance index to search for an acceptable partition, which proved particularly useful in the abovementioned
real-world applications.
First, a new fuzzy Mahalanobis incremental algorithm is constructed to search for an optimal fuzzy Mahalanobis
partition with 2, 3, ... clusters. Among these partitions, selecting the one with the most appropriate number of clusters
is based on appropriately modified existing fuzzy indexes. In addition, the Fuzzy Mahalanobis Minimal Distance index
is defined as a natural extension of the recently proposed Mahalanobis Minimal Distance index for non-fuzzy clustering.
The new fuzzy Mahalanobis incremental algorithm was tested on several artificial data sets and the color image segmentation
problems from real-world applications: art images, nature photography images, and medical images. The algorithm
includes multiple usage of the global optimization algorithm DIRECT. But unlike previously known fuzzy Mahalanobis
indexes, the proposed Fuzzy Mahalanobis Minimal Distance index ensures accurate results even when applied to complex
real-world applications. A possible disadvantage could be the need for longer CPU time. Furthermore, besides effective
identification of the partition with the most appropriate number of clusters, it is shown how to use the proposed Fuzzy
Mahalanobis Minimal Distance index to search for an acceptable partition, which proved particularly useful in the abovementioned
real-world applications.
P. Casas-Gómez and J. F. Torres and J. C. Linares and A. Troncoso and F. Martínez-Álvarez
Forecasting basal area increment in forest ecosystems using deep learning: A multi-species analysis in the Himalayas Journal Article
In: Ecological Informatics, vol. 85, pp. 102951, 2025.
Abstract | Links | BibTeX | Tags: deep learning, time series
@article{CASAS-GOMEZ25,
title = {Forecasting basal area increment in forest ecosystems using deep learning: A multi-species analysis in the Himalayas},
author = {P. Casas-Gómez and J. F. Torres and J. C. Linares and A. Troncoso and F. Martínez-Álvarez},
url = {https://www.sciencedirect.com/science/article/pii/S157495412400493X?via%3Dihub},
doi = {https://doi.org/10.1016/j.ecoinf.2024.102951},
year = {2025},
date = {2025-01-01},
journal = {Ecological Informatics},
volume = {85},
pages = {102951},
abstract = {This study addresses the task of forecasting Basal Area Increment trends in forest ecosystems, which is essential for conservation and biodiversity management, particularly in the context of climate change. Traditional forecasting techniques, such as Linear Mixed Models, Random Forest and standard Artificial Neural Networks, often fail to account for the time-dependent nature of tree growth and utilize simple architectures. To overcome these limitations, we introduce the use of two different Deep Learning models: the Long Short-Term Memory network and the Temporal Convolutional Neural Network, which capture the temporal dependencies of growth by incorporating lagged Basal Area Increment values. Our methodology includes rigorous hyperparameter tuning to optimize the Deep Learning models’ architecture. We evaluate the models’ performance across 15 species in the Himalayan region, individually and collectively, using temperature and precipitation data as predictors. The Deep Learning model significantly outperforms state-of-the-art techniques, achieving the lowest Root Mean Squared Error (7.407 for LSTM and 6.202 for TCNN), highest 𝑅2 (0.495 for LSTM an0.585 for TCNN) and lowest Mean Absolute Percentage Error values (37.653 for LSTM and 34.296 for TCNN). These findings highlight the potential of Deep Learning networks to provide accurate and reliable Basal AreaIncrement forecasts, offering valuable insights for forest management and conservation efforts in the face of ongoing climate change.},
keywords = {deep learning, time series},
pubstate = {published},
tppubtype = {article}
}
This study addresses the task of forecasting Basal Area Increment trends in forest ecosystems, which is essential for conservation and biodiversity management, particularly in the context of climate change. Traditional forecasting techniques, such as Linear Mixed Models, Random Forest and standard Artificial Neural Networks, often fail to account for the time-dependent nature of tree growth and utilize simple architectures. To overcome these limitations, we introduce the use of two different Deep Learning models: the Long Short-Term Memory network and the Temporal Convolutional Neural Network, which capture the temporal dependencies of growth by incorporating lagged Basal Area Increment values. Our methodology includes rigorous hyperparameter tuning to optimize the Deep Learning models’ architecture. We evaluate the models’ performance across 15 species in the Himalayan region, individually and collectively, using temperature and precipitation data as predictors. The Deep Learning model significantly outperforms state-of-the-art techniques, achieving the lowest Root Mean Squared Error (7.407 for LSTM and 6.202 for TCNN), highest 𝑅2 (0.495 for LSTM an0.585 for TCNN) and lowest Mean Absolute Percentage Error values (37.653 for LSTM and 34.296 for TCNN). These findings highlight the potential of Deep Learning networks to provide accurate and reliable Basal AreaIncrement forecasts, offering valuable insights for forest management and conservation efforts in the face of ongoing climate change.
L. Melgar-García and D. Gutiérrez-Avilés and C. Rubio-Escudero and A. Troncoso
Online forecasting using neighbor-based incremental learning for electricity markets Journal Article
In: Neural Computing and Applications, 2025.
Links | BibTeX | Tags: energy, IoT, time series
@article{Melgar2025,
title = {Online forecasting using neighbor-based incremental learning for electricity markets},
author = {L. Melgar-García and D. Gutiérrez-Avilés and C. Rubio-Escudero and A. Troncoso },
url = {https://link.springer.com/article/10.1007/s00521-024-10876-x},
doi = {https://doi.org/10.1007/s00521-024-10876-x},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Neural Computing and Applications},
keywords = {energy, IoT, time series},
pubstate = {published},
tppubtype = {article}
}
A. Lopez-Fernandez and F. Divina and F. A. Gomez-Vela and M. Garcia-Torres
Data mining for enhancing learning and assessment to a microcompetence-based methodology in higher education Journal Article
In: IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 2025.
Abstract | Links | BibTeX | Tags: data mining, education
@article{lopez2025data,
title = {Data mining for enhancing learning and assessment to a microcompetence-based methodology in higher education},
author = { A. Lopez-Fernandez and F. Divina and F. A. Gomez-Vela and M. Garcia-Torres},
doi = {10.1109/RITA.2025.3532879},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {IEEE Revista Iberoamericana de Tecnologias del Aprendizaje},
publisher = {IEEE},
abstract = {This work introduces an innovative teaching methodology based on microcompetences applied in a higher education context. The intervention involved creating a repository of practical case studies in the form of quizzes and integrating microcompetences
into each course activity. The digital tool Sapiens was used to identify learning deficiencies and provide both collective and individualized feedback. The results indicate a significant increase in student participation and academic performance compared to
previous years. Furthermore, students voluntarily used virtual teaching modalities to reinforce their knowledge, particularly in more complex areas. Data mining techniques identified performance patterns among students, highlighting the methodology’s
effectiveness in improving both transversal and specific competences. The study’s findings underscore the importance of implementing microcompetency-based methodologies in higher education to enhance the quality of learning and continuous assessment. This
approach not only facilitated a deeper understanding of course content but also promoted critical thinking, abstract reasoning, and interpersonal skills, preparing students for future academic and professional challenges. Additionally, the flexibility and
adaptability of the digital tools used provided a seamless transition across different teaching modalities, such as in-person, hybrid, and online formats. Thus, the implementation of this innovative methodology has demonstrated its potential to significantly
improve student engagement, participation, and academic success, thereby contributing to a more effective and comprehensive educational experience in higher education. url = https://ieeexplore.ieee.org/abstract/document/10849581},
keywords = {data mining, education},
pubstate = {published},
tppubtype = {article}
}
This work introduces an innovative teaching methodology based on microcompetences applied in a higher education context. The intervention involved creating a repository of practical case studies in the form of quizzes and integrating microcompetences
into each course activity. The digital tool Sapiens was used to identify learning deficiencies and provide both collective and individualized feedback. The results indicate a significant increase in student participation and academic performance compared to
previous years. Furthermore, students voluntarily used virtual teaching modalities to reinforce their knowledge, particularly in more complex areas. Data mining techniques identified performance patterns among students, highlighting the methodology’s
effectiveness in improving both transversal and specific competences. The study’s findings underscore the importance of implementing microcompetency-based methodologies in higher education to enhance the quality of learning and continuous assessment. This
approach not only facilitated a deeper understanding of course content but also promoted critical thinking, abstract reasoning, and interpersonal skills, preparing students for future academic and professional challenges. Additionally, the flexibility and
adaptability of the digital tools used provided a seamless transition across different teaching modalities, such as in-person, hybrid, and online formats. Thus, the implementation of this innovative methodology has demonstrated its potential to significantly
improve student engagement, participation, and academic success, thereby contributing to a more effective and comprehensive educational experience in higher education. url = https://ieeexplore.ieee.org/abstract/document/10849581
into each course activity. The digital tool Sapiens was used to identify learning deficiencies and provide both collective and individualized feedback. The results indicate a significant increase in student participation and academic performance compared to
previous years. Furthermore, students voluntarily used virtual teaching modalities to reinforce their knowledge, particularly in more complex areas. Data mining techniques identified performance patterns among students, highlighting the methodology’s
effectiveness in improving both transversal and specific competences. The study’s findings underscore the importance of implementing microcompetency-based methodologies in higher education to enhance the quality of learning and continuous assessment. This
approach not only facilitated a deeper understanding of course content but also promoted critical thinking, abstract reasoning, and interpersonal skills, preparing students for future academic and professional challenges. Additionally, the flexibility and
adaptability of the digital tools used provided a seamless transition across different teaching modalities, such as in-person, hybrid, and online formats. Thus, the implementation of this innovative methodology has demonstrated its potential to significantly
improve student engagement, participation, and academic success, thereby contributing to a more effective and comprehensive educational experience in higher education. url = https://ieeexplore.ieee.org/abstract/document/10849581
M. Garcia-Torres
Feature selection for high-dimensional data using a multivariate search space reduction strategy based scatter search Journal Article
In: Journal of Heuristics, vol. 31, no. 1, pp. 10, 2025.
Abstract | Links | BibTeX | Tags: feature selection
@article{garcia2025feature,
title = {Feature selection for high-dimensional data using a multivariate search space reduction strategy based scatter search},
author = {M. Garcia-Torres},
doi = {10.1007/s10732-025-09550-9},
year = {2025},
date = {2025-01-01},
journal = {Journal of Heuristics},
volume = {31},
number = {1},
pages = {10},
publisher = {Springer},
abstract = {In feature selection, the increasing of the dimensionality and the complexity of feature interactions make the problem challenging. Furthermore, searching for an optimal subset of features from a high-dimensional feature space is known to be an
NP-hard problem. To improve the efficiency and effectiveness of the search algorithm, feature grouping has emerged as a way to reduce the search space by clustering features according to a measure. In this work we propose to reduce the search space by
applying a greedy algorithm, called Multivariate Greedy Predominant Groups Generator (MGPGG). MGPGG extends the idea of the Greedy Predominant Groups Generator (GPGG) algorithm by taking into account feature interaction among three or more features. For
this purpose, MGPGG uses the Multivariate Symmetrical Uncertainty (MSU) to group features that share information about the class label. We also propose a Scatter Search strategy that integrates MGPGG to find small subsets of features with high predictive power.
The proposed algorithm, called Multivariate Predominant Group-based Scatter Search (MPGSS), is tested on high-dimensional data from biomedical and text-mining fields. The proposal is compared with state-of-the-art feature selection strategies. Results show that
MPGSS is competitive since it is capable of finding small subsets of features while keeping high predictive classification models. url = https://link.springer.com/article/10.1007/s10732-025-09550-9},
keywords = {feature selection},
pubstate = {published},
tppubtype = {article}
}
In feature selection, the increasing of the dimensionality and the complexity of feature interactions make the problem challenging. Furthermore, searching for an optimal subset of features from a high-dimensional feature space is known to be an
NP-hard problem. To improve the efficiency and effectiveness of the search algorithm, feature grouping has emerged as a way to reduce the search space by clustering features according to a measure. In this work we propose to reduce the search space by
applying a greedy algorithm, called Multivariate Greedy Predominant Groups Generator (MGPGG). MGPGG extends the idea of the Greedy Predominant Groups Generator (GPGG) algorithm by taking into account feature interaction among three or more features. For
this purpose, MGPGG uses the Multivariate Symmetrical Uncertainty (MSU) to group features that share information about the class label. We also propose a Scatter Search strategy that integrates MGPGG to find small subsets of features with high predictive power.
The proposed algorithm, called Multivariate Predominant Group-based Scatter Search (MPGSS), is tested on high-dimensional data from biomedical and text-mining fields. The proposal is compared with state-of-the-art feature selection strategies. Results show that
MPGSS is competitive since it is capable of finding small subsets of features while keeping high predictive classification models. url = https://link.springer.com/article/10.1007/s10732-025-09550-9
NP-hard problem. To improve the efficiency and effectiveness of the search algorithm, feature grouping has emerged as a way to reduce the search space by clustering features according to a measure. In this work we propose to reduce the search space by
applying a greedy algorithm, called Multivariate Greedy Predominant Groups Generator (MGPGG). MGPGG extends the idea of the Greedy Predominant Groups Generator (GPGG) algorithm by taking into account feature interaction among three or more features. For
this purpose, MGPGG uses the Multivariate Symmetrical Uncertainty (MSU) to group features that share information about the class label. We also propose a Scatter Search strategy that integrates MGPGG to find small subsets of features with high predictive power.
The proposed algorithm, called Multivariate Predominant Group-based Scatter Search (MPGSS), is tested on high-dimensional data from biomedical and text-mining fields. The proposal is compared with state-of-the-art feature selection strategies. Results show that
MPGSS is competitive since it is capable of finding small subsets of features while keeping high predictive classification models. url = https://link.springer.com/article/10.1007/s10732-025-09550-9
2024
M. J. Jiménez-Navarro and A.R. Troncoso-García and A. Troncoso and F. Martínez-Álvarez and M. Martínez-Ballesteros
Explainable Deep Learning with Embedded Feature Selection for Electricity Demand Forecasting Conference
SST International Conference on Smart Systems and Technologies, 2024.
Abstract | Links | BibTeX | Tags: deep learning, energy, feature selection, XAI
@conference{SST2024,
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://ieeexplore.ieee.org/document/10755283},
doi = {10.1109/SST61991.2024.10755283},
year = {2024},
date = {2024-10-16},
urldate = {2024-10-16},
booktitle = {SST International Conference on Smart Systems and Technologies},
pages = {153-158},
abstract = {Electricity demand forecasting is an important part of the energy industry strategy. Accurate predictions are crucial for maintaining a stable energy supply, planning production, managing distribution, preventing grid overloads, integrating renewable energy sources, and reducing costs and environmental impact. Machine learning and, in particular, deep learning are promising techniques to improve the prediction accuracy of electric demand, but face challenges related to a lack of interpretability due to the “black box” nature of some models. Feature selection methods address these issues by identifying relevant features and simplifying the learning process. This paper aims to explain the most critical lags that impact electric demand forecasting in Spain using the temporal selection layer technique within deep learning models for time series forecasting. This technique transforms a neural network into a model with embedded feature selection, aiming to enhance efficacy and interpretability while reducing computational costs. The results were compared with other methods that incorporate an embedded feature selection mechanism to select the best model. Furthermore, an explainable technique is used to assess the feature importance in the best model over the last year to understand how input features influence electric demand forecasting and provide insights into their contributions and interactions. The results show that our approach improves both the efficacy and interpretability in the context of electric demand forecasting.},
keywords = {deep learning, energy, feature selection, XAI},
pubstate = {published},
tppubtype = {conference}
}
Electricity demand forecasting is an important part of the energy industry strategy. Accurate predictions are crucial for maintaining a stable energy supply, planning production, managing distribution, preventing grid overloads, integrating renewable energy sources, and reducing costs and environmental impact. Machine learning and, in particular, deep learning are promising techniques to improve the prediction accuracy of electric demand, but face challenges related to a lack of interpretability due to the “black box” nature of some models. Feature selection methods address these issues by identifying relevant features and simplifying the learning process. This paper aims to explain the most critical lags that impact electric demand forecasting in Spain using the temporal selection layer technique within deep learning models for time series forecasting. This technique transforms a neural network into a model with embedded feature selection, aiming to enhance efficacy and interpretability while reducing computational costs. The results were compared with other methods that incorporate an embedded feature selection mechanism to select the best model. Furthermore, an explainable technique is used to assess the feature importance in the best model over the last year to understand how input features influence electric demand forecasting and provide insights into their contributions and interactions. The results show that our approach improves both the efficacy and interpretability in the context of electric demand forecasting.
H. Quintián and E. Corchado and A. Troncoso and H. Pérez-García and E. Jove-Pérez and J. L. Calvo-Rolle and F. J. Martínez de Pisón and P. García-Bringas and F. Martínez-Álvarez and Á. Herrero and P. Fosci
Springer, vol. 14858, 2024, ISBN: 978-3-031-74185-2.
Links | BibTeX | Tags: big data, clustering, deep learning, IoT
@proceedings{HAIS2024_part2,
title = {Proceedings of the 19th International Conference on Hybrid Artificial Intelligent Systems (HAIS 2024) Salamanca, Spain, October 9-11, 2024, Part II},
author = {H. Quintián and E. Corchado and A. Troncoso and H. Pérez-García and E. Jove-Pérez and J. L. Calvo-Rolle and F. J. Martínez de Pisón and P. García-Bringas and F. Martínez-Álvarez and Á. Herrero and P. Fosci},
editor = {H. Quintián and E. Corchado and A. Troncoso and H. Pérez-García and E. Jove-Pérez and J. L. Calvo-Rolle and F. J. Martínez de Pisón and P. García-Bringas and F. Martínez-Álvarez and Á. Herrero and P. Fosci},
url = {https://link.springer.com/book/10.1007/978-3-031-74186-9},
doi = {https://doi.org/10.1007/978-3-031-74186-9},
isbn = {978-3-031-74185-2},
year = {2024},
date = {2024-10-10},
volume = {14858},
publisher = {Springer},
series = {Lecture Notes in Artificial Intelligence},
keywords = {big data, clustering, deep learning, IoT},
pubstate = {published},
tppubtype = {proceedings}
}
A. R. Troncoso-García and M. J. Jiménez-Navarro and M. L. Linares-Barrera and I. S. Brito and F. Martínez-Álvarez and M. Martínez-Ballesteros
Time Series Forecasting in Agriculture: Explainable Deep Learning with Lagged Feature Selection Conference
SOCO 19th International Conference on Soft Computing Models in Industrial and Environmental Applications, Lecture Notes in Networks and Systems 2024.
Links | BibTeX | Tags: deep learning, forecasting, time series, XAI
@conference{SOCO24_Troncoso,
title = {Time Series Forecasting in Agriculture: Explainable Deep Learning with Lagged Feature Selection},
author = {A. R. Troncoso-García and M. J. Jiménez-Navarro and M. L. Linares-Barrera and I. S. Brito and F. Martínez-Álvarez and M. Martínez-Ballesteros},
url = {https://link.springer.com/chapter/10.1007/978-3-031-75013-7_14},
doi = {https://doi.org/10.1007/978-3-031-75013-7_14},
year = {2024},
date = {2024-10-10},
booktitle = {SOCO 19th International Conference on Soft Computing Models in Industrial and Environmental Applications},
pages = {139-149},
series = {Lecture Notes in Networks and Systems},
keywords = {deep learning, forecasting, time series, XAI},
pubstate = {published},
tppubtype = {conference}
}
H. Quintián and E. Corchado and A. Troncoso and H. Pérez-García and E. Jove-Pérez and J. L. Calvo-Rolle and F. J. Martínez de Pisón and P. García-Bringas and F. Martínez-Álvarez and Á. Herrero and P. Fosci
Springer, vol. 14857, 2024, ISBN: 978-3-031-74182-1.
Links | BibTeX | Tags: big data, clustering, deep learning, IoT
@proceedings{HAIS2024_part1,
title = {Proceedings of the 19th International Conference on Hybrid Artificial Intelligent Systems (HAIS 2024) Salamanca, Spain, October 9-11, 2024, Part I},
author = {H. Quintián and E. Corchado and A. Troncoso and H. Pérez-García and E. Jove-Pérez and J. L. Calvo-Rolle and F. J. Martínez de Pisón and P. García-Bringas and F. Martínez-Álvarez and Á. Herrero and P. Fosci},
editor = {H. Quintián and E. Corchado and A. Troncoso and H. Pérez-García and E. Jove-Pérez and J. L. Calvo-Rolle and F. J. Martínez de Pisón and P. García-Bringas and F. Martínez-Álvarez and Á. Herrero and P. Fosci},
url = {https://link.springer.com/book/10.1007/978-3-031-74183-8},
doi = {https://doi.org/10.1007/978-3-031-74183-8},
isbn = {978-3-031-74182-1},
year = {2024},
date = {2024-10-09},
volume = {14857},
publisher = {Springer},
series = {Lecture Notes in Artificial Intelligence},
keywords = {big data, clustering, deep learning, IoT},
pubstate = {published},
tppubtype = {proceedings}
}
F. Rodríguez-Díaz, A. M. Chacón-Maldonado and A. R. Troncoso-García and G. Asencio-Cortés
Olive grove and Grapevine pest forecasting through machine learning-based classification and regression Journal Article
In: Results in Engineering, vol. 24, pp. 103058, 2024.
Abstract | Links | BibTeX | Tags: deep learning, feature selection, time series, XAI
@article{RODRIGUEZ24,
title = {Olive grove and Grapevine pest forecasting through machine learning-based classification and regression},
author = {F. Rodríguez-Díaz, A. M. Chacón-Maldonado and A. R. Troncoso-García and G. Asencio-Cortés},
url = {https://www.sciencedirect.com/science/article/pii/S2590123024013136},
doi = {https://doi.org/10.1016/j.rineng.2024.103058},
year = {2024},
date = {2024-09-09},
journal = {Results in Engineering},
volume = {24},
pages = {103058},
abstract = {Pests significantly impact agricultural productivity, making early detection crucial for maximizing yields. This paper explores the use of machine learning models to predict olive fly and red spider mite infestations in Andalusia. Four datasets on crop phenology, pest populations, and damage levels were used, with models developed using the Python package H20, which focuses on interpretability through SHAP values and ICE plots. The results showed high precision in predicting pest outbreaks, particularly for the olive fly, with minimal differences between models using feature selection. In the vineyard dataset, the selection of characteristics improved the performance of the model by reducing the MAE and increasing R2. Explainability techniques identified solar radiation and wind direction as key factors in olive fly predictions, while past pest occurrences and wind velocity were influential for red spider mites, providing farmers with actionable insights for timely pest control.},
keywords = {deep learning, feature selection, time series, XAI},
pubstate = {published},
tppubtype = {article}
}
Pests significantly impact agricultural productivity, making early detection crucial for maximizing yields. This paper explores the use of machine learning models to predict olive fly and red spider mite infestations in Andalusia. Four datasets on crop phenology, pest populations, and damage levels were used, with models developed using the Python package H20, which focuses on interpretability through SHAP values and ICE plots. The results showed high precision in predicting pest outbreaks, particularly for the olive fly, with minimal differences between models using feature selection. In the vineyard dataset, the selection of characteristics improved the performance of the model by reducing the MAE and increasing R2. Explainability techniques identified solar radiation and wind direction as key factors in olive fly predictions, while past pest occurrences and wind velocity were influential for red spider mites, providing farmers with actionable insights for timely pest control.
O. S. Mazari and A. Sebaa and F. Martínez-Álvarez
Space-Time Clustering of Seismicity in Algeria Conference
CSA 6th Conference on Computing Systems and Applications, Lecture Notes in Networks and Systems 2024.
Abstract | Links | BibTeX | Tags: clustering, natural disasters
@conference{CSA24_Mazari,
title = {Space-Time Clustering of Seismicity in Algeria},
author = {O. S. Mazari and A. Sebaa and F. Martínez-Álvarez},
url = {https://link.springer.com/chapter/10.1007/978-3-031-71848-9_36},
doi = {https://doi.org/10.1007/978-3-031-71848-9_36},
year = {2024},
date = {2024-08-08},
booktitle = {CSA 6th Conference on Computing Systems and Applications},
pages = {396–405},
series = {Lecture Notes in Networks and Systems},
abstract = {Each year, earthquakes pose a significant threat to human life, attributed to their sudden and unpredictable nature. Over time, a heightened awareness of this phenomenon has driven increased attention from researchers and experts. This paper seeks to demonstrate the applicability of the k-means algorithm to seismic data, focusing on the identification of seismic zones in Algeria. Initially, we conducted a comprehensive review of existing literature on clustering seismic data, revealing an unexplored niche in the context of Algeria’s seismicity. Subsequently, we introduce our dataset comprising 5876 seismic events. A detailed explanation of the k-means algorithm is provided, with a breakdown of each parameter. Visualization of our findings, including determining the optimal value for k using Elbow and Silhouette scores, is presented and thoroughly discussed. In conclusion, we identify and delineate the seismic zones in Algeria, highlighting the four most critical regions encapsulating these zones. This study contributes to a better understanding of seismic patterns in Algeria, potentially aiding in the development of more effective earthquake preparedness and mitigation strategies.},
keywords = {clustering, natural disasters},
pubstate = {published},
tppubtype = {conference}
}
Each year, earthquakes pose a significant threat to human life, attributed to their sudden and unpredictable nature. Over time, a heightened awareness of this phenomenon has driven increased attention from researchers and experts. This paper seeks to demonstrate the applicability of the k-means algorithm to seismic data, focusing on the identification of seismic zones in Algeria. Initially, we conducted a comprehensive review of existing literature on clustering seismic data, revealing an unexplored niche in the context of Algeria’s seismicity. Subsequently, we introduce our dataset comprising 5876 seismic events. A detailed explanation of the k-means algorithm is provided, with a breakdown of each parameter. Visualization of our findings, including determining the optimal value for k using Elbow and Silhouette scores, is presented and thoroughly discussed. In conclusion, we identify and delineate the seismic zones in Algeria, highlighting the four most critical regions encapsulating these zones. This study contributes to a better understanding of seismic patterns in Algeria, potentially aiding in the development of more effective earthquake preparedness and mitigation strategies.
M. J. Jiménez-Navarro and M. Martínez-Ballesteros and I. S. Brito and F. Martínez-Álvarez and G. Asencio-Cortés
Embedded feature selection for neural networks via learnable drop layer Journal Article
In: Logic Journal of the IGPL, pp. jzae062, 2024.
Abstract | Links | BibTeX | Tags: deep learning, feature selection, time series
@article{JIMENEZ-NAVARRO24b,
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://academic.oup.com/jigpal/advance-article/doi/10.1093/jigpal/jzae062/7689640},
doi = {https://doi.org/10.1093/jigpal/jzae062},
year = {2024},
date = {2024-07-06},
urldate = {2024-07-06},
journal = {Logic Journal of the IGPL},
pages = {jzae062},
abstract = {Feature selection is a widely studied technique whose goal is to reduce the dimensionality of the problem by removing irrelevant features. It has multiple benefits, such as improved efficacy, efficiency and interpretability of almost any type of machine learning model. Feature selection techniques may be divided into three main categories, depending on the process used to remove the features known as Filter, Wrapper and Embedded. Embedded methods are usually the preferred feature selection method that efficiently obtains a selection of the most relevant features of the model. However, not all models support an embedded feature selection that forces the use of a different method, reducing the efficiency and reliability of the selection. Neural networks are an example of a model that does not support embedded feature selection. As neural networks have shown to provide remarkable results in multiple scenarios such as classification and regression, sometimes in an ensemble with a model that includes an embedded feature selection, we attempt to embed a feature selection process with a general-purpose methodology. In this work, we propose a novel general-purpose layer for neural networks that removes the influence of irrelevant features. The Feature-Aware Drop Layer is included at the top of the neural network and trained during the backpropagation process without any additional parameters. Our methodology is tested with 17 datasets for classification and regression tasks, including data from different fields such as Health, Economic and Environment, among others. The results show remarkable improvements compared to three different feature selection approaches, with reliable, efficient and effective results.},
keywords = {deep learning, feature selection, time series},
pubstate = {published},
tppubtype = {article}
}
Feature selection is a widely studied technique whose goal is to reduce the dimensionality of the problem by removing irrelevant features. It has multiple benefits, such as improved efficacy, efficiency and interpretability of almost any type of machine learning model. Feature selection techniques may be divided into three main categories, depending on the process used to remove the features known as Filter, Wrapper and Embedded. Embedded methods are usually the preferred feature selection method that efficiently obtains a selection of the most relevant features of the model. However, not all models support an embedded feature selection that forces the use of a different method, reducing the efficiency and reliability of the selection. Neural networks are an example of a model that does not support embedded feature selection. As neural networks have shown to provide remarkable results in multiple scenarios such as classification and regression, sometimes in an ensemble with a model that includes an embedded feature selection, we attempt to embed a feature selection process with a general-purpose methodology. In this work, we propose a novel general-purpose layer for neural networks that removes the influence of irrelevant features. The Feature-Aware Drop Layer is included at the top of the neural network and trained during the backpropagation process without any additional parameters. Our methodology is tested with 17 datasets for classification and regression tasks, including data from different fields such as Health, Economic and Environment, among others. The results show remarkable improvements compared to three different feature selection approaches, with reliable, efficient and effective results.
F. Rodríguez-Díaz and J. F. Torres and D. Gutiérrez-Avilés and A. Troncoso and F. Martínez-Álvarez
CAEPIA Conference of the Spanish Association for Artificial Intelligence, Lecture Notes in Artificial Intelligence 2024.
Abstract | Links | BibTeX | Tags: quantum computing
@conference{RODRIGUEZ-DIAZ24b,
title = {An Experimental Comparison of Qiskit and Pennylane for Hybrid Quantum-Classical Support Vector Machines},
author = {F. Rodríguez-Díaz and J. F. Torres and D. Gutiérrez-Avilés and A. Troncoso and F. Martínez-Álvarez},
url = {https://link.springer.com/chapter/10.1007/978-3-031-62799-6_13},
doi = {https://doi.org/10.1007/978-3-031-62799-6_13},
year = {2024},
date = {2024-06-06},
booktitle = {CAEPIA Conference of the Spanish Association for Artificial Intelligence},
pages = {121-130},
series = {Lecture Notes in Artificial Intelligence},
abstract = {Quantum computing holds great promise for enhancing machine learning algorithms, particularly by integrating classical and quantum techniques. This study compares two prominent quantum development frameworks, Qiskit and Pennylane, focusing on their suitability for hybrid quantum-classical support vector machines with quantum kernels. Our analysis reveals that Qiskit requires less theoretical information to be used, while Pennylane demonstrates superior performance in terms of execution time. Although both frameworks exhibit variances, our experiments reveal that Qiskit consistently yields superior classification accuracy compared to Pennylane when training classifiers with quantum kernels. Additionally, our results suggest that the performance of both frameworks remains stable for up to 20 qubits, indicating their suitability for practical applications. Overall, our findings provide valuable insights into the strengths and limitations of Qiskit and Pennylane for hybrid quantum-classical machine learning.},
keywords = {quantum computing},
pubstate = {published},
tppubtype = {conference}
}
Quantum computing holds great promise for enhancing machine learning algorithms, particularly by integrating classical and quantum techniques. This study compares two prominent quantum development frameworks, Qiskit and Pennylane, focusing on their suitability for hybrid quantum-classical support vector machines with quantum kernels. Our analysis reveals that Qiskit requires less theoretical information to be used, while Pennylane demonstrates superior performance in terms of execution time. Although both frameworks exhibit variances, our experiments reveal that Qiskit consistently yields superior classification accuracy compared to Pennylane when training classifiers with quantum kernels. Additionally, our results suggest that the performance of both frameworks remains stable for up to 20 qubits, indicating their suitability for practical applications. Overall, our findings provide valuable insights into the strengths and limitations of Qiskit and Pennylane for hybrid quantum-classical machine learning.
A. R. Troncoso-García and M. J. Jiménez-Navarro and F. Martínez-Álvarez and A. Troncoso
Ground-Level Ozone Forecasting using Explainable Machine Learning Conference
CAEPIA Conference of the Spanish Association for Artificial Intelligence, vol. 14640, Lecture Notes in Artificial Intelligence 2024.
Abstract | Links | BibTeX | Tags: forecasting, time series, XAI
@conference{TRONCOSO-GARCIA24c,
title = {Ground-Level Ozone Forecasting using Explainable Machine Learning},
author = {A. R. Troncoso-García and M. J. Jiménez-Navarro and F. Martínez-Álvarez and A. Troncoso},
url = {https://link.springer.com/chapter/10.1007/978-3-031-62799-6_8},
doi = {https://doi.org/10.1007/978-3-031-62799-6_8},
year = {2024},
date = {2024-06-06},
booktitle = {CAEPIA Conference of the Spanish Association for Artificial Intelligence},
volume = {14640},
pages = {71-80},
series = {Lecture Notes in Artificial Intelligence},
abstract = {The ozone concentration at ground level is a pivotal indicator of air quality, as elevated ozone levels can lead to adverse effects on the environment. In this study various machine learning models for ground-level ozone forecasting are optimised using a Bayesian technique. Predictions are obtained 24 h in advance using historical ozone data and related environmental variables, including meteorological measurements and other air quality indicators. The results indicated that the Extra Trees model emerges as the optimal solution, showcasing competitive performance alongside reasonable training times. Furthermore, an explainable artificial intelligence technique is applied to enhance the interpretability of model predictions, providing insights into the contribution of input features to the predictions computed by the model. The features identified as important, namely PM10, air temperature and CO2 concentration, are validated as key factors in the literature to forecast ground-level ozone concentration.},
keywords = {forecasting, time series, XAI},
pubstate = {published},
tppubtype = {conference}
}
The ozone concentration at ground level is a pivotal indicator of air quality, as elevated ozone levels can lead to adverse effects on the environment. In this study various machine learning models for ground-level ozone forecasting are optimised using a Bayesian technique. Predictions are obtained 24 h in advance using historical ozone data and related environmental variables, including meteorological measurements and other air quality indicators. The results indicated that the Extra Trees model emerges as the optimal solution, showcasing competitive performance alongside reasonable training times. Furthermore, an explainable artificial intelligence technique is applied to enhance the interpretability of model predictions, providing insights into the contribution of input features to the predictions computed by the model. The features identified as important, namely PM10, air temperature and CO2 concentration, are validated as key factors in the literature to forecast ground-level ozone concentration.
M. J. Jiménez-Navarro and M. Martínez-Ballesteros and F. Martínez-Álvarez and G. Asencio-Cortés
Explaining deep learning models for ozone pollution prediction via embedded feature selection Journal Article
In: Applied Soft Computing, vol. 157, pp. 111504, 2024.
Abstract | Links | BibTeX | Tags: deep learning, feature selection, time series, XAI
@article{JIMENEZ-NAVARRO24,
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://www.sciencedirect.com/science/article/pii/S1568494624002783},
doi = {https://doi.org/10.1016/j.asoc.2024.111504},
year = {2024},
date = {2024-04-04},
journal = {Applied Soft Computing},
volume = {157},
pages = {111504},
abstract = {Ambient air pollution is a pervasive global issue that poses significant health risks. Among pollutants, ozone (O3) is responsible for an estimated 1 to 1.2 million premature deaths yearly. Furthermore, O3 adversely affects climate warming, crop productivity, and more. Its formation occurs when nitrogen oxides and volatile organic compounds react with short-wavelength solar radiation. Consequently, urban areas with high traffic volume and elevated temperatures are particularly prone to elevated O3 levels, which pose a significant health risk to their inhabitants. In response to this problem, many countries have developed web and mobile applications that provide real-time air pollution information using sensor data. However, while these applications offer valuable insight into current pollution levels, predicting future pollutant behavior is crucial for effective planning and mitigation strategies. Therefore, our main objectives are to develop accurate and efficient prediction models and identify the key factors that influence O3 levels. We adopt a time series forecasting approach to address these objectives, which allows us to analyze and predict O3 future behavior. Additionally, we tackle the feature selection problem to identify the most relevant features and periods that contribute to prediction accuracy by introducing a novel method called the Time Selection Layer in Deep Learning models, which significantly improves model performance, reduces complexity, and enhances interpretability. Our study focuses on data collected from five representative areas in Seville, Cordova, and Jaen provinces in Spain, using multiple sensors to capture comprehensive pollution data. We compare the performance of three models: Lasso, Decision Tree, and Deep Learning with and without incorporating the Time Selection Layer. Our results demonstrate that including the Time Selection Layer significantly enhances the effectiveness and interpretability of Deep Learning models, achieving an average effectiveness improvement of 9% across all monitored areas.},
keywords = {deep learning, feature selection, time series, XAI},
pubstate = {published},
tppubtype = {article}
}
Ambient air pollution is a pervasive global issue that poses significant health risks. Among pollutants, ozone (O3) is responsible for an estimated 1 to 1.2 million premature deaths yearly. Furthermore, O3 adversely affects climate warming, crop productivity, and more. Its formation occurs when nitrogen oxides and volatile organic compounds react with short-wavelength solar radiation. Consequently, urban areas with high traffic volume and elevated temperatures are particularly prone to elevated O3 levels, which pose a significant health risk to their inhabitants. In response to this problem, many countries have developed web and mobile applications that provide real-time air pollution information using sensor data. However, while these applications offer valuable insight into current pollution levels, predicting future pollutant behavior is crucial for effective planning and mitigation strategies. Therefore, our main objectives are to develop accurate and efficient prediction models and identify the key factors that influence O3 levels. We adopt a time series forecasting approach to address these objectives, which allows us to analyze and predict O3 future behavior. Additionally, we tackle the feature selection problem to identify the most relevant features and periods that contribute to prediction accuracy by introducing a novel method called the Time Selection Layer in Deep Learning models, which significantly improves model performance, reduces complexity, and enhances interpretability. Our study focuses on data collected from five representative areas in Seville, Cordova, and Jaen provinces in Spain, using multiple sensors to capture comprehensive pollution data. We compare the performance of three models: Lasso, Decision Tree, and Deep Learning with and without incorporating the Time Selection Layer. Our results demonstrate that including the Time Selection Layer significantly enhances the effectiveness and interpretability of Deep Learning models, achieving an average effectiveness improvement of 9% across all monitored areas.
D. Gutiérrez-Avilés and J. F. Torres and F. Martínez-Álvarez and J. Cugliari
An evolutionary triclustering approach to discover electricity consumption patterns in France Conference
SAC 39th Annual ACM Symposium on Applied Computing, 2024.
Abstract | BibTeX | Tags: clustering, energy, time series
@conference{GUTIERREZ24_SAC,
title = {An evolutionary triclustering approach to discover electricity consumption patterns in France},
author = {D. Gutiérrez-Avilés and J. F. Torres and F. Martínez-Álvarez and J. Cugliari},
year = {2024},
date = {2024-02-04},
booktitle = {SAC 39th Annual ACM Symposium on Applied Computing},
pages = {386-394},
abstract = {Electricity consumption patterns are critical in shaping energy policies
and optimizing resource allocation. In pursuing a more sustainable
and efficient energy future, uncovering hidden consumption
patterns is paramount. This paper introduces an innovative approach,
leveraging evolutionary triclustering techniques, to unveil
previously undisclosed electricity consumption patterns in France.
By harnessing the power of triclustering algorithms, this research
provides a comprehensive analysis of electricity usage across various
dimensions, shedding light on intricate relationships among
variables. Using this novel method, the study reveals concealed
patterns and offers insights that can inform decision-makers and
stakeholders in the energy sector. The findings contribute to a better
understanding of electricity consumption dynamics, aiding in
developing more targeted and effective energy management strategies.
This research represents a significant step forward in the quest
for sustainable energy solutions and underscores the potential of
evolutionary triclustering as a valuable tool in uncovering complex
consumption patterns.},
keywords = {clustering, energy, time series},
pubstate = {published},
tppubtype = {conference}
}
Electricity consumption patterns are critical in shaping energy policies
and optimizing resource allocation. In pursuing a more sustainable
and efficient energy future, uncovering hidden consumption
patterns is paramount. This paper introduces an innovative approach,
leveraging evolutionary triclustering techniques, to unveil
previously undisclosed electricity consumption patterns in France.
By harnessing the power of triclustering algorithms, this research
provides a comprehensive analysis of electricity usage across various
dimensions, shedding light on intricate relationships among
variables. Using this novel method, the study reveals concealed
patterns and offers insights that can inform decision-makers and
stakeholders in the energy sector. The findings contribute to a better
understanding of electricity consumption dynamics, aiding in
developing more targeted and effective energy management strategies.
This research represents a significant step forward in the quest
for sustainable energy solutions and underscores the potential of
evolutionary triclustering as a valuable tool in uncovering complex
consumption patterns.
and optimizing resource allocation. In pursuing a more sustainable
and efficient energy future, uncovering hidden consumption
patterns is paramount. This paper introduces an innovative approach,
leveraging evolutionary triclustering techniques, to unveil
previously undisclosed electricity consumption patterns in France.
By harnessing the power of triclustering algorithms, this research
provides a comprehensive analysis of electricity usage across various
dimensions, shedding light on intricate relationships among
variables. Using this novel method, the study reveals concealed
patterns and offers insights that can inform decision-makers and
stakeholders in the energy sector. The findings contribute to a better
understanding of electricity consumption dynamics, aiding in
developing more targeted and effective energy management strategies.
This research represents a significant step forward in the quest
for sustainable energy solutions and underscores the potential of
evolutionary triclustering as a valuable tool in uncovering complex
consumption patterns.
R. Pérez-Chacón and G. Asencio-Cortés and A. Troncoso and F. Martínez-Álvarez
Pattern sequence-based algorithm for multivariate big data time series forecasting: Application to electricity consumption Journal Article
In: Future Generation Computer Systems, vol. 154, pp. 397-412, 2024.
Abstract | Links | BibTeX | Tags: big data, energy, forecasting, time series
@article{PEREZ24,
title = {Pattern sequence-based algorithm for multivariate big data time series forecasting: Application to electricity consumption},
author = {R. Pérez-Chacón and G. Asencio-Cortés and A. Troncoso and F. Martínez-Álvarez},
url = {https://www.sciencedirect.com/science/article/pii/S0167739X23004752},
doi = {https://doi.org/10.1016/j.future.2023.12.021},
year = {2024},
date = {2024-01-29},
journal = {Future Generation Computer Systems},
volume = {154},
pages = {397-412},
abstract = {Several interrelated variables typically characterize real-world processes, and a time series cannot be predicted without considering the influence that other time series might have on the target time series. This work proposes a novel algorithm to forecast multivariate big data time series. This new general-purpose approach consists first of a previous pattern recognition performed jointly using all time series that form the multivariate time series and then predicts the target time series by searching for similarities between pattern sequences. The proposed algorithm is designed to tackle multivariate time series forecasting problems within the context of big data. In particular, the algorithm has been developed with a distributed nature to enhance its efficiency in analyzing and processing large volumes of data. Moreover, the algorithm is straightforward to use, with only two parameters needing adjustment. Another advantage of the MV-bigPSF algorithm is its ability to perform multi-step forecasting, which is particularly useful in many practical applications. To evaluate the algorithm’s performance, real-world data from Uruguay’s power consumption has been utilized. Specifically, MV-bigPSF has been compared with both univariate and multivariate methods. Regarding the univariate ones, MV-bigPSF improved 12.8% in MAPE compared to the second-best method. Regarding the multivariate comparison, MV-bigPSF improved 44.8% in MAPE with respect to the second most accurate method. Regarding efficiency, the execution time of MV-bigPSF was 1.83 times faster than the second-fastest multivariate method, both in a single-core environment. Therefore, the proposed algorithm can be a valuable tool for practitioners and researchers working in multivariate time series forecasting, particularly in big data applications.},
keywords = {big data, energy, forecasting, time series},
pubstate = {published},
tppubtype = {article}
}
Several interrelated variables typically characterize real-world processes, and a time series cannot be predicted without considering the influence that other time series might have on the target time series. This work proposes a novel algorithm to forecast multivariate big data time series. This new general-purpose approach consists first of a previous pattern recognition performed jointly using all time series that form the multivariate time series and then predicts the target time series by searching for similarities between pattern sequences. The proposed algorithm is designed to tackle multivariate time series forecasting problems within the context of big data. In particular, the algorithm has been developed with a distributed nature to enhance its efficiency in analyzing and processing large volumes of data. Moreover, the algorithm is straightforward to use, with only two parameters needing adjustment. Another advantage of the MV-bigPSF algorithm is its ability to perform multi-step forecasting, which is particularly useful in many practical applications. To evaluate the algorithm’s performance, real-world data from Uruguay’s power consumption has been utilized. Specifically, MV-bigPSF has been compared with both univariate and multivariate methods. Regarding the univariate ones, MV-bigPSF improved 12.8% in MAPE compared to the second-best method. Regarding the multivariate comparison, MV-bigPSF improved 44.8% in MAPE with respect to the second most accurate method. Regarding efficiency, the execution time of MV-bigPSF was 1.83 times faster than the second-fastest multivariate method, both in a single-core environment. Therefore, the proposed algorithm can be a valuable tool for practitioners and researchers working in multivariate time series forecasting, particularly in big data applications.
C. G. García-Soto and J. F. Torres and M. A. Zamora-Izquierdo and J. Palma and A. Troncoso
Water consumption time series forecasting in urban centers using deep neural networks Journal Article
In: Applied Water Science, vol. 14, pp. 1-14, 2024.
Abstract | Links | BibTeX | Tags: deep learning, forecasting, time series
@article{GARCIA-SOTO24,
title = {Water consumption time series forecasting in urban centers using deep neural networks},
author = {C. G. García-Soto and J. F. Torres and M. A. Zamora-Izquierdo and J. Palma and A. Troncoso},
url = {https://link.springer.com/article/10.1007/s13201-023-02072-4},
doi = {https://doi.org/10.1007/s13201-023-02072-4},
year = {2024},
date = {2024-01-12},
journal = {Applied Water Science},
volume = {14},
pages = {1-14},
abstract = {The time series analysis and prediction techniques are highly valued in many application felds, such as economy, medicine and biology, environmental sciences or meteorology, among others. In the last years, there is a growing interest in the sustainable and optimal management of a resource as scarce as essential: the water. Forecasting techniques for water management can be used for diferent time horizons from the planning of constructions that can respond to long-term needs, to the detection of anomalies in the operation of facilities or the optimization of the operation in the short and medium term. In this paper, a deep neural network is specifcally designed to predict water consumption in the short-term. Results are reported using the time series of water consumption for a year and a half measured with 10-min frequency in the city of Murcia, the seventh largest city in Spain by number of inhabitants. The results are compared with K Nearest Neighbors, Random Forest, Extreme Gradient Boosting, Seasonal Autoregressive Integrated Moving Average and two persistence models as naive methods, showing the proposed deep learning model the most accurate results.},
keywords = {deep learning, forecasting, time series},
pubstate = {published},
tppubtype = {article}
}
The time series analysis and prediction techniques are highly valued in many application felds, such as economy, medicine and biology, environmental sciences or meteorology, among others. In the last years, there is a growing interest in the sustainable and optimal management of a resource as scarce as essential: the water. Forecasting techniques for water management can be used for diferent time horizons from the planning of constructions that can respond to long-term needs, to the detection of anomalies in the operation of facilities or the optimization of the operation in the short and medium term. In this paper, a deep neural network is specifcally designed to predict water consumption in the short-term. Results are reported using the time series of water consumption for a year and a half measured with 10-min frequency in the city of Murcia, the seventh largest city in Spain by number of inhabitants. The results are compared with K Nearest Neighbors, Random Forest, Extreme Gradient Boosting, Seasonal Autoregressive Integrated Moving Average and two persistence models as naive methods, showing the proposed deep learning model the most accurate results.
F. Martínez-Álvarez and R. Scitovski and C. Rubio-Escudero and A. Morales-Esteban
Emerging trends in big data analytics and natural disasters (Editorial) Journal Article
In: Computers and Geosciences, vol. 182, pp. 105465, 2024.
Links | BibTeX | Tags: big data, natural disasters, time series
@article{MARTINEZ24,
title = {Emerging trends in big data analytics and natural disasters (Editorial)},
author = {F. Martínez-Álvarez and R. Scitovski and C. Rubio-Escudero and A. Morales-Esteban},
url = {https://www.sciencedirect.com/science/article/pii/S0098300423001693},
doi = {https://doi.org/10.1016/j.cageo.2023.105465},
year = {2024},
date = {2024-01-01},
journal = {Computers and Geosciences},
volume = {182},
pages = {105465},
keywords = {big data, natural disasters, time series},
pubstate = {published},
tppubtype = {article}
}
M. García-Torres and D. P. Pinto-Roa and C. Núñez-Castillo and B. Quiñonez and G. Vázquez and M. Allegretti and M. E. García-Diaz
Feature selection applied to QoS/QoE modeling on video and web-based mobile data services: An ordinal approach Journal Article
In: Computer Communications, 2024.
Abstract | Links | BibTeX | Tags: feature selection
@article{garcia2024feature,
title = {Feature selection applied to QoS/QoE modeling on video and web-based mobile data services: An ordinal approach},
author = {M. García-Torres and D. P. Pinto-Roa and C. Núñez-Castillo and B. Quiñonez and G. Vázquez and M. Allegretti and M. E. García-Diaz},
url = {https://www.sciencedirect.com/science/article/pii/S0140366424000410},
doi = {10.1016/j.comcom.2024.02.004},
year = {2024},
date = {2024-01-01},
journal = {Computer Communications},
publisher = {Elsevier},
abstract = {Nowadays, mobile service providers perceive the user experience as a reliable indicator of the quality associated to a service. Given a set of Quality of Service (QoS) factors, the aim is to predict the Quality of Experience (QoE), measured in terms of the Mean Opinion Score (MOS). Although this problem is receiving much attention, there are still some challenges that require more research in order to find effective solutions for meeting user’s expectation in terms of service quality. A core challenge in this topic refers to the analysis of the contribution of each factor to the QoS/QoE Model. In this work, we study the mapping between QoS and QoE on video and web-based services using a machine learning approach. For such purpose, we design a lab-testing methodology to emulate different cellular transmission network scenarios. Then, we address the problem of inducing a predictive model and identifying relevant QoS factors. Results suggest that bandwidth is a key factor when analyzing user’s perception of service quality.},
keywords = {feature selection},
pubstate = {published},
tppubtype = {article}
}
Nowadays, mobile service providers perceive the user experience as a reliable indicator of the quality associated to a service. Given a set of Quality of Service (QoS) factors, the aim is to predict the Quality of Experience (QoE), measured in terms of the Mean Opinion Score (MOS). Although this problem is receiving much attention, there are still some challenges that require more research in order to find effective solutions for meeting user’s expectation in terms of service quality. A core challenge in this topic refers to the analysis of the contribution of each factor to the QoS/QoE Model. In this work, we study the mapping between QoS and QoE on video and web-based services using a machine learning approach. For such purpose, we design a lab-testing methodology to emulate different cellular transmission network scenarios. Then, we address the problem of inducing a predictive model and identifying relevant QoS factors. Results suggest that bandwidth is a key factor when analyzing user’s perception of service quality.
F. Morales-Mareco and M. García-Torres and F. Divina and D. H Stalder and C. Sauer
Machine learning for electric energy consumption forecasting: Application to the Paraguayan system Journal Article
In: Logic Journal of the IGPL, pp. jzae035, 2024.
Abstract | Links | BibTeX | Tags: energy
@article{morales2024machine,
title = {Machine learning for electric energy consumption forecasting: Application to the Paraguayan system},
author = { F. Morales-Mareco and M. García-Torres and F. Divina and D. H Stalder and C. Sauer},
url = {https://academic.oup.com/jigpal/advance-article-abstract/doi/10.1093/jigpal/jzae035/7639120},
doi = {10.1093/jigpal/jzae035},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Logic Journal of the IGPL},
pages = {jzae035},
publisher = {Oxford University Press},
abstract = {In this paper we address the problem of short-term
electric energy prediction using a time series forecasting approach
applied to data generated by a Paraguayan electricity distribution
provider. The dataset used in this work contains data collected over a
three-year period. This is the first time that these data have been
used; therefore, a preprocessing phase of the data was also performed.
In particular, we propose a comparative study of various machine
learning and statistical strategies with the objective of predicting the
electric energy consumption for a given prediction horizon, in our case
seven days, using historical data. In this paper we have tested the
effectiveness of the techniques with different historical window sizes.
Specifically, we considered two ensemble strategies, a neural network, a
deep learning technique and linear regression. Moreover, in this study,
we tested whether the inclusion of meteorological data can help achieve
better predictions. In particular, we considered data regarding
temperature, humidity, wind speed and atmospheric pressure registered
during the three-year period of data collection. The results show that,
in general, the deep learning approach obtains the best results and that
such results are obtained when meteorological data are also considered.
Moreover, when meteorological data is used, a smaller historical window
size is required to obtain precise predictions.},
keywords = {energy},
pubstate = {published},
tppubtype = {article}
}
In this paper we address the problem of short-term
electric energy prediction using a time series forecasting approach
applied to data generated by a Paraguayan electricity distribution
provider. The dataset used in this work contains data collected over a
three-year period. This is the first time that these data have been
used; therefore, a preprocessing phase of the data was also performed.
In particular, we propose a comparative study of various machine
learning and statistical strategies with the objective of predicting the
electric energy consumption for a given prediction horizon, in our case
seven days, using historical data. In this paper we have tested the
effectiveness of the techniques with different historical window sizes.
Specifically, we considered two ensemble strategies, a neural network, a
deep learning technique and linear regression. Moreover, in this study,
we tested whether the inclusion of meteorological data can help achieve
better predictions. In particular, we considered data regarding
temperature, humidity, wind speed and atmospheric pressure registered
during the three-year period of data collection. The results show that,
in general, the deep learning approach obtains the best results and that
such results are obtained when meteorological data are also considered.
Moreover, when meteorological data is used, a smaller historical window
size is required to obtain precise predictions.
electric energy prediction using a time series forecasting approach
applied to data generated by a Paraguayan electricity distribution
provider. The dataset used in this work contains data collected over a
three-year period. This is the first time that these data have been
used; therefore, a preprocessing phase of the data was also performed.
In particular, we propose a comparative study of various machine
learning and statistical strategies with the objective of predicting the
electric energy consumption for a given prediction horizon, in our case
seven days, using historical data. In this paper we have tested the
effectiveness of the techniques with different historical window sizes.
Specifically, we considered two ensemble strategies, a neural network, a
deep learning technique and linear regression. Moreover, in this study,
we tested whether the inclusion of meteorological data can help achieve
better predictions. In particular, we considered data regarding
temperature, humidity, wind speed and atmospheric pressure registered
during the three-year period of data collection. The results show that,
in general, the deep learning approach obtains the best results and that
such results are obtained when meteorological data are also considered.
Moreover, when meteorological data is used, a smaller historical window
size is required to obtain precise predictions.
G. Sosa-Cabrera and S. Gómez-Guerrero and M. García-Torres and C. E Schaerer
Feature selection: A perspective on inter-attribute cooperation Journal Article
In: International Journal of Data Science and Analytics, vol. 17, no. 2, pp. 139–151, 2024.
Abstract | Links | BibTeX | Tags: feature selection
@article{sosa2024feature,
title = {Feature selection: A perspective on inter-attribute cooperation},
author = { G. Sosa-Cabrera and S. Gómez-Guerrero and M. García-Torres and C. E Schaerer},
url = {https://link.springer.com/article/10.1007/s41060-023-00439-z},
doi = {10.1007/s41060-023-00439-z},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {International Journal of Data Science and Analytics},
volume = {17},
number = {2},
pages = {139–151},
publisher = {Springer},
abstract = {High-dimensional datasets depict a challenge for learning
tasks in data mining and machine learning. Feature selection is an
effective technique in dealing with dimensionality reduction. It is
often an essential data processing step prior to applying a learning
algorithm. Over the decades, filter feature selection methods have
evolved from simple univariate relevance ranking algorithms to more
sophisticated relevance-redundancy trade-offs and to multivariate
dependencies-based approaches in recent years. This tendency to capture
multivariate dependence aims at obtaining unique information about the
class from the intercooperation among features. This paper presents a
comprehensive survey of the state-of-the-art work on filter feature
selection methods assisted by feature intercooperation, and summarizes
the contributions of different approaches found in the literature.
Furthermore, current issues and challenges are introduced to identify
promising future research and development.},
keywords = {feature selection},
pubstate = {published},
tppubtype = {article}
}
High-dimensional datasets depict a challenge for learning
tasks in data mining and machine learning. Feature selection is an
effective technique in dealing with dimensionality reduction. It is
often an essential data processing step prior to applying a learning
algorithm. Over the decades, filter feature selection methods have
evolved from simple univariate relevance ranking algorithms to more
sophisticated relevance-redundancy trade-offs and to multivariate
dependencies-based approaches in recent years. This tendency to capture
multivariate dependence aims at obtaining unique information about the
class from the intercooperation among features. This paper presents a
comprehensive survey of the state-of-the-art work on filter feature
selection methods assisted by feature intercooperation, and summarizes
the contributions of different approaches found in the literature.
Furthermore, current issues and challenges are introduced to identify
promising future research and development.
tasks in data mining and machine learning. Feature selection is an
effective technique in dealing with dimensionality reduction. It is
often an essential data processing step prior to applying a learning
algorithm. Over the decades, filter feature selection methods have
evolved from simple univariate relevance ranking algorithms to more
sophisticated relevance-redundancy trade-offs and to multivariate
dependencies-based approaches in recent years. This tendency to capture
multivariate dependence aims at obtaining unique information about the
class from the intercooperation among features. This paper presents a
comprehensive survey of the state-of-the-art work on filter feature
selection methods assisted by feature intercooperation, and summarizes
the contributions of different approaches found in the literature.
Furthermore, current issues and challenges are introduced to identify
promising future research and development.
F. Divina and M. García-Torres and F. Gómez-Vela and D. S. Rodriguez-Baena
A stacking ensemble learning for Iberian pigs activity prediction: a time series forecasting approach Journal Article
In: AIMS Mathematics, vol. 9, no. 5, pp. 13358–13384, 2024.
Abstract | Links | BibTeX | Tags: time series
@article{divina2024stacking,
title = {A stacking ensemble learning for Iberian pigs activity prediction: a time series forecasting approach},
author = { F. Divina and M. García-Torres and F. Gómez-Vela and D. S. Rodriguez-Baena},
url = {https://www.aimspress.com/article/doi/10.3934/math.2024652},
doi = {10.3934/math.2024652},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {AIMS Mathematics},
volume = {9},
number = {5},
pages = {13358–13384},
abstract = {Automatic determination of abnormal animal activities can
be helpful for the timely detection of signs of health and welfare
problems. Usually, this problem is addressed as a classification
problem, which typically requires manual annotation of behaviors. This
manual annotation can introduce noise into the data and may not always
be possible. This motivated us to address the problem as a time-series
forecasting problem in which the activity of an animal can be predicted.
In this work, different machine learning techniques were tested to
obtain activity patterns for Iberian pigs. In particular, we propose a
novel stacking ensemble learning approach that combines base learners
with meta-learners to obtain the final predictive model. Results confirm
the superior performance of the proposed method relative to the other
tested strategies. We also explored the possibility of using predictive
models trained on an animal to predict the activity of different animals
on the same farm. As expected, the predictive performance degrades in
this case, but it remains acceptable. The proposed method could be
integrated into a monitoring system that may have the potential to
transform the way farm animals are monitored, improving their health and
welfare conditions, for example, by allowing the early detection of a
possible health problem.},
keywords = {time series},
pubstate = {published},
tppubtype = {article}
}
Automatic determination of abnormal animal activities can
be helpful for the timely detection of signs of health and welfare
problems. Usually, this problem is addressed as a classification
problem, which typically requires manual annotation of behaviors. This
manual annotation can introduce noise into the data and may not always
be possible. This motivated us to address the problem as a time-series
forecasting problem in which the activity of an animal can be predicted.
In this work, different machine learning techniques were tested to
obtain activity patterns for Iberian pigs. In particular, we propose a
novel stacking ensemble learning approach that combines base learners
with meta-learners to obtain the final predictive model. Results confirm
the superior performance of the proposed method relative to the other
tested strategies. We also explored the possibility of using predictive
models trained on an animal to predict the activity of different animals
on the same farm. As expected, the predictive performance degrades in
this case, but it remains acceptable. The proposed method could be
integrated into a monitoring system that may have the potential to
transform the way farm animals are monitored, improving their health and
welfare conditions, for example, by allowing the early detection of a
possible health problem.
be helpful for the timely detection of signs of health and welfare
problems. Usually, this problem is addressed as a classification
problem, which typically requires manual annotation of behaviors. This
manual annotation can introduce noise into the data and may not always
be possible. This motivated us to address the problem as a time-series
forecasting problem in which the activity of an animal can be predicted.
In this work, different machine learning techniques were tested to
obtain activity patterns for Iberian pigs. In particular, we propose a
novel stacking ensemble learning approach that combines base learners
with meta-learners to obtain the final predictive model. Results confirm
the superior performance of the proposed method relative to the other
tested strategies. We also explored the possibility of using predictive
models trained on an animal to predict the activity of different animals
on the same farm. As expected, the predictive performance degrades in
this case, but it remains acceptable. The proposed method could be
integrated into a monitoring system that may have the potential to
transform the way farm animals are monitored, improving their health and
welfare conditions, for example, by allowing the early detection of a
possible health problem.
A. Gil-Gamboa and P. Paneque and O. Trull and A. Troncoso
Medium-term water consumption forecasting based on deep neural networks Journal Article
In: Expert Systems With Applications, vol. 247, pp. 123234, 2024.
Abstract | Links | BibTeX | Tags: deep learning, forecasting, time series
@article{ESWA2024,
title = {Medium-term water consumption forecasting based on deep neural networks},
author = {A. Gil-Gamboa and P. Paneque and O. Trull and A. Troncoso},
url = {https://www.sciencedirect.com/science/article/pii/S095741742400099X},
doi = {10.1016/j.eswa.2024.123234},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Expert Systems With Applications},
volume = {247},
pages = {123234},
abstract = {Water consumption forecasting is an essential tool for water management, as it allows for efficient planning and allocation of water resources, an undervalued but indispensable resource for all living beings. With the increasing demand for accurate and timely water forecasting, traditional forecasting methods are proving to be insufficient. Deep learning techniques, which have shown remarkable performance in a wide range of applications, offer a promising approach to address the challenges of water consumption forecasting. In this work, the use of deep learning models for medium-term water consumption forecasting of residential areas is explored. A deep feed-forward neural network is developed to predict water consumption of a company’s customers for the next quarter. First, customers are grouped according to their consumption as these customers include both household consumers and special consumers such as public swimming pools, sports halls or small industries. Then, a deep feed-forward neural network is designed for household customers by obtaining the optimal values for those hyperparameters that have a great influence on the network performance. Results are reported using a real-world dataset composed of the water consumption from 1999 to 2015 on a quarterly basis, corresponding to 3262 clients of a water supply company. Finally, the proposed algorithm is evaluated by comparing it with other reference algorithms including an LSTM network.},
keywords = {deep learning, forecasting, time series},
pubstate = {published},
tppubtype = {article}
}
Water consumption forecasting is an essential tool for water management, as it allows for efficient planning and allocation of water resources, an undervalued but indispensable resource for all living beings. With the increasing demand for accurate and timely water forecasting, traditional forecasting methods are proving to be insufficient. Deep learning techniques, which have shown remarkable performance in a wide range of applications, offer a promising approach to address the challenges of water consumption forecasting. In this work, the use of deep learning models for medium-term water consumption forecasting of residential areas is explored. A deep feed-forward neural network is developed to predict water consumption of a company’s customers for the next quarter. First, customers are grouped according to their consumption as these customers include both household consumers and special consumers such as public swimming pools, sports halls or small industries. Then, a deep feed-forward neural network is designed for household customers by obtaining the optimal values for those hyperparameters that have a great influence on the network performance. Results are reported using a real-world dataset composed of the water consumption from 1999 to 2015 on a quarterly basis, corresponding to 3262 clients of a water supply company. Finally, the proposed algorithm is evaluated by comparing it with other reference algorithms including an LSTM network.
J. F. Torres and M. Martinez-Ballesteros and A. Troncoso and F. Martínez-Álvarez
Special issue on Advances in Time Series Forecasting Journal Article
In: AIMS Mathematics, vol. 9, iss. 9, pp. 24163-24165, 2024.
@article{AIMS_Math2024,
title = {Special issue on Advances in Time Series Forecasting},
author = {J. F. Torres and M. Martinez-Ballesteros and A. Troncoso and F. Martínez-Álvarez},
url = {https://www.aimspress.com/article/doi/10.3934/math.20241174},
doi = {10.3934/math.20241174},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {AIMS Mathematics},
volume = {9},
issue = {9},
pages = {24163-24165},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
L. Melgar-García and A. Troncoso
A novel incremental ensemble learning for real-time explainable forecasting of electricity price Journal Article
In: Knowledge-Based Systems, vol. 305, pp. 112574, 2024.
Links | BibTeX | Tags: energy, IoT, time series, XAI
@article{Melgar2024,
title = {A novel incremental ensemble learning for real-time explainable forecasting of electricity price},
author = {L. Melgar-García and A. Troncoso},
url = {https://www.sciencedirect.com/science/article/pii/S0950705124012085},
doi = {https://doi.org/10.1016/j.knosys.2024.112574},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Knowledge-Based Systems},
volume = {305},
pages = {112574},
keywords = {energy, IoT, time series, XAI},
pubstate = {published},
tppubtype = {article}
}
A. M. Chacón-Maldonado and L. Melgar-García and G. Asencio-Cortés and A. Troncoso
A novel method based on hybrid deep learning with explainability for olive fruit pest forecasting Journal Article
In: Neural Computing and Applications, 2024.
Links | BibTeX | Tags: deep learning, precision agriculture, XAI
@article{Chacon2024,
title = {A novel method based on hybrid deep learning with explainability for olive fruit pest forecasting},
author = {A. M. Chacón-Maldonado and L. Melgar-García and G. Asencio-Cortés and A. Troncoso},
url = {https://link.springer.com/article/10.1007/s00521-024-10731-z},
doi = {https://doi.org/10.1007/s00521-024-10731-z},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Neural Computing and Applications},
keywords = {deep learning, precision agriculture, XAI},
pubstate = {published},
tppubtype = {article}
}
P. B. Corthis and G. P. Ramesh and M. García-Torres and R. Ruiz
Effective Identification and Authentication of Healthcare IoT Using Fog Computing with Hybrid Cryptographic Algorithm Journal Article
In: Symmetry, vol. 16, no. 6, pp. 726, 2024.
Abstract | Links | BibTeX | Tags: IoT
@article{corthis2024effective,
title = {Effective Identification and Authentication of Healthcare IoT Using Fog Computing with Hybrid Cryptographic Algorithm},
author = {P. B. Corthis and G. P. Ramesh and M. García-Torres and R. Ruiz},
doi = {10.3390/sym16060726},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Symmetry},
volume = {16},
number = {6},
pages = {726},
publisher = {MDPI},
abstract = {Currently, Internet of Things (IoT)-based cloud systems face several problems such as privacy leakage, failure in centralized operation, managing IoT devices, and malicious attacks. The data transmission
between the cloud and healthcare IoT needs trust and secure transmission of Electronic Health Records (EHRs). IoT-enabled healthcare equipment is seen in hospitals that have been implementing the technology for many
years. Nonetheless, medical agencies fail to consider the security risk associated with healthcare IoT devices, which are readily compromised and cause potential threats to authentication and encryption procedures.
Existing cloud computing methods like homomorphic encryption and the elliptic curve cryptography are unable to meet the security, identity, authentication, and security needs of healthcare IoT devices. The
majority of conventional healthcare IoT algorithms lack secure data transmission. Therefore, fog computing is introduced to overcome the problems of IoT device verification, authentication, and identification
for scalable and secure transmission of data. In this research manuscript, fog computing includes a hybrid mathematical model: Elliptic Curve Cryptography (ECC) and Proxy Re-encryption (PR) with Enhanced
Salp Swarm Algorithm (ESSA) for IoT device verification, identification, and authentication of EHRs. ESSA is incorporated into the PR algorithm to determine the optimal key size and parameters of the PR algorithm.
Specifically, in the ESSA, a Whale Optimization Algorithm (WOA) is integrated with the conventional Salp Swarm Algorithm (SSA) to enhance its global and local search processes. The primary objective of the
proposed mathematical model is to further secure data sharing in the real time services. The extensive experimental analysis shows that the proposed model approximately reduced 60 Milliseconds (ms) to 18
milliseconds of processing time and improved 25% to 3% of reliability, compared to the traditional cryptographic algorithms. Additionally, the proposed model obtains a communication cost of 4260 bits with a memory
usage of 680 bytes in the context of security analysis. url = https://www.mdpi.com/2073-8994/16/6/726},
keywords = {IoT},
pubstate = {published},
tppubtype = {article}
}
Currently, Internet of Things (IoT)-based cloud systems face several problems such as privacy leakage, failure in centralized operation, managing IoT devices, and malicious attacks. The data transmission
between the cloud and healthcare IoT needs trust and secure transmission of Electronic Health Records (EHRs). IoT-enabled healthcare equipment is seen in hospitals that have been implementing the technology for many
years. Nonetheless, medical agencies fail to consider the security risk associated with healthcare IoT devices, which are readily compromised and cause potential threats to authentication and encryption procedures.
Existing cloud computing methods like homomorphic encryption and the elliptic curve cryptography are unable to meet the security, identity, authentication, and security needs of healthcare IoT devices. The
majority of conventional healthcare IoT algorithms lack secure data transmission. Therefore, fog computing is introduced to overcome the problems of IoT device verification, authentication, and identification
for scalable and secure transmission of data. In this research manuscript, fog computing includes a hybrid mathematical model: Elliptic Curve Cryptography (ECC) and Proxy Re-encryption (PR) with Enhanced
Salp Swarm Algorithm (ESSA) for IoT device verification, identification, and authentication of EHRs. ESSA is incorporated into the PR algorithm to determine the optimal key size and parameters of the PR algorithm.
Specifically, in the ESSA, a Whale Optimization Algorithm (WOA) is integrated with the conventional Salp Swarm Algorithm (SSA) to enhance its global and local search processes. The primary objective of the
proposed mathematical model is to further secure data sharing in the real time services. The extensive experimental analysis shows that the proposed model approximately reduced 60 Milliseconds (ms) to 18
milliseconds of processing time and improved 25% to 3% of reliability, compared to the traditional cryptographic algorithms. Additionally, the proposed model obtains a communication cost of 4260 bits with a memory
usage of 680 bytes in the context of security analysis. url = https://www.mdpi.com/2073-8994/16/6/726
between the cloud and healthcare IoT needs trust and secure transmission of Electronic Health Records (EHRs). IoT-enabled healthcare equipment is seen in hospitals that have been implementing the technology for many
years. Nonetheless, medical agencies fail to consider the security risk associated with healthcare IoT devices, which are readily compromised and cause potential threats to authentication and encryption procedures.
Existing cloud computing methods like homomorphic encryption and the elliptic curve cryptography are unable to meet the security, identity, authentication, and security needs of healthcare IoT devices. The
majority of conventional healthcare IoT algorithms lack secure data transmission. Therefore, fog computing is introduced to overcome the problems of IoT device verification, authentication, and identification
for scalable and secure transmission of data. In this research manuscript, fog computing includes a hybrid mathematical model: Elliptic Curve Cryptography (ECC) and Proxy Re-encryption (PR) with Enhanced
Salp Swarm Algorithm (ESSA) for IoT device verification, identification, and authentication of EHRs. ESSA is incorporated into the PR algorithm to determine the optimal key size and parameters of the PR algorithm.
Specifically, in the ESSA, a Whale Optimization Algorithm (WOA) is integrated with the conventional Salp Swarm Algorithm (SSA) to enhance its global and local search processes. The primary objective of the
proposed mathematical model is to further secure data sharing in the real time services. The extensive experimental analysis shows that the proposed model approximately reduced 60 Milliseconds (ms) to 18
milliseconds of processing time and improved 25% to 3% of reliability, compared to the traditional cryptographic algorithms. Additionally, the proposed model obtains a communication cost of 4260 bits with a memory
usage of 680 bytes in the context of security analysis. url = https://www.mdpi.com/2073-8994/16/6/726
M. J. Jiménez-Navarro and M. Martínez-Ballesteros and F. Mártinez-Álvarez and A. Troncoso and G. Asencio-Cortés
From Simple to Complex: A Sequential Method for Enhancing Time Series Forecasting with Deep Learning Journal Article
In: Logic Journal of the IGPL, vol. 32, no. 6, pp. 986-1003, 2024.
Abstract | Links | BibTeX | Tags: deep learning, time series
@article{JIMENEZ-NAVARRO23a,
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. Mártinez-Álvarez and A. Troncoso and G. Asencio-Cortés},
url = {https://academic.oup.com/jigpal/advance-article/doi/10.1093/jigpal/jzae030/7670726},
doi = {10.1093/jigpal/jzae030},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Logic Journal of the IGPL},
volume = {32},
number = {6},
pages = {986-1003},
abstract = {Time series forecasting is a well-known deep learning application field in which previous data are used to predict the future behavior of the series. Recently, several deep learning approaches have been proposed in which several nonlinear functions are applied to the input to obtain the output. In this paper, we introduce a novel method to improve the performance of deep learning models in time series forecasting. This method divides the model into hierarchies or levels from simpler to more complex ones. Simpler levels handle smoothed versions of the input, whereas the most complex level processes the original time series. This method follows the human learning process where general/simpler tasks are performed first, and afterward, more precise/harder ones are accomplished.Our proposed methodology has been applied to the LSTM architecture, showing remarkable performance in various time series. In addition, a comparison is reported including a standard LSTM and novel methods such as DeepAR, Temporal Fusion Transformer (TFT), NBEATS and Echo State Network (ESN).},
keywords = {deep learning, time series},
pubstate = {published},
tppubtype = {article}
}
Time series forecasting is a well-known deep learning application field in which previous data are used to predict the future behavior of the series. Recently, several deep learning approaches have been proposed in which several nonlinear functions are applied to the input to obtain the output. In this paper, we introduce a novel method to improve the performance of deep learning models in time series forecasting. This method divides the model into hierarchies or levels from simpler to more complex ones. Simpler levels handle smoothed versions of the input, whereas the most complex level processes the original time series. This method follows the human learning process where general/simpler tasks are performed first, and afterward, more precise/harder ones are accomplished.Our proposed methodology has been applied to the LSTM architecture, showing remarkable performance in various time series. In addition, a comparison is reported including a standard LSTM and novel methods such as DeepAR, Temporal Fusion Transformer (TFT), NBEATS and Echo State Network (ESN).
2023
A. R. Troncoso-García and I. S. Brito and A. Troncoso and F. Mártinez-Álvarez
Explainable hybrid deep learning and Coronavirus Optimization Algorithm for improving evapotranspiration forecasting Journal Article
In: Computers and Electronics in Agriculture, vol. 215, pp. 108387, 2023.
Abstract | Links | BibTeX | Tags: deep learning, forecasting, precision agriculture, XAI
@article{TRONCOSO-GARCIA23b,
title = {Explainable hybrid deep learning and Coronavirus Optimization Algorithm for improving evapotranspiration forecasting},
author = {A. R. Troncoso-García and I. S. Brito and A. Troncoso and F. Mártinez-Álvarez},
url = {https://www.sciencedirect.com/science/article/pii/S0168169923007755},
doi = {https://doi.org/10.1016/j.compag.2023.108387},
year = {2023},
date = {2023-11-08},
journal = {Computers and Electronics in Agriculture},
volume = {215},
pages = {108387},
abstract = {Reference evapotranspiration is a critical hydrological measurement closely associated with agriculture. Accurate forecasting is vital in effective water management and crop planning in sustainable agriculture. In this study, the future values of reference evapotranspiration are forecasted by applying a recurrent long shortterm memory neural network optimized using the Coronavirus Optimization Algorithm, a novel bioinspired metaheuristic based on the spread of COVID-19. The input data is sourced from the Sistema Agrometeorológico para a Gestão da Rega no Alentejo, in Portugal, with meteorological data such as air temperature or wind speed. Several baseline models are applied to the same problem to facilitate comparisons, including support vector machines, multi-layer perceptron, Lasso and decision tree. The results demonstrate the successful forecasting performance of the proposed model and its potential in this field. In turn, to gain deeper insights into the model’s inner workings, the SHapley Additive exPlanation tool is applied for explainability. Consequently, the study identifies the most relevant variables for reference evapotranspiration forecasting, including previously measured evapotranspiration values. Additionally, a univariable model is tested using historic evapotranspiration values as input, offering a comparable performance with a considerable reduction of computational time},
keywords = {deep learning, forecasting, precision agriculture, XAI},
pubstate = {published},
tppubtype = {article}
}
Reference evapotranspiration is a critical hydrological measurement closely associated with agriculture. Accurate forecasting is vital in effective water management and crop planning in sustainable agriculture. In this study, the future values of reference evapotranspiration are forecasted by applying a recurrent long shortterm memory neural network optimized using the Coronavirus Optimization Algorithm, a novel bioinspired metaheuristic based on the spread of COVID-19. The input data is sourced from the Sistema Agrometeorológico para a Gestão da Rega no Alentejo, in Portugal, with meteorological data such as air temperature or wind speed. Several baseline models are applied to the same problem to facilitate comparisons, including support vector machines, multi-layer perceptron, Lasso and decision tree. The results demonstrate the successful forecasting performance of the proposed model and its potential in this field. In turn, to gain deeper insights into the model’s inner workings, the SHapley Additive exPlanation tool is applied for explainability. Consequently, the study identifies the most relevant variables for reference evapotranspiration forecasting, including previously measured evapotranspiration values. Additionally, a univariable model is tested using historic evapotranspiration values as input, offering a comparable performance with a considerable reduction of computational time
D. Hadjout and A. Sebaa and J. F. Torres and F. Mártinez-Álvarez
Electricity consumption forecasting with outliers handling based on clustering and deep learning with application to the Algerian market Journal Article
In: Expert Systems with Applications, vol. 227, pp. 120123, 2023.
Abstract | Links | BibTeX | Tags: clustering, deep learning, energy, time series
@article{HADJOUT23,
title = {Electricity consumption forecasting with outliers handling based on clustering and deep learning with application to the Algerian market},
author = {D. Hadjout and A. Sebaa and J. F. Torres and F. Mártinez-Álvarez},
url = {https://www.sciencedirect.com/science/article/abs/pii/S0957417423006255},
doi = {https://doi.org/10.1016/j.eswa.2023.120123},
year = {2023},
date = {2023-10-01},
journal = {Expert Systems with Applications},
volume = {227},
pages = {120123},
abstract = {The reduction of electricity loss and the effective management of electricity demand are vital operations for production and distribution electricity enterprises. To achieve these goals, accurate forecasts of aggregate and individual electricity consumers are necessary. A novel multistep forecasting method is developed to forecast medium-term electricity consumption of the Algerian economic sector. The proposed method goes through the following three steps: cleaning steps, clustering steps and forecasting step of each cluster. The aim of the first step is to detect and then replace outliers. To complete the first phase, Robust Exponential and Holt-Winters Smoothing algorithms are adapted. Then, to carry out accurate forecasting at a lowest level, K-Shape and K-Means clustering methods are utilized to extract similarities and identify customer consumption patterns as a second step. The third step entails developing a deep learning model based on Gated Recurrent Units to forecast the electricity consumption in each cluster. To validate the proposed method, we compared our results to the most known methods in literature like Autoregressive Integrated Moving Average, Seasonal Grey Model, LSTM networks, Temporal Convolutional Networks and two ensemble models. The results of several experiments conducted with 2000 electricity consumers during 14 years from an Algeria province (Bejaia) demonstrate that the proposed method provides remarkable prediction performances. Thus, prediction performances of the K-Shape-based clustering method reach much higher prediction accuracy. According to the MAPE metric, the results of the best predictions are equal to 2.04%. It is also notable that 87% of the clients have a considerably low prediction error.},
keywords = {clustering, deep learning, energy, time series},
pubstate = {published},
tppubtype = {article}
}
The reduction of electricity loss and the effective management of electricity demand are vital operations for production and distribution electricity enterprises. To achieve these goals, accurate forecasts of aggregate and individual electricity consumers are necessary. A novel multistep forecasting method is developed to forecast medium-term electricity consumption of the Algerian economic sector. The proposed method goes through the following three steps: cleaning steps, clustering steps and forecasting step of each cluster. The aim of the first step is to detect and then replace outliers. To complete the first phase, Robust Exponential and Holt-Winters Smoothing algorithms are adapted. Then, to carry out accurate forecasting at a lowest level, K-Shape and K-Means clustering methods are utilized to extract similarities and identify customer consumption patterns as a second step. The third step entails developing a deep learning model based on Gated Recurrent Units to forecast the electricity consumption in each cluster. To validate the proposed method, we compared our results to the most known methods in literature like Autoregressive Integrated Moving Average, Seasonal Grey Model, LSTM networks, Temporal Convolutional Networks and two ensemble models. The results of several experiments conducted with 2000 electricity consumers during 14 years from an Algeria province (Bejaia) demonstrate that the proposed method provides remarkable prediction performances. Thus, prediction performances of the K-Shape-based clustering method reach much higher prediction accuracy. According to the MAPE metric, the results of the best predictions are equal to 2.04%. It is also notable that 87% of the clients have a considerably low prediction error.
J. F. Torres and S. Valencia and F. Martínez-Álvarez and N. Hoyos
Predicting Wildfires in the Caribbean Using Multi-source Satellite Data and Deep Learning Conference
IWANN 17th International Work-Conference on Artificial Neural Networks, vol. 14135, Lecture Notes in Computer Science 2023.
Links | BibTeX | Tags: deep learning, natural disasters, time series
@conference{TORRES23_IWANN,
title = {Predicting Wildfires in the Caribbean Using Multi-source Satellite Data and Deep Learning},
author = {J. F. Torres and S. Valencia and F. Martínez-Álvarez and N. Hoyos},
url = {https://link.springer.com/chapter/10.1007/978-3-031-43078-7_1},
doi = {https://doi.org/10.1007/978-3-031-43078-7_1},
year = {2023},
date = {2023-09-30},
booktitle = {IWANN 17th International Work-Conference on Artificial Neural Networks},
volume = {14135},
pages = {3-14},
series = {Lecture Notes in Computer Science},
keywords = {deep learning, natural disasters, time series},
pubstate = {published},
tppubtype = {conference}
}
M. J. Jiménez-Navarro and M. Martínez-Ballesteros and F. Martínez-Álvarez and G. Asencio-Cortés
Embedded Temporal Feature Selection for Time Series Forecasting Using Deep Learning Conference
IWANN 17th International Work-Conference on Artificial Neural Networks, vol. 14135, Lecture Notes in Computer Science 2023.
Links | BibTeX | Tags: deep learning, feature selection, time series
@conference{JIMENEZ-NAVARRO23_IWANN,
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://link.springer.com/chapter/10.1007/978-3-031-43078-7_2},
doi = {https://doi.org/10.1007/978-3-031-43078-7_2},
year = {2023},
date = {2023-09-30},
booktitle = {IWANN 17th International Work-Conference on Artificial Neural Networks},
volume = {14135},
pages = {15-26},
series = {Lecture Notes in Computer Science},
keywords = {deep learning, feature selection, time series},
pubstate = {published},
tppubtype = {conference}
}
P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and F. Martínez-Álvarez and A. Troncoso and Á. Herrero and J. L. Calvo-Rolle and H. Quintián and E. Corchado
Springer, vol. 749, 2023, ISBN: 978-3-031-42529-5.
Links | BibTeX | Tags: big data, clustering, deep learning, IoT
@proceedings{SOCO2023a,
title = {Proceedings of the 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023) Salamanca, Spain, September 5-7, 2023, volume 1},
author = {P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and F. Martínez-Álvarez and A. Troncoso and Á. Herrero and J. L. Calvo-Rolle and H. Quintián and E. Corchado},
editor = {P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and F. Martínez-Álvarez and A. Troncoso and Á. Herrero and J. L. Calvo-Rolle and H. Quintián and E. Corchado},
url = {https://link.springer.com/book/10.1007/978-3-031-42529-5},
doi = {https://doi.org/10.1007/978-3-031-42529-5},
isbn = {978-3-031-42529-5},
year = {2023},
date = {2023-09-05},
volume = {749},
publisher = {Springer},
series = {Lecture Notes in Networks and Systems},
keywords = {big data, clustering, deep learning, IoT},
pubstate = {published},
tppubtype = {proceedings}
}
P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and F. Martínez-Álvarez and A. Troncoso and Á. Herrero and J. L. Calvo-Rolle and H. Quintián and E. Corchado
Springer, vol. 750, 2023, ISBN: 978-3-031-42536-3.
Links | BibTeX | Tags: big data, clustering, deep learning, IoT
@proceedings{SOCO2023b,
title = {Proceedings of the 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023) Salamanca, Spain, September 5-7, 2023, volume 2},
author = {P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and F. Martínez-Álvarez and A. Troncoso and Á. Herrero and J. L. Calvo-Rolle and H. Quintián and E. Corchado},
editor = {P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and F. Martínez-Álvarez and A. Troncoso and Á. Herrero and J. L. Calvo-Rolle and H. Quintián and E. Corchado},
url = {https://link.springer.com/book/10.1007/978-3-031-42536-3},
doi = {10.1007/978-3-030-20055-8},
isbn = {978-3-031-42536-3},
year = {2023},
date = {2023-09-05},
volume = {750},
publisher = {Springer},
series = {Lecture Notes in Networks and Systems},
keywords = {big data, clustering, deep learning, IoT},
pubstate = {published},
tppubtype = {proceedings}
}
P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and F. Martínez-Álvarez and A. Troncoso and Á. Herrero and J. L. Calvo-Rolle and H. Quintián and E. Corchado
Springer, vol. 14001, 2023, ISBN: 978-3-031-40725-3.
Links | BibTeX | Tags: big data, clustering, deep learning, IoT
@proceedings{HAIS2023,
title = {Proceedings of the 18th International Conference on Hybrid Artificial Intelligent Systems (HAIS 2023) Salamanca, Spain, September 5-7, 2023},
author = {P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and F. Martínez-Álvarez and A. Troncoso and Á. Herrero and J. L. Calvo-Rolle and H. Quintián and E. Corchado},
editor = {P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and F. Martínez-Álvarez and A. Troncoso and Á. Herrero and J. L. Calvo-Rolle and H. Quintián and E. Corchado},
url = {https://link.springer.com/book/10.1007/978-3-031-40725-3},
doi = {https://doi.org/10.1007/978-3-031-40725-3},
isbn = {978-3-031-40725-3},
year = {2023},
date = {2023-09-05},
volume = {14001},
publisher = {Springer},
series = {Lecture Notes in Artificial Intelligence},
keywords = {big data, clustering, deep learning, IoT},
pubstate = {published},
tppubtype = {proceedings}
}
P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and F. Martínez-Álvarez and A. Troncoso and Á. Herrero and J. L. Calvo-Rolle and H. Quintián and E. Corchado
Springer, vol. 748, 2023, ISBN: 978-3-031-42519-6.
Links | BibTeX | Tags: big data, clustering
@proceedings{CISIS-ICEUTE2023,
title = {Proceedings of the International Joint Conference 16th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2023) 14th International Conference on EUropean Transnational Education (ICEUTE 2023). Salamanca, Spain, September 5-7, 2023},
author = {P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and F. Martínez-Álvarez and A. Troncoso and Á. Herrero and J. L. Calvo-Rolle and H. Quintián and E. Corchado},
editor = {P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and F. Martínez-Álvarez and A. Troncoso and Á. Herrero and J. L. Calvo-Rolle and H. Quintián and E. Corchado},
url = {https://link.springer.com/book/10.1007/978-3-031-42519-6},
doi = {https://doi.org/10.1007/978-3-031-42519-6},
isbn = {978-3-031-42519-6},
year = {2023},
date = {2023-09-05},
volume = {748},
publisher = {Springer},
series = {Lecture Notes in Networks and Systems},
keywords = {big data, clustering},
pubstate = {published},
tppubtype = {proceedings}
}
A. Vellinger and J. F. Torres and F. Divina and W. Vanhoof
Neuroevolutionary Transfer Learning for Time Series Forecasting Conference
SOCO International Conference on Soft Computing Models in Industrial and Environmental Applications, vol. 749, Lecture Notes in Networks and Systems 2023.
Links | BibTeX | Tags: deep learning, forecasting, time series, transfer learning
@conference{VELLINGER23,
title = {Neuroevolutionary Transfer Learning for Time Series Forecasting},
author = {A. Vellinger and J. F. Torres and F. Divina and W. Vanhoof},
doi = {https://doi.org/10.1007/978-3-031-42529-5_21},
year = {2023},
date = {2023-08-31},
booktitle = {SOCO International Conference on Soft Computing Models in Industrial and Environmental Applications},
volume = {749},
pages = {219-228},
series = {Lecture Notes in Networks and Systems},
keywords = {deep learning, forecasting, time series, transfer learning},
pubstate = {published},
tppubtype = {conference}
}
M. J. Jiménez-Navarro and M. Martínez-Ballesteros and F. Martínez-Álvarez and G. Asencio-Cortés
A New Deep Learning Architecture with Inductive Bias Balance for Oil Temperature Forecasting Journal Article
In: Journal of Big Data, vol. 10, pp. 80, 2023.
Abstract | Links | BibTeX | Tags: deep learning, time series
@article{JIMENEZ-NAVARRO23c,
title = {A New Deep Learning Architecture with Inductive Bias Balance for Oil Temperature Forecasting},
author = {M. J. Jiménez-Navarro and M. Martínez-Ballesteros and F. Martínez-Álvarez and G. Asencio-Cortés},
url = {https://journalofbigdata.springeropen.com/articles/10.1186/s40537-023-00745-0},
doi = {https://doi.org/10.1186/s40537-023-00745-0},
year = {2023},
date = {2023-05-28},
journal = {Journal of Big Data},
volume = {10},
pages = {80},
abstract = {Ensuring the optimal performance of power transformers is a laborious task in which the insulation system plays a vital role in decreasing their deterioration. The insulation system uses insulating oil to control temperature, as high temperatures can reduce the lifetime of the transformers and lead to expensive maintenance. Deep learning architectures have been demonstrated remarkable results in various fields. However, this improvement often comes at the cost of increased computing resources, which, in turn, increases the carbon footprint and hinders the optimization of architectures. In this study, we introduce a novel deep learning architecture that achieves a comparable efficacy to the best existing architectures in transformer oil temperature forecasting while improving efficiency. Effective forecasting can help prevent high temperatures and monitor the future condition of power transformers, thereby reducing unnecessary waste. To balance the inductive bias in our architecture, we propose the Smooth Residual Block, which divides the original problem into multiple subproblems to obtain different representations of the time series, collaboratively achieving the final forecasting. We applied our architecture to the Electricity Transformer datasets, which obtain transformer insulating oil temperature measures from two transformers in China. The results showed a 13% improvement in MSE and a 57% improvement in performance compared to the best current architectures, to the best of our knowledge. Moreover, we analyzed the architecture behavior to gain an intuitive understanding of the achieved solution.},
keywords = {deep learning, time series},
pubstate = {published},
tppubtype = {article}
}
Ensuring the optimal performance of power transformers is a laborious task in which the insulation system plays a vital role in decreasing their deterioration. The insulation system uses insulating oil to control temperature, as high temperatures can reduce the lifetime of the transformers and lead to expensive maintenance. Deep learning architectures have been demonstrated remarkable results in various fields. However, this improvement often comes at the cost of increased computing resources, which, in turn, increases the carbon footprint and hinders the optimization of architectures. In this study, we introduce a novel deep learning architecture that achieves a comparable efficacy to the best existing architectures in transformer oil temperature forecasting while improving efficiency. Effective forecasting can help prevent high temperatures and monitor the future condition of power transformers, thereby reducing unnecessary waste. To balance the inductive bias in our architecture, we propose the Smooth Residual Block, which divides the original problem into multiple subproblems to obtain different representations of the time series, collaboratively achieving the final forecasting. We applied our architecture to the Electricity Transformer datasets, which obtain transformer insulating oil temperature measures from two transformers in China. The results showed a 13% improvement in MSE and a 57% improvement in performance compared to the best current architectures, to the best of our knowledge. Moreover, we analyzed the architecture behavior to gain an intuitive understanding of the achieved solution.
M. J. Jiménez-Navarro and M. Martínez-Ballesteros and F. Martínez-Álvarez and G. Asencio-Cortés
PHILNet: A Novel Efficient Approach for Time Series Forecasting using Deep Learning Journal Article
In: Information Sciences, vol. 632, pp. 815-832, 2023.
Abstract | Links | BibTeX | Tags: deep learning, time series
@article{JIMENEZ-NAVARRO23b,
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://doi.org/10.1016/j.ins.2023.03.021},
doi = {https://www.sciencedirect.com/science/article/pii/S0020025523003183?via%3Dihub},
year = {2023},
date = {2023-03-03},
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 = {deep learning, time series},
pubstate = {published},
tppubtype = {article}
}
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.
O. S. Mazari and A. Sebaa and J. L. Amaro-Mellado and F. Martínez-Álvarez
Creating a homogenized earthquake catalog for Algeria and mapping the main seismic parameters using a geographic information system Journal Article
In: Journal of African Earth Sciences, vol. 201, pp. 104859, 2023.
Abstract | Links | BibTeX | Tags: natural disasters
@article{MAZARI23,
title = {Creating a homogenized earthquake catalog for Algeria and mapping the main seismic parameters using a geographic information system},
author = {O. S. Mazari and A. Sebaa and J. L. Amaro-Mellado and F. Martínez-Álvarez},
url = {https://www.sciencedirect.com/science/article/pii/S1464343X23000687},
doi = {https://doi.org/10.1016/j.jafrearsci.2023.104895},
year = {2023},
date = {2023-03-03},
journal = {Journal of African Earth Sciences},
volume = {201},
pages = {104859},
abstract = {A homogeneous earthquake catalog is an essential instrument to study earthquake occurrence patterns, employing diverse engineering applications. In this paper, we describe a series of compilation and processing steps to compile an updated earthquake catalog for Algeria, a North African country with relatively high seismic activity. The procedure consisted of several steps. First, a range of reliable catalogs were considered; second, the data was integrated and refined; third, magnitudes are homogenized from different kinds of magnitudes into moment magnitude (M_w); declustering is then performed; and, finally, the magnitude-year completeness was estimated. The resulting Algeria catalog is bounded by the geographical limits (19° - 38.5° N and 9.5° W - 12.5° E), and covers the 1960-2020 period. It includes 4021 seismic events, reported up to M_w 7.1. We also calculate a set of seismic parameters, namely M_max and b-value, and mapped them using a geographic information system. Thus, the territory is divided into cells based on different grids to conduct the analysis. The results of the seismic parameters mapping are discussed, highlighting significant details. Several cells presented a M_max between 6.0 and 7.1. Regarding the b-value, two regions (Oran and Constantine) presented a high b-value, implying low-stress areas, and three regions (Algiers, Batna, and Chlef) a low b-value (0.65- 0.85), suggesting high-stress areas. Finally, we suggest some recommendations for future seismic hazard assessment studies.},
keywords = {natural disasters},
pubstate = {published},
tppubtype = {article}
}
A homogeneous earthquake catalog is an essential instrument to study earthquake occurrence patterns, employing diverse engineering applications. In this paper, we describe a series of compilation and processing steps to compile an updated earthquake catalog for Algeria, a North African country with relatively high seismic activity. The procedure consisted of several steps. First, a range of reliable catalogs were considered; second, the data was integrated and refined; third, magnitudes are homogenized from different kinds of magnitudes into moment magnitude (M_w); declustering is then performed; and, finally, the magnitude-year completeness was estimated. The resulting Algeria catalog is bounded by the geographical limits (19° - 38.5° N and 9.5° W - 12.5° E), and covers the 1960-2020 period. It includes 4021 seismic events, reported up to M_w 7.1. We also calculate a set of seismic parameters, namely M_max and b-value, and mapped them using a geographic information system. Thus, the territory is divided into cells based on different grids to conduct the analysis. The results of the seismic parameters mapping are discussed, highlighting significant details. Several cells presented a M_max between 6.0 and 7.1. Regarding the b-value, two regions (Oran and Constantine) presented a high b-value, implying low-stress areas, and three regions (Algiers, Batna, and Chlef) a low b-value (0.65- 0.85), suggesting high-stress areas. Finally, we suggest some recommendations for future seismic hazard assessment studies.
D. Azzouguer and A. Sebaa and D. Hadjout and F. Martínez-Álvarez
IEEE International Conference on Advanced Systems and Emergent Technologies, 2023.
Abstract | Links | BibTeX | Tags: energy, time series
@conference{AZZOUGUER23,
title = {Fraud Detection of Electricity Consumption using Robust Exponential and Holt-Winters Smoothing method},
author = {D. Azzouguer and A. Sebaa and D. Hadjout and F. Martínez-Álvarez},
url = {https://ieeexplore.ieee.org/document/10150645},
doi = {10.1109/IC_ASET58101.2023.10150645},
year = {2023},
date = {2023-02-20},
booktitle = {IEEE International Conference on Advanced Systems and Emergent Technologies},
abstract = {Non-technical losses (NTL), especially fraud detection is very important for electricity distribution enterprises. Fraud detection allows for maximizing the effective economic return for such enterprises. This paper provides an electricity fraud detection approach based on robust exponential and Holt-Winters Smoothing methods. The proposed approach is a procedure that aims to discover the fraudulent behavior of electricity consumers and goes through three crucial steps: (1) the prediction of monthly consumption, (2) the detection of abnormal consumption of electrical meters, and (3) the detection of fraud cases of economic customers. The proposed model was trained and evaluated. Its experimental validation is achieved by using a large dataset of real users from the Algerian economic sector with almost 2000 clients and 14 years of monthly electricity consumption. The proposed solution revealed good performance compared to the literature and the comparison with the models implemented in this article: SARIMA for prediction and two sigma for anomaly detection. The results show highly efficient and realistic countermeasures to fraud detection, which leads us to say that this method is robust and can enhance company profit.},
keywords = {energy, time series},
pubstate = {published},
tppubtype = {conference}
}
Non-technical losses (NTL), especially fraud detection is very important for electricity distribution enterprises. Fraud detection allows for maximizing the effective economic return for such enterprises. This paper provides an electricity fraud detection approach based on robust exponential and Holt-Winters Smoothing methods. The proposed approach is a procedure that aims to discover the fraudulent behavior of electricity consumers and goes through three crucial steps: (1) the prediction of monthly consumption, (2) the detection of abnormal consumption of electrical meters, and (3) the detection of fraud cases of economic customers. The proposed model was trained and evaluated. Its experimental validation is achieved by using a large dataset of real users from the Algerian economic sector with almost 2000 clients and 14 years of monthly electricity consumption. The proposed solution revealed good performance compared to the literature and the comparison with the models implemented in this article: SARIMA for prediction and two sigma for anomaly detection. The results show highly efficient and realistic countermeasures to fraud detection, which leads us to say that this method is robust and can enhance company profit.
E. T. Habtemariam and K. Kekeba and M. Martínez-Ballesteros and F. Mártinez-Álvarez
A Bayesian Optimization-Based LSTM Model for Wind Power Forecasting in the Adama District, Ethiopia Journal Article
In: Energies, vol. 16, pp. 2317, 2023.
Abstract | Links | BibTeX | Tags: deep learning, time series
@article{EJIGU23,
title = {A Bayesian Optimization-Based LSTM Model for Wind Power Forecasting in the Adama District, Ethiopia},
author = {E. T. Habtemariam and K. Kekeba and M. Martínez-Ballesteros and F. Mártinez-Álvarez},
url = {https://www.mdpi.com/1996-1073/16/5/2317},
doi = {https://doi.org/10.3390/en16052317},
year = {2023},
date = {2023-02-19},
journal = {Energies},
volume = {16},
pages = {2317},
abstract = {Renewable energies such as solar and wind power have become promising sources of energy to address the increase in greenhouse gases caused by the use of fossil fuels and to resolve current energy crises. Integrating wind energy into a large-scale electric grid presents a significant challenge due to the high intermittency and nonlinear behavior of wind power. Accurate wind power forecasting is essential for safe and efficient integration into the grid system. Many prediction models have been developed to predict the uncertain and nonlinear time series of wind power, but most neglect the use of Bayesian optimization to optimize the hyperparameters while training deep learning algorithms. The efficiency of grid search strategies decreases as the number of hyperparameters increases, and computation time complexity becomes an issue. This paper presents a robust and optimized Long-Short Term Memory network for forecasting wind power generation in the day ahead in the context of Ethiopia's renewable energy sector. The proposal uses Bayesian optimization to find the best hyperparameter combination in a reasonable computation time. The results indicate that tuning hyperparameters using this metaheuristic prior to building deep learning models significantly improves the predictive performance of the models. The proposed models were evaluated using MAE, RMSE, and MAPE metrics and outperformed both the baseline models and the optimized Gated Recurrent Unit architecture.},
keywords = {deep learning, time series},
pubstate = {published},
tppubtype = {article}
}
Renewable energies such as solar and wind power have become promising sources of energy to address the increase in greenhouse gases caused by the use of fossil fuels and to resolve current energy crises. Integrating wind energy into a large-scale electric grid presents a significant challenge due to the high intermittency and nonlinear behavior of wind power. Accurate wind power forecasting is essential for safe and efficient integration into the grid system. Many prediction models have been developed to predict the uncertain and nonlinear time series of wind power, but most neglect the use of Bayesian optimization to optimize the hyperparameters while training deep learning algorithms. The efficiency of grid search strategies decreases as the number of hyperparameters increases, and computation time complexity becomes an issue. This paper presents a robust and optimized Long-Short Term Memory network for forecasting wind power generation in the day ahead in the context of Ethiopia's renewable energy sector. The proposal uses Bayesian optimization to find the best hyperparameter combination in a reasonable computation time. The results indicate that tuning hyperparameters using this metaheuristic prior to building deep learning models significantly improves the predictive performance of the models. The proposed models were evaluated using MAE, RMSE, and MAPE metrics and outperformed both the baseline models and the optimized Gated Recurrent Unit architecture.
A. M. Fernández and D. Gutiérrez-Avilés and A. Troncoso and F. Martínez-Álvarez
A new Apache Spark-based framework for big data streaming forecasting in IoT networks Journal Article
In: Journal of Supercomputing, vol. 79, pp. 11078–11100, 2023.
Abstract | Links | BibTeX | Tags: big data, IoT
@article{FERNANDEZ23,
title = {A new Apache Spark-based framework for big data streaming forecasting in IoT networks},
author = {A. M. Fernández and D. Gutiérrez-Avilés and A. Troncoso and F. Martínez-Álvarez},
url = {https://link.springer.com/article/10.1007/s11227-023-05100-x},
doi = {https://doi.org/10.1007/s11227-023-05100-x},
year = {2023},
date = {2023-02-02},
journal = {Journal of Supercomputing},
volume = {79},
pages = {11078–11100},
abstract = {Analyzing time-dependent data acquired in a continuous flow is a major challenge for various fields, such as big data and machine learning. Being able to analyze a large volume of data from various sources, such as sensors, networks, and the internet, is essential for improving the efficiency of our society's production processes. Additionally, this vast amount of data is collected dynamically in a continuous stream. The goal of this research is to provide a comprehensive framework for forecasting big data streams from Internet of Things networks and serve as a guide for designing and deploying other third-party solutions. Hence, a new framework for time series forecasting in a big data streaming scenario, using data collected from Internet of Things networks, is presented. This framework comprises of five main modules: Internet of Things network design and deployment, big data streaming architecture, stream data modeling method, big data forecasting method, and a comprehensive real-world application scenario, consisting of a physical Internet of Things network feeding the big data streaming architecture, being the linear regression the algorithm used for illustrative purposes. Comparison with other frameworks reveals that this is the first framework that incorporates and integrates all the aforementioned modules.},
keywords = {big data, IoT},
pubstate = {published},
tppubtype = {article}
}
Analyzing time-dependent data acquired in a continuous flow is a major challenge for various fields, such as big data and machine learning. Being able to analyze a large volume of data from various sources, such as sensors, networks, and the internet, is essential for improving the efficiency of our society's production processes. Additionally, this vast amount of data is collected dynamically in a continuous stream. The goal of this research is to provide a comprehensive framework for forecasting big data streams from Internet of Things networks and serve as a guide for designing and deploying other third-party solutions. Hence, a new framework for time series forecasting in a big data streaming scenario, using data collected from Internet of Things networks, is presented. This framework comprises of five main modules: Internet of Things network design and deployment, big data streaming architecture, stream data modeling method, big data forecasting method, and a comprehensive real-world application scenario, consisting of a physical Internet of Things network feeding the big data streaming architecture, being the linear regression the algorithm used for illustrative purposes. Comparison with other frameworks reveals that this is the first framework that incorporates and integrates all the aforementioned modules.
A. R. Troncoso-García and M. Martínez-Ballesteros and F. Mártinez-Álvarez and A. Troncoso
A new approach based on association rules to add explainability to time series forecasting models Journal Article
In: Information Fusion, vol. 94, pp. 169-180, 2023.
Abstract | Links | BibTeX | Tags: association rules, time series, XAI
@article{TRONCOSO-GARCIA23,
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. Mártinez-Álvarez and A. Troncoso},
url = {https://www.sciencedirect.com/science/article/pii/S1566253523000295},
doi = {10.1016/j.inffus.2023.01.021},
year = {2023},
date = {2023-01-22},
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 = {association rules, time series, XAI},
pubstate = {published},
tppubtype = {article}
}
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.
M. J. Jiménez-Navarro and M. Martínez-Ballesteros and I. S. Brito and F. Martínez-Álvarez and G. Asencio-Cortés
SAC 38th Annual ACM Symposium on Applied Computing, 2023.
Abstract | Links | BibTeX | Tags: deep learning, precision agriculture, time series
@conference{EVAPOCVOA23,
title = {A bioinspired ensemble approach for multi-horizon reference evapotranspiration forecasting in Portugal},
author = {M. J. Jiménez-Navarro and M. Martínez-Ballesteros and I. S. Brito and F. Martínez-Álvarez and G. Asencio-Cortés},
url = {https://dl.acm.org/doi/abs/10.1145/3555776.3578634},
doi = {https://doi.org/10.1145/3555776.3578634},
year = {2023},
date = {2023-01-01},
booktitle = {SAC 38th Annual ACM Symposium on Applied Computing},
pages = {441-448},
abstract = {The year 2022 was the driest year in Portugal since 1931 with 97% of territory in severe drought. Water is especially important for the agricultural sector in Portugal, as it represents 78% total consumption according to the Water Footprint report published in 2010. Reference evapotranspiration is essential due to its importance in optimal irrigation planning that reduces water consumption. This study analyzes and proposes a framework to forecast daily reference evapotranspiration at eight stations in Portugal from 2012 to 2022 without relying on public meteorological forecasts. The data include meteorological data obtained from sensors included in the stations. The goal is to perform a multi-horizon forecasting of reference evapotranspiration using the multiple related covariates. The framework combines the data processing and the analysis of several state-of-the-art forecasting methods including classical, linear, tree-based, artificial neural network and ensembles. Then, an ensemble of all trained models is proposed using a recent bioinspired metaheuristic named Coronavirus Optimization Algorithm to weight the predictions. The results in terms of MAE and MSE are reported, indicating that our approach achieved a MAE of 0.658.},
keywords = {deep learning, precision agriculture, time series},
pubstate = {published},
tppubtype = {conference}
}
The year 2022 was the driest year in Portugal since 1931 with 97% of territory in severe drought. Water is especially important for the agricultural sector in Portugal, as it represents 78% total consumption according to the Water Footprint report published in 2010. Reference evapotranspiration is essential due to its importance in optimal irrigation planning that reduces water consumption. This study analyzes and proposes a framework to forecast daily reference evapotranspiration at eight stations in Portugal from 2012 to 2022 without relying on public meteorological forecasts. The data include meteorological data obtained from sensors included in the stations. The goal is to perform a multi-horizon forecasting of reference evapotranspiration using the multiple related covariates. The framework combines the data processing and the analysis of several state-of-the-art forecasting methods including classical, linear, tree-based, artificial neural network and ensembles. Then, an ensemble of all trained models is proposed using a recent bioinspired metaheuristic named Coronavirus Optimization Algorithm to weight the predictions. The results in terms of MAE and MSE are reported, indicating that our approach achieved a MAE of 0.658.
L. Melgar-García, M. Hosseini and A. Troncoso
Identification of anomalies in urban sound data with Autoencoders Conference
HAIS 18th International Conference on Hybrid Artificial Intelligence Systems, Lecture Notes in Computer Science 2023.
BibTeX | Tags: deep learning, IoT, time series
@conference{HAIS23_Laura,
title = {Identification of anomalies in urban sound data with Autoencoders},
author = {L. Melgar-García, M. Hosseini and A. Troncoso},
year = {2023},
date = {2023-01-01},
booktitle = {HAIS 18th International Conference on Hybrid Artificial Intelligence Systems},
series = {Lecture Notes in Computer Science},
keywords = {deep learning, IoT, time series},
pubstate = {published},
tppubtype = {conference}
}