José F. Torres

Citations and socials

José F. Torres Maldonado received the Degree of Computer Science (2017) and a Master Degree of Computer Science from Pablo de Olavide University, Seville, Spain (2018). He received his Ph.D. in Computer Science at Pablo de Olavide University in March 2022, where he currently is an Assistant Teacher. He also works as Assistant Teacher at Universidad Nacional de Educación a Distancia (UNED). Furthermore, he is currently an invited teacher in two master degrees: “Máster Universitario en Inteligencia Artificial” at the Spanish Association for Artificial Intelligence (AEPIA), and “Mestrado das Internet dais Cosas” at Instituto Politécnico de Beja (Portugal). His research focuses on Big Data, Time Series forecasting, Artificial Intelligence and Deep Learning techniques.

He’s the author of 10 articles in international journals indexed in the JCR (Q1 and Q2) and author of 8 articles in international conference. In addition, he has also been a reviewer in many international conferences, including IEEE International Conference on Data Science and Advanced Analytics (DSAA), Conference on Health Information Science (HIS), International Conference Soft Computing and Pattern Recognition (SoCPaR), or International Conference on Pattern Recognition Applications and Methods (ICPRAM), among others.

His research activity is developed in the Data Science & Big Data Lab, having participated in several public/private projects, highlighting four of them: “Big Data Streaming: Análisis de datos masivos continuos. Modelos predictivos” and “Big Time-Aware Data: Análisis de datos masivos indexados en el tiempo”, both founded by the Spanish Ministry of Economy and Competitiveness, “CONBIDA: Construcción basada de big data: modelos predictivos para la transformación digital de procesos en la construcción”, co-founded by DETEA and “ANAMERLEC: Análisis de datos asociados a la predicción del mercado eléctrico”, co-founded by ISOTROL.

Teaching

Computer Science (Major: Information Systems), Pablo de Olavide University

Programming Fundamentals
Object-oriented Programming
Advanced Programming
Technology Integration (Coordinator)
Fundamentals of Information Systems (former)
Project engineering (former)

Computer Engineering, National University of Distance Education (UNED)

Database Systems
Operating Systems
Design and Administration of Operating Systems
Computer graphics
Databases
Fundamentals of Artificial Intelligence
Criminality and Computer Security (former)

Advanced Bioinformatics Analysis, Pablo de Olavide University

Data Science and Big Data

Artificial Intelligence, Menéndez Pelayo International University

Complex and Temporal Data

Projects

0313_PERSISTAH_5_P. Earthquake resilient schools in the region of Algarve and Huelva.

Principal Investigator: João M. C. Estêvão (Partner Pablo de Olavide University: Francisco Martínez Álvarez).
Funding: European Union (POCTEP).
2016-2020.

TIN2017-88209-C2-1-R. Big Data Streaming: Análisis de datos masivos continuos. Modelos predictivos.

Principal Investigator: Alicia Troncoso Lora, Francisco Martínez Álvarez
2018-2020

TIN2014-55894-C2-2-R. Big Time-Aware Data: Análisis de datos masivos indexados en el tiempo.

Principal Investigator: Alicia Troncoso Lora
2015-2018

Net LIoT: Diseño e implementación de red y plataforma Smart Lantia IoT

Principal Investigator: Francisco Martínez Álvarez
Industrial partner: Lantia IOT
2018-2020

CONBIDA. Modelos predictivos para la gestión colaborativa de estimaciones en procesos de relación con el cliente

Principal Investigator: Alicia Troncoso Lora
Industrial partner: Detea
2018-2019

easyM2M. Nuevos protocolos de comunicación para la creación de Smart Cities

Principal Investigator: Francisco Martínez Álvarez
Industrial partner: Lantia IOT
2017-2019

ANAMERLEC. Análisis de datos asociados a la predicción del mercado eléctrico

Principal Investigator: Alicia Troncoso Lora
Industrial partner: Isotrol
2017-2018

ITC-20161178. AQUASIG: Sistema inteligente de gestión del abastecimiento y consumo urbano de agua

Principal Investigator: Francisco Martínez Álvarez
Industrial partner: Geographica
2016-2018

ITC-20151078. Optimización de la conservación de la infraestructura ferroviaria para transporte urbano

Principal Investigator: Francisco Martínez Álvarez
Industrial partner: ec2ce
2015-2017

R&D Activities

Publications

20 entries « ‹ 1 of 2 › »

2023

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

Expert Systems with Applications, 227 , pp. 120123, 2023.

Abstract | Links | BibTeX

@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 = {},
pubstate = {published},
tppubtype = {article}
}

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, 14135 , Lecture Notes in Computer Science 2023.

Links | BibTeX

2022

D. Hadjout and J. F. Torres and A. Troncoso and A. Sebaa and F. Martínez-Álvarez

Electricity consumption forecasting based on ensemble deep learning with application to the Algerian market Journal Article

Energy, 243 , pp. 123060, 2022.

Abstract | Links | BibTeX

J. F. Torres and F. Martínez-Álvarez and A. Troncoso

A deep LSTM network for the Spanish electricity consumption forecasting Journal Article

Neural Computing and Applications, 34 , pp. 10533-10545, 2022.

Abstract | Links | BibTeX

@article{TORRES22b,
title = {A deep LSTM network for the Spanish electricity consumption forecasting},
author = {J. F. Torres and F. Martínez-Álvarez and A. Troncoso},
url = {https://link.springer.com/article/10.1007/s00521-021-06773-2},
doi = {https://doi.org/10.1007/s00521-021-06773-2},
year = {2022},
date = {2022-02-05},
journal = {Neural Computing and Applications},
volume = {34},
pages = {10533-10545},
abstract = {Nowadays, electricity is a basic commodity necessary for the well-being of any modern society. Due to the growth in electricity consumption in recent years, mainly in large cities, electricity forecasting is key to the management of an efficient, sustainable and safe smart grid for the consumer. In this work, a deep neural network is proposed to address the electricity consumption forecasting in the short-term, namely, a long short-term memory (LSTM) network due to its ability to deal with sequential data such as time-series data. First, the optimal values for certain hyper-parameters have been obtained by a random search and a metaheuristic, called coronavirus optimization algorithm (CVOA), based on the propagation of the SARS-Cov-2 virus. Then, the optimal LSTM has been applied to predict the electricity demand with 4-h forecast horizon. Results using Spanish electricity data during nine years and half measured with 10-min frequency are presented and discussed. Finally, the performance of the proposed LSTM using random search and the LSTM using CVOA is compared, on the one hand, with that of recently published deep neural networks (such as a deep feed-forward neural network optimized with a grid search) and temporal fusion transformers optimized with a sampling algorithm, and, on the other hand, with traditional machine learning techniques, such as a linear regression, decision trees and tree-based ensemble techniques (gradient-boosted trees and random forest), achieving the smallest prediction error below 1.5%.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

2021

K.-T. T. Bui and J. F. Torres and D. Gutiérrez-Avilés and V. H. Nhu and F. Martínez-Álvarez and D. T. Bui

Deformation forecasting of a hydropower dam by hybridizing a Long Short-Term Memory deep learning network with the Coronavirus Optimization Algorithm Journal Article

Computer-Aided Civil and Infrastructure Engineering, 37 , pp. 1368-1386, 2021.

Abstract | Links | BibTeX

@article{BUI22b,
title = {Deformation forecasting of a hydropower dam by hybridizing a Long Short-Term Memory deep learning network with the Coronavirus Optimization Algorithm},
author = {K.-T. T. Bui and J. F. Torres and D. Gutiérrez-Avilés and V. H. Nhu and F. Martínez-Álvarez and D. T. Bui},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/mice.12810},
doi = {https://doi.org/10.1111/mice.12810},
year = {2021},
date = {2021-11-24},
journal = {Computer-Aided Civil and Infrastructure Engineering},
volume = {37},
pages = {1368-1386},
abstract = {The safety operation and management of hydropower dam play a critical role in social-economic development and ensure people's safety in many countries; therefore, modeling and forecasting the hydropower dam's deformations with high accuracy is crucial. This research aims to propose and validate a new model based on deep learning long short-term memory (LSTM) and the coronavirus optimization algorithm (CVOA), named CVOA-LSTM, for forecasting the deformations of the hydropower dam. The second-largest hydropower dam of Vietnam, located in the Hoa Binh province, is focused. Herein, we used the LSTM to establish the deformation model, whereas the CVOA was utilized to optimize the three parameters of the LSTM, the number of hidden layers, the learning rate, and the dropout. The efficacy of the proposed CVOA-LSTM model is assessed by comparing its forecasting performance with state-of-the-art benchmarks, sequential minimal optimization for support vector regression, Gaussian process, M5' model tree, multilayer perceptron neural network, reduced error pruning tree, random tree, random forest, and radial basis function neural network. The result shows that the proposed CVOA-LSTM model has high forecasting capability (R2 = 0.874, root mean square error = 0.34, mean absolute error = 0.23) and outperforms the benchmarks. We conclude that CVOA-LSTM is a new tool that can be considered to forecast the hydropower dam's deformations.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

A. Melara and J. F. Torres and A. Troncoso and F. Martínez-Álvarez

Electricity Generation Forecasting in Concentrating Solar-Thermal Power Plants with Ensemble Learning Conference

SOCO International Conference on Soft Computing Models in Industrial and Environmental Applications, 1401 , Advances in Intelligent Systems and Computing 2021.

Links | BibTeX

D. Hadjout and J. F. Torres and A. Sebaa and F. Martínez-Álvarez

Medium-Term Electricity Consumption Forecasting in Algeria Based on Clustering, Deep Learning and Bayesian Optimization Methods Conference

SOCO International Conference on Soft Computing Models in Industrial and Environmental Applications, 1401 , Advances in Intelligent Systems and Computing 2021.

Links | BibTeX

J. F. Torres and M. J. Jiménez-Navarro and F. Martínez-Álvarez and A. Troncoso

Electricity consumption time series forecasting using Temporal Convolutional Networks Conference

CAEPIA Conference of the Spanish Association for Artificial Intelligence , Lecture Notes in Artificial Intelligence 2021.

BibTeX

J. F. Torres and D. Hadjout and A. Sebaa and F. Martínez-Álvarez and A. Troncoso

Deep Learning for Time Series Forecasting: A Survey Journal Article

Big Data, 9 (1), pp. 3-21, 2021.

Abstract | Links | BibTeX

2020

F. Divina and J. F. Torres and M. García-Torres and F. Martínez-Álvarez and A. Troncoso

Hybridizing deep learning and neuroevolution: Application to the Spanish short-term electric energy consumption forecasting Journal Article

Applied Sciences, 10 (16), pp. 5487, 2020.

Abstract | Links | BibTeX

F. Martínez-Álvarez and G. Asencio-Cortés and J. F. Torres and D. Gutiérrez-Avilés and L. Melgar-García and R. Pérez-Chacón and C. Rubio-Escudero and A. Troncoso and J. C. Riquelme

Coronavirus Optimization Algorithm: A bioinspired metaheuristic based on the COVID-19 propagation model Journal Article

Big Data, 8 (4), pp. 308-322, 2020.

Abstract | Links | BibTeX

@article{MARTINEZ-ALVAREZ20,
title = {Coronavirus Optimization Algorithm: A bioinspired metaheuristic based on the COVID-19 propagation model},
author = {F. Martínez-Álvarez and G. Asencio-Cortés and J. F. Torres and D. Gutiérrez-Avilés and L. Melgar-García and R. Pérez-Chacón and C. Rubio-Escudero and A. Troncoso and J. C. Riquelme},
url = {https://www.liebertpub.com/doi/full/10.1089/big.2020.0051},
doi = {10.1089/big.2020.0051},
year = {2020},
date = {2020-07-22},
journal = {Big Data},
volume = {8},
number = {4},
pages = {308-322},
abstract = {This work proposes a novel bioinspired metaheuristic, simulating how the coronavirus spreads and infects healthy people. From a primary infected individual (patient zero), the coronavirus rapidly infects new victims, creating large populations of infected people who will either die or spread infection. Relevant terms such as reinfection probability, super-spreading rate, social distancing measures or traveling rate are introduced into the model in order to simulate the coronavirus activity as accurately as possible. The infected population initially grows exponentially over time, but taking into consideration social isolation measures, the mortality rate and number of recoveries, the infected population gradually decreases. The Coronavirus Optimization Algorithm has two major advantages when compared to other similar strategies. Firstly, the input parameters are already set according to the disease statistics, preventing researchers from initializing them with arbitrary values. Secondly, the approach has the ability to end after several iterations, without setting this value either. Furthermore, a parallel multi-virus version is proposed, where several coronavirus strains evolve over time and explore wider search space areas in less iterations. Finally, the metaheuristic has been combined with deep learning models, in order to find optimal hyperparameters during the training phase. As application case, the problem of electricity load time series forecasting has been addressed, showing quite remarkable performance.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

2019

J. F. Torres and D. Gutiérrez-Avilés and A. Troncoso and F. Martínez-Álvarez

Random Hyper-Parameter Search-Based Deep Neural Network for Power Consumption Forecasting Conference

IWANN 15th International Work-Conference on Artificial Neural Networks, 11506 , Lecture Notes in Computer Science 2019.

Links | BibTeX

J. F. Torres and A. Troncoso and I. Koprinska and Z. Wang and F. Martínez-Álvarez

Big data solar power forecasting based on deep learning and multiple data sources Journal Article

Expert Systems, 36 , pp. id12394, 2019.

Links | BibTeX

2018

A. Galicia and J. F. Torres and F. Martínez-Álvarez and A. Troncoso

A novel Spark-based multi-step forecasting algorithm for big data time series Journal Article

Information Sciences, 467 , pp. 800-818, 2018.

Abstract | Links | BibTeX

J. F. Torres and A. Galicia and A. Troncoso and F. Martínez-Álvarez

A scalable approach based on deep learning for big data time series forecasting Journal Article

Integrated Computer-Aided Engineering, 25 (4), pp. 335-348, 2018.

Abstract | Links | BibTeX

20 entries « ‹ 1 of 2 › »

José F. Torres

Assistant Professor

Profile details

Citations and socials

Teaching

Computer Science (Major: Information Systems), Pablo de Olavide University

Computer Engineering, National University of Distance Education (UNED)

Advanced Bioinformatics Analysis, Pablo de Olavide University

Artificial Intelligence, Menéndez Pelayo International University

Projects

0313_PERSISTAH_5_P. Earthquake resilient schools in the region of Algarve and Huelva.

TIN2017-88209-C2-1-R. Big Data Streaming: Análisis de datos masivos continuos. Modelos predictivos.

TIN2014-55894-C2-2-R. Big Time-Aware Data: Análisis de datos masivos indexados en el tiempo.

Net LIoT: Diseño e implementación de red y plataforma Smart Lantia IoT

CONBIDA. Modelos predictivos para la gestión colaborativa de estimaciones en procesos de relación con el cliente

easyM2M. Nuevos protocolos de comunicación para la creación de Smart Cities

ANAMERLEC. Análisis de datos asociados a la predicción del mercado eléctrico

ITC-20161178. AQUASIG: Sistema inteligente de gestión del abastecimiento y consumo urbano de agua

ITC-20151078. Optimización de la conservación de la infraestructura ferroviaria para transporte urbano

R&D Activities

Publications

2023

2022

2021

2020

2019

2018