UNIVERSIDAD PABLO DE OLAVIDE
Publications
association rules astrostatistics big data bioinformatics clustering data streaming deep learning education energy feature selection IoT natural disasters pattern recognition time series transfer learning
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 | Tags: deep learning, energy, time series) @article{HADJOUT22, title = {Electricity consumption forecasting based on ensemble deep learning with application to the Algerian market}, author = {D. Hadjout and J. F. Torres and A. Troncoso and A. Sebaa and F. Martínez-Álvarez}, url = {https://www.sciencedirect.com/science/article/pii/S0360544221033090}, doi = {https://doi.org/10.1016/j.energy.2021.123060}, year = {2022}, date = {2022-03-15}, journal = {Energy}, volume = {243}, pages = {123060}, abstract = {The economic sector is one of the most important pillars of countries. Economic activities of industry are intimately linked with the ability to meet their needs for electricity. Therefore, electricity forecasting is a very important task. It allows for better planning and management of energy resources. Several methods have been proposed to forecast energy consumption. In this work, to predict monthly electricity consumption for the economic sector, we develop a novel approach based on ensemble learning. Our approach combines three models that proved successful in the field, namely: Long Short Term Memory and Gated Recurrent Unit neural networks, and Temporal Convolutional Networks. The experiments have been conducted with almost 2000 clients and 14 years of monthly electricity consumption from Bejaia, Algeria. The results show that the proposed ensemble models achieve better performance than both the company's requirements and the prediction of the traditional individual models. Finally, statistical tests have been carried out to prove that significance of the ensemble models developed.}, keywords = {deep learning, energy, time series}, pubstate = {published}, tppubtype = {article} } The economic sector is one of the most important pillars of countries. Economic activities of industry are intimately linked with the ability to meet their needs for electricity. Therefore, electricity forecasting is a very important task. It allows for better planning and management of energy resources. Several methods have been proposed to forecast energy consumption. In this work, to predict monthly electricity consumption for the economic sector, we develop a novel approach based on ensemble learning. Our approach combines three models that proved successful in the field, namely: Long Short Term Memory and Gated Recurrent Unit neural networks, and Temporal Convolutional Networks. The experiments have been conducted with almost 2000 clients and 14 years of monthly electricity consumption from Bejaia, Algeria. The results show that the proposed ensemble models achieve better performance than both the company's requirements and the prediction of the traditional individual models. Finally, statistical tests have been carried out to prove that significance of the ensemble models developed. |
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, 2022. (Abstract | Links | BibTeX | Tags: deep learning, energy) @article{TORRES22, 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}, 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 = {deep learning, energy}, pubstate = {published}, tppubtype = {article} } 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%. |
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, in press , 2021. (BibTeX | Tags: deep learning, time series) @article{BUI22, 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}, year = {2021}, date = {2021-11-24}, journal = {Computer-Aided Civil and Infrastructure Engineering}, volume = {in press}, keywords = {deep learning, time series}, pubstate = {published}, tppubtype = {article} } |
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) Conference of the Spanish Association for Artificial Intelligence (CAEPIA'21), Lecture Notes in Artificial Intelligence 2021. (BibTeX | Tags: deep learning, time series) @conference{TORRES21b, title = {Electricity consumption time series forecasting using Temporal Convolutional Networks}, author = {J. F. Torres and M. J. Jiménez-Navarro and F. Martínez-Álvarez and A. Troncoso}, year = {2021}, date = {2021-09-01}, booktitle = {Conference of the Spanish Association for Artificial Intelligence (CAEPIA'21)}, series = {Lecture Notes in Artificial Intelligence}, keywords = {deep learning, time series}, pubstate = {published}, tppubtype = {conference} } |
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 | Tags: deep learning, energy, time series) @conference{MELARA21, title = {Electricity Generation Forecasting in Concentrating Solar-Thermal Power Plants with Ensemble Learning}, author = {A. Melara and J. F. Torres and A. Troncoso and F. Martínez-Álvarez}, doi = {https://doi.org/10.1007/978-3-030-87869-6_63}, year = {2021}, date = {2021-09-01}, booktitle = {SOCO International Conference on Soft Computing Models in Industrial and Environmental Applications}, volume = {1401}, pages = {665-674}, series = {Advances in Intelligent Systems and Computing}, keywords = {deep learning, energy, time series}, pubstate = {published}, tppubtype = {conference} } |
D. Hadjout and J. F. Torres and A. Sebaa and F. Martínez-Álvarez SOCO International Conference on Soft Computing Models in Industrial and Environmental Applications, 1401 , Advances in Intelligent Systems and Computing 2021. (Links | BibTeX | Tags: deep learning, energy, time series) @conference{HADJOUT21, title = {Medium-Term Electricity Consumption Forecasting in Algeria Based on Clustering, Deep Learning and Bayesian Optimization Methods}, author = {D. Hadjout and J. F. Torres and A. Sebaa and F. Martínez-Álvarez}, doi = {https://doi.org/10.1007/978-3-030-87869-6_70}, year = {2021}, date = {2021-09-01}, booktitle = {SOCO International Conference on Soft Computing Models in Industrial and Environmental Applications}, volume = {1401}, pages = {739-748}, series = {Advances in Intelligent Systems and Computing}, keywords = {deep learning, energy, time series}, pubstate = {published}, tppubtype = {conference} } |
M. J. Jiménez-Navarro and F. Martínez-Álvarez and A. Troncoso and G. Asencio-Cortés HLNet: A Novel Hierarchical Deep Neural Network for Time Series Forecasting (Conference) SOCO International Conference on Soft Computing Models in Industrial and Environmental Applications, 1401 , Advances in Intelligent Systems and Computing 2021. (Links | BibTeX | Tags: deep learning, time series) @conference{JIMENEZ-NAVARRO21, title = {HLNet: A Novel Hierarchical Deep Neural Network for Time Series Forecasting}, author = {M. J. Jiménez-Navarro and F. Martínez-Álvarez and A. Troncoso and G. Asencio-Cortés}, doi = {https://doi.org/10.1007/978-3-030-87869-6_68}, year = {2021}, date = {2021-09-01}, booktitle = {SOCO International Conference on Soft Computing Models in Industrial and Environmental Applications}, volume = {1401}, pages = {717-727}, series = {Advances in Intelligent Systems and Computing}, keywords = {deep learning, time series}, pubstate = {published}, tppubtype = {conference} } |
M. A. Molina and M. J. Jiménez-Navarro and F. Martínez-Álvarez and G. Asencio-Cortés A Model-Based Deep Transfer Learning Algorithm for Phenology Forecasting Using Satellite Imagery (Conference) HAIS, 12886 , Lecture Notes in Computer Science 2021. (Links | BibTeX | Tags: deep learning, time series) @conference{MOLINA21, title = {A Model-Based Deep Transfer Learning Algorithm for Phenology Forecasting Using Satellite Imagery}, author = {M. A. Molina and M. J. Jiménez-Navarro and F. Martínez-Álvarez and G. Asencio-Cortés}, url = {https://link.springer.com/chapter/10.1007/978-3-030-86271-8_43}, doi = {https://doi.org/10.1007/978-3-030-86271-8_43}, year = {2021}, date = {2021-09-01}, booktitle = {HAIS}, volume = {12886}, pages = {511-523}, series = {Lecture Notes in Computer Science}, keywords = {deep learning, time series}, pubstate = {published}, tppubtype = {conference} } |
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 | Tags: big data, deep learning, time series) @article{TORRES21, title = {Deep Learning for Time Series Forecasting: A Survey}, author = {J. F. Torres and D. Hadjout and A. Sebaa and F. Martínez-Álvarez and A. Troncoso}, url = {https://www.liebertpub.com/doi/10.1089/big.2020.0159}, doi = {10.1089/big.2020.0159}, year = {2021}, date = {2021-02-05}, journal = {Big Data}, volume = {9}, number = {1}, pages = {3-21}, abstract = {Deep learning, one of the most remarkable techniques of machine learning, has been a major success in many fields, including image processing, speech recognition, and text understanding. It is powerful engines capable of learning arbitrary mapping functions, not require a scaled or stationary time series as input, support multivariate inputs, and support multi-step outputs. All of these features together make deep learning useful tools when dealing with more complex time series prediction problems involving large amounts of data, and multiple variables with complex relationships. This paper provides an overview of the most common Deep Learning types for time series forecasting, Explain the relationships between deep learning models and classical approaches to time series forecasting. A brief background of the particular challenges presents in time-series data and the most common deep learning techniques that are often used for time series forecasting is provided. Previous studies that applied deep learning to time series are reviewed.}, keywords = {big data, deep learning, time series}, pubstate = {published}, tppubtype = {article} } Deep learning, one of the most remarkable techniques of machine learning, has been a major success in many fields, including image processing, speech recognition, and text understanding. It is powerful engines capable of learning arbitrary mapping functions, not require a scaled or stationary time series as input, support multivariate inputs, and support multi-step outputs. All of these features together make deep learning useful tools when dealing with more complex time series prediction problems involving large amounts of data, and multiple variables with complex relationships. This paper provides an overview of the most common Deep Learning types for time series forecasting, Explain the relationships between deep learning models and classical approaches to time series forecasting. A brief background of the particular challenges presents in time-series data and the most common deep learning techniques that are often used for time series forecasting is provided. Previous studies that applied deep learning to time series are reviewed. |
M. A. Molina and G. Asencio-Cortés and J. C. Riquelme and F. Martínez-Álvarez A Preliminary Study on Deep Transfer Learning Applied to Image Classification for Small Datasets (Conference) 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020), Advances in Intelligent Systems and Computing 2021. (Links | BibTeX | Tags: deep learning, pattern recognition, transfer learning) @conference{molina2021, title = {A Preliminary Study on Deep Transfer Learning Applied to Image Classification for Small Datasets}, author = {M. A. Molina and G. Asencio-Cortés and J. C. Riquelme and F. Martínez-Álvarez}, url = {https://link.springer.com/chapter/10.1007/978-3-030-57802-2_71}, year = {2021}, date = {2021-01-01}, booktitle = {15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020)}, series = {Advances in Intelligent Systems and Computing}, keywords = {deep learning, pattern recognition, transfer learning}, pubstate = {published}, tppubtype = {conference} } |
R. Parra and V. Ojeda and J.L. Vázquez Noguera and M. García-Torres and J.C. Mello-Román and C. Villalba and J. Facon and F. Divina and O. Cardozo and V. Castillo A Trust-Based Methodology to Evaluate Deep Learning Models for Automatic Diagnosis of Ocular Toxoplasmosis from Fundus Images (Journal Article) Diagnostics, 11 (11), pp. 1951, 2021. (Links | BibTeX | Tags: bioinformatics, deep learning, pattern recognition) @article{parra2021trust, title = {A Trust-Based Methodology to Evaluate Deep Learning Models for Automatic Diagnosis of Ocular Toxoplasmosis from Fundus Images}, author = {R. Parra and V. Ojeda and J.L. Vázquez Noguera and M. García-Torres and J.C. Mello-Román and C. Villalba and J. Facon and F. Divina and O. Cardozo and V. Castillo}, doi = {10.3390/diagnostics11111951}, year = {2021}, date = {2021-01-01}, journal = {Diagnostics}, volume = {11}, number = {11}, pages = {1951}, publisher = {Multidisciplinary Digital Publishing Institute pubstate = published}, keywords = {bioinformatics, deep learning, pattern recognition}, pubstate = {published}, tppubtype = {article} } |
J. Ayala and M. García-Torres and J.L. Vázquez Noguera and F. Gómez-Vela and F. Divina Technical analysis strategy optimization using a machine learning approach in stock market indices (Journal Article) Knowledge-Based Systems, pp. 107119, 2021. (Links | BibTeX | Tags: deep learning, pattern recognition) @article{ayala2021technical, title = {Technical analysis strategy optimization using a machine learning approach in stock market indices}, author = {J. Ayala and M. García-Torres and J.L. Vázquez Noguera and F. Gómez-Vela and F. Divina}, doi = {10.1016/j.knosys.2021.107119 volume=225}, year = {2021}, date = {2021-01-01}, journal = {Knowledge-Based Systems}, pages = {107119}, publisher = {Elsevier pubstate = published}, keywords = {deep learning, pattern recognition}, pubstate = {published}, tppubtype = {article} } |
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 | Tags: big data, deep learning, energy, time series) @article{DIVINA2020, title = {Hybridizing deep learning and neuroevolution: Application to the Spanish short-term electric energy consumption forecasting}, author = {F. Divina and J. F. Torres and M. García-Torres and F. Martínez-Álvarez and A. Troncoso}, url = {https://www.mdpi.com/2076-3417/10/16/5487}, doi = {https://doi.org/10.3390/app10165487}, year = {2020}, date = {2020-07-30}, journal = {Applied Sciences}, volume = {10}, number = {16}, pages = {5487}, abstract = {The electric energy production would be much more efficient if accurate estimations of the future demand were available, since these would allow allocating only the resources needed for the production of the right amount of energy required. With this motivation in mind, we propose a strategy, based on neuroevolution, that can be used to this aim. Our proposal uses a genetic algorithm in order to find a sub-optimal set of hyper-parameters for configuring a deep neural network, which can then be used for obtaining the forecasting. Such a strategy is justified by the observation that the performances achieved by deep neural networks are strongly dependent on the right setting of the hyper-parameters, and genetic algorithms have shown excellent search capabilities in huge search spaces. Moreover, we base our proposal on a distributed computing platform, which allows its use on a large time-series. In order to assess the performances of our approach, we have applied it to a large dataset, related to the electric energy consumption registered in Spain over almost 10 years. Experimental results confirm the validity of our proposal since it outperforms all other forecasting techniques to which it has been compared.}, keywords = {big data, deep learning, energy, time series}, pubstate = {published}, tppubtype = {article} } The electric energy production would be much more efficient if accurate estimations of the future demand were available, since these would allow allocating only the resources needed for the production of the right amount of energy required. With this motivation in mind, we propose a strategy, based on neuroevolution, that can be used to this aim. Our proposal uses a genetic algorithm in order to find a sub-optimal set of hyper-parameters for configuring a deep neural network, which can then be used for obtaining the forecasting. Such a strategy is justified by the observation that the performances achieved by deep neural networks are strongly dependent on the right setting of the hyper-parameters, and genetic algorithms have shown excellent search capabilities in huge search spaces. Moreover, we base our proposal on a distributed computing platform, which allows its use on a large time-series. In order to assess the performances of our approach, we have applied it to a large dataset, related to the electric energy consumption registered in Spain over almost 10 years. Experimental results confirm the validity of our proposal since it outperforms all other forecasting techniques to which it has been compared. |
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 | Tags: big data, deep learning, energy, time series) @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 = {big data, deep learning, energy, time series}, pubstate = {published}, tppubtype = {article} } 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. |
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 | Tags: deep learning, energy, time series) @conference{TORRES19-2, title = {Random Hyper-Parameter Search-Based Deep Neural Network for Power Consumption Forecasting}, author = {J. F. Torres and D. Gutiérrez-Avilés and A. Troncoso and F. Martínez-Álvarez}, url = {https://link.springer.com/chapter/10.1007/978-3-030-20521-8_22}, doi = {https://doi.org/10.1007/978-3-030-20521-8_22}, year = {2019}, date = {2019-05-16}, booktitle = {IWANN 15th International Work-Conference on Artificial Neural Networks}, volume = {11506}, pages = {259-269}, series = {Lecture Notes in Computer Science}, keywords = {deep learning, energy, time series}, pubstate = {published}, tppubtype = {conference} } |
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 | Tags: deep learning, energy, time series) @article{TORRES19-1, title = {Big data solar power forecasting based on deep learning and multiple data sources}, author = {J. F. Torres and A. Troncoso and I. Koprinska and Z. Wang and F. Martínez-Álvarez}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.12394}, doi = {https://doi.org/10.1111/exsy.12394}, year = {2019}, date = {2019-03-01}, journal = {Expert Systems}, volume = {36}, pages = {id12394}, keywords = {deep learning, energy, time series}, pubstate = {published}, tppubtype = {article} } |
2018 |
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 | Tags: deep learning, energy, time series) @article{ICAE2018, title = {A scalable approach based on deep learning for big data time series forecasting}, author = {J. F. Torres and A. Galicia and A. Troncoso and F. Martínez-Álvarez}, url = {https://content.iospress.com/articles/integrated-computer-aided-engineering/ica580}, doi = {10.3233/ICA-180580}, year = {2018}, date = {2018-01-01}, journal = {Integrated Computer-Aided Engineering}, volume = {25}, number = {4}, pages = {335-348}, abstract = {This paper presents a method based on deep learning to deal with big data times series forecasting. The deep feed forward neural network provided by the H2O big data analysis framework has been used along with the Apache Spark platform for distributed computing. Since H2O does not allow the conduction of multi-step regression, a general-purpose methodology that can be used for prediction horizons with arbitrary length is proposed here, being the prediction horizon, h, the number of future values to be predicted. The solution consists in splitting the problem into h forecasting subproblems, being h the number of samples to be simultaneously predicted. Thus, the best prediction model for each subproblem can be obtained, making easier its parallelization and adaptation to the big data context. Moreover, a grid search is carried out to obtain the optimal hyperparameters of the deep learning-based approach. Results from a real-world dataset composed of electricity consumption in Spain, with a ten-minute frequency sampling rate, from 2007 to 2016 are reported. In particular, the accuracy and runtimes versus computing resources and size of the dataset are analyzed. Finally, the performance and the scalability of the proposed method is compared to other recently published techniques, showing to be a suitable method to process big data time series.}, keywords = {deep learning, energy, time series}, pubstate = {published}, tppubtype = {article} } This paper presents a method based on deep learning to deal with big data times series forecasting. The deep feed forward neural network provided by the H2O big data analysis framework has been used along with the Apache Spark platform for distributed computing. Since H2O does not allow the conduction of multi-step regression, a general-purpose methodology that can be used for prediction horizons with arbitrary length is proposed here, being the prediction horizon, h, the number of future values to be predicted. The solution consists in splitting the problem into h forecasting subproblems, being h the number of samples to be simultaneously predicted. Thus, the best prediction model for each subproblem can be obtained, making easier its parallelization and adaptation to the big data context. Moreover, a grid search is carried out to obtain the optimal hyperparameters of the deep learning-based approach. Results from a real-world dataset composed of electricity consumption in Spain, with a ten-minute frequency sampling rate, from 2007 to 2016 are reported. In particular, the accuracy and runtimes versus computing resources and size of the dataset are analyzed. Finally, the performance and the scalability of the proposed method is compared to other recently published techniques, showing to be a suitable method to process big data time series. |
J. F. Torres and A. Troncoso and I. Koprinska and Z. Wang and F. Martínez-Álvarez Deep learning for big data time series forecasting applied to solar power (Conference) SOCO 13th International Conference on Soft Computing Models in Industrial and Environmental Applications, Advances in Intelligent Systems and Computing 2018. (Links | BibTeX | Tags: deep learning, energy, time series) @conference{SOCO2018, title = {Deep learning for big data time series forecasting applied to solar power}, author = {J. F. Torres and A. Troncoso and I. Koprinska and Z. Wang and F. Martínez-Álvarez}, url = {https://link.springer.com/chapter/10.1007/978-3-319-94120-2_12}, year = {2018}, date = {2018-01-01}, booktitle = {SOCO 13th International Conference on Soft Computing Models in Industrial and Environmental Applications}, series = {Advances in Intelligent Systems and Computing}, keywords = {deep learning, energy, time series}, pubstate = {published}, tppubtype = {conference} } |
2017 |
J. F. Torres and A. Troncoso and F. Martínez-Álvarez Deep Learning - Based Approach for Time Series Forecasting with Application to Electricity Load (Conference) IWINAC International Work-Conference on the Interplay Between Natural and Artificial Computation, Lecture Notes in computer Science 2017. (Links | BibTeX | Tags: deep learning, energy, time series) @conference{IWINAC2017, title = {Deep Learning - Based Approach for Time Series Forecasting with Application to Electricity Load}, author = {J. F. Torres and A. Troncoso and F. Martínez-Álvarez}, url = {https://link.springer.com/chapter/10.1007/978-3-319-59773-7_21}, year = {2017}, date = {2017-01-01}, booktitle = {IWINAC International Work-Conference on the Interplay Between Natural and Artificial Computation}, series = {Lecture Notes in computer Science}, keywords = {deep learning, energy, time series}, pubstate = {published}, tppubtype = {conference} } |