Publications
2024 |
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) Applied Water Science, 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. |
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) Computers and Electronics in Agriculture, 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) Expert Systems with Applications, 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, 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} } |
A. R. Troncoso-García and M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso Deep Learning-Based Approach for Sleep Apnea Detection Using Physiological Signals (Conference) IWANN 17th International Work-Conference on Artificial Neural Networks, 14134 , Lecture Notes in Computer Science 2023. (Links | BibTeX | Tags: deep learning, forecasting, time series) @conference{TRONCOSO-GARCIA23_IWANN, title = {Deep Learning-Based Approach for Sleep Apnea Detection Using Physiological Signals}, author = {A. R. Troncoso-García and M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso}, 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 = {2023}, date = {2023-09-30}, booktitle = {IWANN 17th International Work-Conference on Artificial Neural Networks}, volume = {14134}, pages = {626–637}, series = {Lecture Notes in Computer Science}, keywords = {deep learning, forecasting, 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, 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, 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, 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, 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} } |
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, 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) Journal of Big Data, 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) Information Sciences, 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. |
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) Energies, 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. |
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) Logic Journal of the IGPL, in press , 2023. (Abstract | 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}, year = {2023}, date = {2023-01-20}, journal = {Logic Journal of the IGPL}, volume = {in press}, 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). |
A. R. Troncoso-García and m. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso Evolutionary computation to explain deep learning models for time series forecasting (Conference) SAC 38th Annual ACM Symposium on Applied Computing, 2023. (Links | BibTeX | Tags: deep learning, time series, XAI) @conference{SAC2023, title = {Evolutionary computation to explain deep learning models for time series forecasting}, author = {A. R. Troncoso-García and m. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso}, url = {https://dl.acm.org/doi/abs/10.1145/3555776.3578994}, year = {2023}, date = {2023-01-01}, booktitle = {SAC 38th Annual ACM Symposium on Applied Computing}, keywords = {deep learning, time series, XAI}, pubstate = {published}, tppubtype = {conference} } |
A. R. Troncoso-García and M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso Deep Learning-Based Approach for Sleep Apnea Detection Using Physiological Signals (Conference) IWANN International Work-conference on Artificial Intelligence, Lecture Notes in Computer Science 2023. (BibTeX | Tags: deep learning, feature selection, time series) @conference{IWANN2023, title = {Deep Learning-Based Approach for Sleep Apnea Detection Using Physiological Signals}, author = {A. R. Troncoso-García and M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso}, year = {2023}, date = {2023-01-01}, booktitle = {IWANN International Work-conference on Artificial Intelligence}, series = {Lecture Notes in Computer Science}, keywords = {deep learning, feature selection, time series}, pubstate = {published}, tppubtype = {conference} } |
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} } |
E. Tefera and A. Troncoso and M. Martínez Ballesteros and F. Martínez-Álvarez A New Hybrid CNN-LSTM for Wind Power Forecasting in Ethiopia (Conference) HAIS 18th International Conference on Hybrid Artificial Intelligence Systems, Lecture Notes in Computer Science 2023. (BibTeX | Tags: deep learning, energy, time series) @conference{HAIS23_Ejigu, title = {A New Hybrid CNN-LSTM for Wind Power Forecasting in Ethiopia}, author = {E. Tefera and A. Troncoso and M. Martínez Ballesteros and F. Martínez-Álvarez}, 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, energy, time series}, pubstate = {published}, tppubtype = {conference} } |
M. J. Jiménez-Navarro and M. Martínez-Ballesteros and I. S. Brito and F. Martínez-Álvarez and G. Asencio-Cortés A bioinspired ensemble approach for multi-horizon reference evapotranspiration forecasting in Portugal (Conference) 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 and F. Martínez-Álvarez and D. T. Bui and A. Troncoso A novel semantic segmentation approach based on U-Net, WU-Net, and U-Net++ deep learning for predicting areas sensitive to pluvial flood at tropical area (Journal Article) International Journal of Digital Earth, 16 (1), pp. 3661-3679, 2023. (Links | BibTeX | Tags: deep learning, natural disasters) @article{Melgar2023c, title = {A novel semantic segmentation approach based on U-Net, WU-Net, and U-Net++ deep learning for predicting areas sensitive to pluvial flood at tropical area}, author = {L. Melgar-García and F. Martínez-Álvarez and D. T. Bui and A. Troncoso}, url = {https://www.tandfonline.com/doi/full/10.1080/17538947.2023.2252401}, doi = {https://doi.org/10.1080/17538947.2023.2252401}, year = {2023}, date = {2023-01-01}, journal = {International Journal of Digital Earth}, volume = {16}, number = {1}, pages = {3661-3679}, keywords = {deep learning, natural disasters}, pubstate = {published}, tppubtype = {article} } |
2022 |
A. M. Chacón-Maldonado and M. A. Molina and A. Troncoso and F. Martínez-Álvarez and G. Asencio-Cortés Olive Phenology Forecasting Using Information Fusion-Based Imbalanced Preprocessing and Automated Deep Learning (Conference) HAIS 17th International Conference on Hybrid Artificial Intelligence Systems, Lecture Notes in Computer Science 2022. (Links | BibTeX | Tags: deep learning, pattern recognition, time series) @conference{HAIS22_Andres, title = {Olive Phenology Forecasting Using Information Fusion-Based Imbalanced Preprocessing and Automated Deep Learning}, author = {A. M. Chacón-Maldonado and M. A. Molina and A. Troncoso and F. Martínez-Álvarez and G. Asencio-Cortés}, url = {https://link.springer.com/chapter/10.1007/978-3-031-15471-3_24}, year = {2022}, date = {2022-09-12}, booktitle = {HAIS 17th International Conference on Hybrid Artificial Intelligence Systems}, journal = {HAIS 17th International Conference on Hybrid Artificial Intelligence Systems, Lecture Notes in Computer Science 2022}, pages = {274-285}, series = {Lecture Notes in Computer Science}, keywords = {deep learning, pattern recognition, time series}, pubstate = {published}, tppubtype = {conference} } |
A. R. Troncoso-García and M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso Explainable machine learning for sleep apnea prediction (Conference) KES International Conference on Knowledge Based and Intelligent information and Engineering Systems, 2022. (Abstract | Links | BibTeX | Tags: association rules, deep learning, time series, XAI) @conference{TRONCOSO-GARCIA22, title = {Explainable machine learning for sleep apnea prediction}, author = {A. R. Troncoso-García and M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso}, url = {https://www.sciencedirect.com/science/article/pii/S1877050922012406}, doi = {https://doi.org/10.1016/j.procs.2022.09.351}, year = {2022}, date = {2022-09-10}, booktitle = {KES International Conference on Knowledge Based and Intelligent information and Engineering Systems}, pages = {2930-2939}, abstract = {Machine and deep learning has become one of the most useful tools in the last years as a diagnosis-decision-support tool in the health area. However, it is widely known that artificial intelligence models are considered a black box and most experts experience difficulties explaining and interpreting the models and their results. In this context, explainable artificial intelligence is emerging with the aim of providing black-box models with sufficient interpretability so that models can be easily understood and further applied. Obstructive sleep apnea is a common chronic respiratory disease related to sleep. Its diagnosis nowadays is done by processing different data signals, such as electrocardiogram or respiratory rate. The waveform of the respiratory signal is of importance too. Machine learning models could be applied to the signal's analysis. Data from a polysomnography study for automatic sleep apnea detection have been used to evaluate the use of the Local Interpretable Model-Agnostic (LIME) library for explaining the health data models. Results obtained help to understand how several features have been used in the model and their influence in the quality of sleep.}, keywords = {association rules, deep learning, time series, XAI}, pubstate = {published}, tppubtype = {conference} } Machine and deep learning has become one of the most useful tools in the last years as a diagnosis-decision-support tool in the health area. However, it is widely known that artificial intelligence models are considered a black box and most experts experience difficulties explaining and interpreting the models and their results. In this context, explainable artificial intelligence is emerging with the aim of providing black-box models with sufficient interpretability so that models can be easily understood and further applied. Obstructive sleep apnea is a common chronic respiratory disease related to sleep. Its diagnosis nowadays is done by processing different data signals, such as electrocardiogram or respiratory rate. The waveform of the respiratory signal is of importance too. Machine learning models could be applied to the signal's analysis. Data from a polysomnography study for automatic sleep apnea detection have been used to evaluate the use of the Local Interpretable Model-Agnostic (LIME) library for explaining the health data models. Results obtained help to understand how several features have been used in the model and their influence in the quality of sleep. |
P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and J. R. Villar-Flecha and A. Troncoso and E. A. de la Cal and Á. Herrero and F. Martínez-Álvarez and G. Psaila and H. Quintián and E. Corchado Springer, 13469 , 2022, ISBN: 978-3-031-15470-6. (Links | BibTeX | Tags: big data, clustering, deep learning, IoT) @proceedings{HAIS2022, title = {Proceedings of the 17th International Conference on Hybrid Artificial Intelligent Systems (HAIS 2022) Salamanca, Spain, September 5-7, 2022}, author = {P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and J. R. Villar-Flecha and A. Troncoso and E. A. de la Cal and Á. Herrero and F. Martínez-Álvarez and G. Psaila and H. Quintián and E. Corchado}, url = {https://link.springer.com/book/10.1007/978-3-031-15471-3}, doi = {https://doi.org/10.1007/978-3-031-15471-3}, isbn = {978-3-031-15470-6}, year = {2022}, date = {2022-09-05}, volume = {13469}, 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 J. R. Villar-Flecha and A. Troncoso and E. A. de la Cal and Á. Herrero and F. Martínez-Álvarez and G. Psaila and H. Quintián and E. Corchado Springer, 531 , 2022, ISBN: 978-3-031-18050-7. (Links | BibTeX | Tags: big data, clustering, deep learning, IoT) @proceedings{SOCO2022, title = {Proceedings of the 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022) Salamanca, Spain, September 5-7, 2022}, author = {P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and J. R. Villar-Flecha and A. Troncoso and E. A. de la Cal and Á. Herrero and F. Martínez-Álvarez and G. Psaila and H. Quintián and E. Corchado}, url = {https://link.springer.com/book/10.1007/978-3-031-18050-7}, doi = {https://doi.org/10.1007/978-3-031-18050-7}, isbn = {978-3-031-18050-7}, year = {2022}, date = {2022-09-05}, volume = {531}, 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 J. R. Villar-Flecha and A. Troncoso and E. A. de la Cal and Á. Herrero and F. Martínez-Álvarez and G. Psaila and H. Quintián and E. Corchado Springer, 532 , 2022, ISBN: 978-3-031-18409-3. (Links | BibTeX | Tags: big data, deep learning) @proceedings{CISIS-ICEUTE2022, title = {Proceedings of the International Joint Conference 15th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2022) 13th International Conference on EUropean Transnational Education (ICEUTE 2022). Salamanca, Spain, September 5-7, 2022}, author = {P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and J. R. Villar-Flecha and A. Troncoso and E. A. de la Cal and Á. Herrero and F. Martínez-Álvarez and G. Psaila and H. Quintián and E. Corchado}, url = {https://link.springer.com/book/10.1007/978-3-031-18409-3}, doi = {https://doi.org/10.1007/978-3-031-18409-3}, isbn = {978-3-031-18409-3}, year = {2022}, date = {2022-09-05}, volume = {532}, publisher = {Springer}, series = {Lecture Notes in Networks and Systems}, keywords = {big data, deep learning}, pubstate = {published}, tppubtype = {proceedings} } |
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, 34 , pp. 10533-10545, 2022. (Abstract | Links | BibTeX | Tags: deep learning, energy) @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 = {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%. |
E. T. Habtermariam and K. Kekeba and A. Troncoso and F. Martínez-Álvarez A Cluster-Based Deep Learning Model for Energy Consumption Forecasting in Ethiopia (Conference) SOCO 17th International Conference on Soft Computing Models in Industrial and Environmental Applications , 531 , Lecture Notes in Networks and Systems 2022. (Links | BibTeX | Tags: deep learning, energy, pattern recognition, time series) @conference{SOCO22_Ejigu, title = {A Cluster-Based Deep Learning Model for Energy Consumption Forecasting in Ethiopia}, author = {E. T. Habtermariam and K. Kekeba and A. Troncoso and F. Martínez-Álvarez}, url = {https://link.springer.com/chapter/10.1007/978-3-031-18050-7_41}, year = {2022}, date = {2022-01-01}, booktitle = {SOCO 17th International Conference on Soft Computing Models in Industrial and Environmental Applications }, journal = {SOCO 17th International Conference on Soft Computing Models in Industrial and Environmental Applications, Lecture Notes in Networks Systems, Vol. 531.}, volume = {531}, pages = {423-432}, series = {Lecture Notes in Networks and Systems}, keywords = {deep learning, energy, pattern recognition, time series}, pubstate = {published}, tppubtype = {conference} } |
M. J. Jiménez-Navarro and M. Martínez-Ballesteros and I. S. Sousa Brito and F. Martínez-Álvarez and G. Asencio-Cortés Feature-Aware Drop Layer (FADL): A Nonparametric Neural Network Layer for Feature Selection (Conference) SOCO 17th International Conference on Soft Computing Models in Industrial and Environmental Applications, 531 , Lecture Notes in Networks Systems 2022. (Links | BibTeX | Tags: deep learning, feature selection) @conference{FADL23, title = {Feature-Aware Drop Layer (FADL): A Nonparametric Neural Network Layer for Feature Selection}, author = {M. J. Jiménez-Navarro and M. Martínez-Ballesteros and I. S. Sousa Brito and F. Martínez-Álvarez and G. Asencio-Cortés}, url = {https://link.springer.com/chapter/10.1007/978-3-031-18050-7_54}, year = {2022}, date = {2022-01-01}, booktitle = {SOCO 17th International Conference on Soft Computing Models in Industrial and Environmental Applications}, volume = {531}, pages = {557-566}, series = {Lecture Notes in Networks Systems}, keywords = {deep learning, feature selection}, pubstate = {published}, tppubtype = {conference} } |
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 | Tags: deep learning, time series) @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 = {deep learning, time series}, pubstate = {published}, tppubtype = {article} } 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. |
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 | 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 = {CAEPIA Conference of the Spanish Association for Artificial Intelligence }, 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 16th International Conference on Hybrid Artificial Intelligence Systems, 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 16th International Conference on Hybrid Artificial Intelligence Systems}, 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. |
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. |
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) SOCO 15th International Conference on Soft Computing Models in Industrial and Environmental Applications, 1268 , Advances in Intelligent Systems and Computing 2020. (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 = {2020}, date = {2020-01-01}, booktitle = {SOCO 15th International Conference on Soft Computing Models in Industrial and Environmental Applications}, volume = {1268}, pages = {741-750}, series = {Advances in Intelligent Systems and Computing}, keywords = {deep learning, pattern recognition, transfer learning}, pubstate = {published}, tppubtype = {conference} } |
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. M. Fernández 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. (Abstract | 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. M. Fernández 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}, abstract = {This paper presents a novel method to predict times series using deep learning. In particular, the method can be used for arbitrary time horizons, dividing each predicted sample into a single problem. This fact allows easy parallelization and adaptation to the big data context. Deep learning implementation in H2O library is used for each subproblem. However, H2O does not permit multi-step regression, therefore the solution proposed consists in splitting into h forecasting subproblems, being h the number of samples to be predicted, and, each of one has been separately studied, getting the best prediction model for each subproblem. Additionally, Apache Spark is used to load in memory large datasets and speed up the execution time. This methodology has been tested on a real-world dataset composed of electricity consumption in Spain, with a ten minute frequency sampling rate, from 2007 to 2016. Reported results exhibit errors less than 2%.}, keywords = {deep learning, energy, time series}, pubstate = {published}, tppubtype = {conference} } This paper presents a novel method to predict times series using deep learning. In particular, the method can be used for arbitrary time horizons, dividing each predicted sample into a single problem. This fact allows easy parallelization and adaptation to the big data context. Deep learning implementation in H2O library is used for each subproblem. However, H2O does not permit multi-step regression, therefore the solution proposed consists in splitting into h forecasting subproblems, being h the number of samples to be predicted, and, each of one has been separately studied, getting the best prediction model for each subproblem. Additionally, Apache Spark is used to load in memory large datasets and speed up the execution time. This methodology has been tested on a real-world dataset composed of electricity consumption in Spain, with a ten minute frequency sampling rate, from 2007 to 2016. Reported results exhibit errors less than 2%. |