| Deep learning for time series forecasting: a survey JF Torres, D Hadjout, A Sebaa, F Martínez-Álvarez, A Troncoso Big Data 9 (1), 3-21, 2021 | 621 | 2021 |
| Energy time series forecasting based on pattern sequence similarity FM Alvarez, A Troncoso, JC Riquelme, JSA Ruiz IEEE Transactions on Knowledge and Data Engineering 23 (8), 1230-1243, 2010 | 342 | 2010 |
| Electricity market price forecasting based on weighted nearest neighbors techniques AT Lora, JMR Santos, AG Expósito, JLM Ramos, JCR Santos IEEE Transactions on Power Systems 22 (3), 1294-1301, 2007 | 311 | 2007 |
| Multi-step forecasting for big data time series based on ensemble learning A Galicia, R Talavera-Llames, A Troncoso, I Koprinska, ... Knowledge-Based Systems 163, 830-841, 2019 | 250 | 2019 |
| A survey on data mining techniques applied to electricity-related time series forecasting F Martínez-Álvarez, A Troncoso, G Asencio-Cortés, JC Riquelme Energies 8 (11), 13162-13193, 2015 | 215 | 2015 |
| Coronavirus optimization algorithm: a bioinspired metaheuristic based on the COVID-19 propagation model F Martínez-Álvarez, G Asencio-Cortés, JF Torres, D Gutiérrez-Avilés, ... Big data 8 (4), 308-322, 2020 | 159 | 2020 |
| A scalable approach based on deep learning for big data time series forecasting JF Torres, A Galicia, A Troncoso, F Martínez-Álvarez Integrated Computer-Aided Engineering 25 (4), 335-348, 2018 | 139 | 2018 |
| Big data analytics for discovering electricity consumption patterns in smart cities R Pérez-Chacón, JM Luna-Romera, A Troncoso, F Martínez-Álvarez, ... Energies 11 (3), 683, 2018 | 127 | 2018 |
| A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables J García-Gutiérrez, F Martínez-Álvarez, A Troncoso, JC Riquelme Neurocomputing 167, 24-31, 2015 | 119 | 2015 |
| Pattern recognition to forecast seismic time series A Morales-Esteban, F Martínez-Álvarez, A Troncoso, JL Justo, ... Expert systems with applications 37 (12), 8333-8342, 2010 | 113 | 2010 |
| Medium–large earthquake magnitude prediction in Tokyo with artificial neural networks G Asencio-Cortes, F Martinez-Alvarez, A Troncoso, A Morales-Esteban Neural Computing and Applications 28, 1043-1055, 2017 | 103 | 2017 |
| Local models-based regression trees for very short-term wind speed prediction A Troncoso, S Salcedo-Sanz, C Casanova-Mateo, JC Riquelme, L Prieto Renewable Energy 81, 589-598, 2015 | 98 | 2015 |
| A deep LSTM network for the Spanish electricity consumption forecasting JF Torres, F Martínez-Álvarez, A Troncoso Neural Computing and Applications 34 (13), 10533-10545, 2022 | 96 | 2022 |
| Electricity consumption forecasting based on ensemble deep learning with application to the Algerian market D Hadjout, JF Torres, A Troncoso, A Sebaa, F Martínez-Álvarez Energy 243, 123060, 2022 | 85 | 2022 |
| Biclustering of gene expression data by correlation-based scatter search JA Nepomuceno, A Troncoso, JS Aguilar-Ruiz BioData mining 4, 1-17, 2011 | 85 | 2011 |
| Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution M Martínez-Ballesteros, A Troncoso, F Martínez-Álvarez, JC Riquelme Integrated Computer-Aided Engineering 17 (3), 227-242, 2010 | 79 | 2010 |
| Time-series clustering based on the characterization of segment typologies D Guijo-Rubio, AM Durán-Rosal, PA Gutiérrez, A Troncoso, ... IEEE transactions on cybernetics 51 (11), 5409-5422, 2020 | 68 | 2020 |
| Big data solar power forecasting based on deep learning and multiple data sources JF Torres, A Troncoso, I Koprinska, Z Wang, F MartínezÁlvarez Expert Systems 36 (4), e12394, 2019 | 68 | 2019 |
| A new hybrid method for predicting univariate and multivariate time series based on pattern forecasting MA Castán-Lascorz, P Jiménez-Herrera, A Troncoso, G Asencio-Cortés Information Sciences 586, 611-627, 2022 | 65 | 2022 |
| Deep learning-based approach for time series forecasting with application to electricity load JF Torres, AM Fernández, A Troncoso, F Martínez-Álvarez Biomedical Applications Based on Natural and Artificial Computing …, 2017 | 61 | 2017 |
Francisco Martínez-ÁlvarezFull Professor, Data Science & Big Data Lab, Pablo de Olavide University of Seville, Spain在 upo.es 的电子邮件经过验证
