‪Alicia Troncoso (ORCID: 0000-0002-9801-7999) https://datalab.upo.es/troncoso‬

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Francisco Martínez-ÁlvarezFull Professor, Data Science & Big Data Lab, Pablo de Olavide University of Seville, SpainVerified email at upo.es
José C. RiquelmeUniversidad de SevillaVerified email at us.es
Gualberto Asencio-Cortés, Ph.D. (000...Universidad Pablo de Olavide, Sevilla, EspañaVerified email at upo.es
José Francisco Torres MaldonadoData Science & Big Data Lab, Pablo de Olavide UniversityVerified email at upo.es
Jesus S. Aguilar-RuizFull Professor, Data Analytics Science & Engineering, Universidad Pablo de Olavide, Sevilla, EspañaVerified email at upo.es
María Martínez BallesterosUniversidad de SevillaVerified email at us.es
David Gutiérrez-AvilésUniversidad de SevillaVerified email at us.es
Antonio Gomez-ExpositoProfessor of Electrical Engineering, Universidad de SevillaVerified email at us.es
Cristina Rubio-EscuderoSenior Lecturer, University of SevillaVerified email at us.es
Juan A. NepomucenoUniversidad de SevillaVerified email at us.es
Jose Luis Martinez RamosUniversidad de SevillaVerified email at us.es
Laura Melgar-GarcíaUniversidad Politécnica de MadridVerified email at upm.es
Rubén Pérez ChacónAssistant ProfessorVerified email at upo.es
Irena KoprinskaSchool of Computer Science, University of SydneyVerified email at sydney.edu.au
Jesus RiquelmeUniversity SevillaVerified email at us.es
Antonio Morales-EstebanUniversity of SevilleVerified email at us.es
Oscar LuacesArtificial Intelligence Center (University of Oviedo)Verified email at uniovi.es
Ricardo L. Talavera-Llames (0000-00...Universidad Pablo de Olavide, Sevilla, España.Verified email at upo.es
Antonio BahamondeUniversidad de OviedoVerified email at uniovi.es
Amparo Alonso-BetanzosCatedrática de Computación, CITIC (UDC)Verified email at udc.es

Alicia Troncoso (ORCID: 0000-0002-9801-7999) https://datalab.upo.es/troncoso

Full Professor, Data Science & Big Data Lab, Universidad Pablo de Olavide

Verified email at upo.es - Homepage

Deep Learning Time Series Forecasting Energy Forecasting Explainability Data Mining


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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	610	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	338	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	248	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	214	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	156	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	138	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	93	2022
Biclustering of gene expression data by correlation-based scatter search JA Nepomuceno, A Troncoso, JS Aguilar-Ruiz BioData mining 4, 1-17, 2011	84	2011
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	83	2022
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
Big data solar power forecasting based on deep learning and multiple data sources JF Torres, A Troncoso, I Koprinska, Z Wang, F Martínez‐Álvarez Expert Systems 36 (4), e12394, 2019	68	2019
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	67	2020
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	66	2022
An evolutionary algorithm to discover quantitative association rules in multidimensional time series M Martínez-Ballesteros, F Martínez-Álvarez, A Troncoso, JC Riquelme Soft Computing 15, 2065-2084, 2011	61	2011

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