Alicia Troncoso (ORCID: 0000-0002-9801-7999)
Alicia Troncoso (ORCID: 0000-0002-9801-7999)
Full Professor, Data Science & Big Data Lab, Universidad Pablo de Olavide
Dirección de correo verificada de - Página principal
Citado por
Citado por
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
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
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
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
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
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
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
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
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
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
Medium–large earthquake magnitude prediction in Tokyo with artificial neural networks
G Asencio-Cortés, F Martínez-Álvarez, A Troncoso, A Morales-Esteban
Neural Computing and Applications 28, 1043-1055, 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
Biclustering of gene expression data by correlation-based scatter search
JA Nepomuceno, A Troncoso, JS Aguilar-Ruiz
BioData mining 4, 1-17, 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
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
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
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
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
Time-series prediction: Application to the short-term electric energy demand
A Troncoso Lora, JM Riquelme Santos, JC Riquelme, A Gómez Expósito, ...
Current Topics in Artificial Intelligence: 10th Conference of the Spanish …, 2004
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
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–20