Francisco Martínez-Álvarez
Francisco Martínez-Álvarez
Data Science & Big Data Lab, Pablo de Olavide University of Seville, Spain
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Zitiert von
Jahr
Energy time series forecasting based on pattern sequence similarity
F Martinez Alvarez, A Troncoso, J Riquelme, J Aguilar Ruiz
IEEE Transactions on Knowledge and Data Engineering 23 (8), 1230-1243, 2011
244*2011
Neural networks to predict earthquakes in Chile
J Reyes, A Morales-Esteban, F Martínez-Álvarez
Applied Soft Computing 13 (2), 1314-1328, 2013
1372013
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
1272015
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
802010
Determining the best set of seismicity indicators to predict earthquakes. Two case studies: Chile and the Iberian Peninsula
F Martínez-Álvarez, J Reyes, A Morales-Esteban, C Rubio-Escudero
Knowledge-Based Systems 50, 198-210, 2013
752013
Earthquake magnitude prediction in Hindukush region using machine learning techniques
KM Asim, F Martínez-Álvarez, A Basit, T Iqbal
Natural Hazards 85 (1), 471-486, 2017
732017
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
692019
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
642010
A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area
DT Bui, ND Hoang, F Martínez-Álvarez, PTT Ngo, PV Hoa, TD Pham, ...
Science of The Total Environment 701, 134413, 2020
602020
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
592018
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
582015
Earthquake prediction in seismogenic areas of the Iberian Peninsula based on computational intelligence
A Morales-Esteban, F Martínez-Álvarez, J Reyes
Tectonophysics 593, 121-134, 2013
552013
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
502018
A fast partitioning algorithm using adaptive Mahalanobis clustering with application to seismic zoning
A Morales-Esteban, F Martínez-Álvarez, S Scitovski, R Scitovski
Computers & Geosciences 73, 132-141, 2014
492014
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 (5), 1043-1055, 2017
472017
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 (10), 2065, 2011
462011
Detecting precursory patterns to enhance earthquake prediction in Chile
E Florido, F Martínez-Álvarez, A Morales-Esteban, J Reyes, ...
Computers & geosciences 76, 112-120, 2015
452015
Partitioning-clustering techniques applied to the electricity price time series
F Martínez-Álvarez, A Troncoso, JC Riquelme, JM Riquelme
International Conference on Intelligent Data Engineering and Automated …, 2007
442007
A sensitivity study of seismicity indicators in supervised learning to improve earthquake prediction
G Asencio-Cortés, F Martínez-Álvarez, A Morales-Esteban, J Reyes
Knowledge-Based Systems 101, 15-30, 2016
432016
Combining pattern sequence similarity with neural networks for forecasting electricity demand time series
I Koprinska, M Rana, A Troncoso, F Martínez-Álvarez
The 2013 International Joint Conference on Neural Networks (IJCNN), 1-8, 2013
402013
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