Francisco Martínez-Álvarez
Francisco Martínez-Álvarez
Full Professor, Data Science & Big Data Lab, Pablo de Olavide University of Seville, Spain
Verified email at - Homepage
Cited by
Cited by
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
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
Energy time series forecasting based on pattern sequence similarity
F Martínez-Álvarez, A Troncoso, JC Riquelme, JS Aguilar-Ruiz
IEEE Transactions on Knowledge and Data Engineering 23 (8), 1230-1243, 2011
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
Neural networks to predict earthquakes in Chile
J Reyes, A Morales-Esteban, F Martínez-Álvarez
Applied Soft Computing 13 (2), 1314-1328, 2013
Earthquake magnitude prediction in Hindukush region using machine learning techniques
KM Asim, F Martínez-Álvarez, A Basit, T Iqbal
Natural Hazards 85, 471-486, 2017
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
A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source geospatial data
MS Tehrany, S Jones, F Shabani, F Martínez-Álvarez, D Tien Bui
Theoretical and Applied Climatology 137, 637-653, 2019
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
Earthquake prediction model using support vector regressor and hybrid neural networks
KM Asim, A Idris, T Iqbal, F Martínez-Álvarez
PloS one 13 (7), e0199004, 2018
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
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
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-Cortes, F Martinez-Alvarez, A Troncoso, A Morales-Esteban
Neural Computing and Applications 28, 1043-1055, 2017
Earthquake prediction in California using regression algorithms and cloud-based big data infrastructure
G Asencio–Cortés, A Morales–Esteban, X Shang, F Martínez–Álvarez
Computers & Geosciences 115, 198-210, 2018
A novel imputation methodology for time series based on pattern sequence forecasting
N Bokde, MW Beck, FM Álvarez, K Kulat
Pattern recognition letters 116, 88-96, 2018
Seismic indicators based earthquake predictor system using Genetic Programming and AdaBoost classification
KM Asim, A Idris, T Iqbal, F Martínez-Álvarez
Soil Dynamics and Earthquake Engineering 111, 1-7, 2018
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
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