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Francisco Martínez-Álvarez

Francisco Martínez-Álvarez

D-Index & Metrics

Computer Science

D-Index
41
Citations
6668
World Ranking
8884
National Ranking
131

Overview

Francisco Martínez-Álvarez is affiliated with Pablo de Olavide University in Spain. Their research focuses primarily on computer science, engineering, and environmental science, with significant contributions in artificial intelligence, management science and operations research, electrical and electronic engineering, signal processing, and plant science.

Their work covers key topics such as energy load and power forecasting, time series analysis and forecasting, stock market forecasting methods, data stream mining techniques, forecasting techniques and applications, smart agriculture and AI, and machine learning and data classification.

Recent publications include:

  • Deep Learning for Time Series Forecasting: A Survey, 2020, Big Data
  • A deep LSTM network for the Spanish electricity consumption forecasting, 2022, Neural Computing and Applications
  • Electricity consumption forecasting based on ensemble deep learning with application to the Algerian market, 2021, Energy
  • Deformation forecasting of a hydropower dam by hybridizing a long short-term memory deep learning network with the coronavirus optimization algorithm, 2022, Computer-Aided Civil and Infrastructure Engineering
  • Big data time series forecasting based on pattern sequence similarity and its application to the electricity demand, 2020, Information Sciences

Frequent co-authors in Martínez-Álvarez's research include Alicia Troncoso, M. Martínez-Ballesteros, G. Asencio-Cortés, M. J. Jiménez-Navarro, and J. F. Torres.

Several academic journals have published multiple papers by Martínez-Álvarez, including Neurocomputing, SSRN Electronic Journal, Information Sciences, Applied Sciences, and Computers & Geosciences.

Martínez-Álvarez has also contributed to books published mainly by Springer International Publishing, Springer Science+Business Media, and Springer Nature. Titles include the 18th and 17th International Conferences on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023, SOCO 2022), Hybrid Artificial Intelligent Systems (2022, 2023), and Computational Intelligence for Water and Environmental Sciences (2022).

Best Publications

  • Deep Learning for Time Series Forecasting: A Survey.

    José F. Torres;Dalil Hadjout;Abderrazak Sebaa;Francisco Martínez-Álvarez

  • A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area.

    Dieu Tien Bui;Nhat-Duc Hoang;Francisco Martínez-Álvarez;Phuong-Thao Thi Ngo

  • Energy Time Series Forecasting Based on Pattern Sequence Similarity

    Francisco Martinez Alvarez;A Troncoso;J C Riquelme;Jesus S Aguilar Ruiz

  • Earthquake magnitude prediction in Hindukush region using machine learning techniques

    Khawaja M. Asim;Francisco Martínez-Álvarez;A. Basit;Talat Iqbal

  • Multi-step forecasting for big data time series based on ensemble learning

    A. Galicia;R. Talavera-Llames;A. Troncoso;I. Koprinska

  • Neural networks to predict earthquakes in Chile

    J. Reyes;A. Morales-Esteban;F. MartíNez-ÁLvarez

  • A deep LSTM network for the Spanish electricity consumption forecasting

    Unknown

  • A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

    Francisco Martínez-Álvarez;Alicia Troncoso;Gualberto Asencio-Cortés;José C. Riquelme

  • A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source geospatial data

    Mahyat Shafapour Tehrany;Simon Jones;Farzin Shabani;Farzin Shabani;Francisco Martínez-Álvarez

  • Coronavirus Optimization Algorithm: A Bioinspired Metaheuristic Based on the COVID-19 Propagation Model.

    Francisco Martínez-Álvarez;Gualberto Asencio-Cortés;José F. Torres;David Gutiérrez-Avilés

  • Earthquake prediction model using support vector regressor and hybrid neural networks.

    Khawaja M Asim;Adnan Idris;Talat Iqbal;Francisco Martínez-Álvarez

  • A scalable approach based on deep learning for big data time series forecasting

    J.F. Torres;A. Galicia;A. Troncoso;F. Martínez-Álvarez

  • 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

  • Pattern recognition to forecast seismic time series

    A. Morales-Esteban;F. Martínez-Álvarez;A. Troncoso;J. L. Justo

  • Big Data Analytics for Discovering Electricity Consumption Patterns in Smart Cities

    Rubén Pérez-Chacón;José M. Luna-Romera;Alicia Troncoso;Francisco Martínez-Álvarez

  • A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables

    J. García-Gutiérrez;F. Martínez-Álvarez;A. Troncoso;J.C. Riquelme

  • Medium---large earthquake magnitude prediction in Tokyo with artificial neural networks

    G. Asencio-Cortés;F. Martínez-Álvarez;A. Troncoso;A. Morales-Esteban

  • Cluster Analysis and Applications

    Unknown

  • A fast partitioning algorithm using adaptive Mahalanobis clustering with application to seismic zoning

    Antonio Morales-Esteban;Francisco Martínez-Álvarez;Sanja Scitovski;Rudolf Scitovski

  • Earthquake prediction in seismogenic areas of the Iberian Peninsula based on computational intelligence

    A. Morales-Esteban;F. Martínez-Álvarez;J. Reyes

  • Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution

    M. Martínez-Ballesteros;A. Troncoso;F. Martínez-Álvarez;J. C. Riquelme

  • A sensitivity study of seismicity indicators in supervised learning to improve earthquake prediction

    G. Asencio-Cortés;F. Martínez-Álvarez;A. Morales-Esteban;J. Reyes

  • Seismic indicators based earthquake predictor system using Genetic Programming and AdaBoost classification

    Khawaja M. Asim;Adnan Idris;Talat Iqbal;Francisco Martínez-Álvarez

Frequent Co-Authors

Alicia Troncoso
Alicia Troncoso Pablo de Olavide University
José C. Riquelme
José C. Riquelme University of Seville
Dieu Tien Bui
Dieu Tien Bui University of South-Eastern Norway
José Miguel Azañón
José Miguel Azañón University of Granada
Nhat-Duc Hoang
Nhat-Duc Hoang Duy Tan University
Emilio Corchado
Emilio Corchado University of Salamanca
Pijush Samui
Pijush Samui National Institute of Technology Patna
Simon Jones
Simon Jones Microsoft (United States)

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