David Guijo-Rubio

621 total citations
33 papers, 340 citations indexed

About

David Guijo-Rubio is a scholar working on Artificial Intelligence, Environmental Engineering and Signal Processing. According to data from OpenAlex, David Guijo-Rubio has authored 33 papers receiving a total of 340 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 10 papers in Environmental Engineering and 7 papers in Signal Processing. Recurrent topics in David Guijo-Rubio's work include Time Series Analysis and Forecasting (7 papers), Energy Load and Power Forecasting (7 papers) and Hydrological Forecasting Using AI (6 papers). David Guijo-Rubio is often cited by papers focused on Time Series Analysis and Forecasting (7 papers), Energy Load and Power Forecasting (7 papers) and Hydrological Forecasting Using AI (6 papers). David Guijo-Rubio collaborates with scholars based in Spain, United Kingdom and Australia. David Guijo-Rubio's co-authors include César Hervás‐Martínez, Pedro Antonio Gutiérrez, Sancho Salcedo‐Sanz, Antonio M. Durán-Rosal, J. Sanz, C. Casanova‐Mateo, Alicia Troncoso, María Dolores Ayllón, Javier Briceño and Rubén Ciria and has published in prestigious journals such as PLoS ONE, Expert Systems with Applications and Energy.

In The Last Decade

David Guijo-Rubio

27 papers receiving 334 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
David Guijo-Rubio Spain 10 126 85 82 43 38 33 340
Adriaan Brebels Russia 4 88 0.7× 78 0.9× 41 0.5× 33 0.8× 12 0.3× 8 364
Antonio M. Durán-Rosal Spain 10 145 1.2× 80 0.9× 53 0.6× 69 1.6× 32 0.8× 24 297
Anton Tyukov Russia 4 81 0.6× 65 0.8× 38 0.5× 26 0.6× 12 0.3× 8 341
Ran Chen China 14 66 0.5× 35 0.4× 60 0.7× 23 0.5× 2 0.1× 68 636
Timur Janovsky Russia 2 72 0.6× 59 0.7× 37 0.5× 22 0.5× 12 0.3× 3 326
Thomas Skjødeberg Toftegaard Denmark 10 45 0.4× 80 0.9× 58 0.7× 16 0.4× 4 0.1× 38 536
Yanmei Xu China 8 41 0.3× 62 0.7× 46 0.6× 17 0.4× 2 0.1× 28 418
Evangelos A. Yfantis United States 11 165 1.3× 27 0.3× 98 1.2× 16 0.4× 9 0.2× 54 585
Mohamad Mazen Hittawe France 13 126 1.0× 41 0.5× 44 0.5× 12 0.3× 13 0.3× 16 400

Countries citing papers authored by David Guijo-Rubio

Since Specialization
Citations

This map shows the geographic impact of David Guijo-Rubio's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by David Guijo-Rubio with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Guijo-Rubio more than expected).

Fields of papers citing papers by David Guijo-Rubio

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Guijo-Rubio. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by David Guijo-Rubio. The network helps show where David Guijo-Rubio may publish in the future.

Co-authorship network of co-authors of David Guijo-Rubio

This figure shows the co-authorship network connecting the top 25 collaborators of David Guijo-Rubio. A scholar is included among the top collaborators of David Guijo-Rubio based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with David Guijo-Rubio. David Guijo-Rubio is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Martínez-Estudillo, Francisco José, et al.. (2025). Splitting criteria for ordinal decision trees: An experimental study. Pattern Recognition. 171. 112273–112273.
2.
Guijo-Rubio, David, et al.. (2025). dlordinal: A Python package for deep ordinal classification. Neurocomputing. 622. 129305–129305. 3 indexed citations
3.
Guijo-Rubio, David, et al.. (2025). Enhancing wind speed prediction in wind farms through ordinal classification. Energy and AI. 22. 100596–100596.
4.
Salcedo‐Sanz, Sancho, et al.. (2025). Artificial Intelligence-Based Methods and Algorithms in Fog and Atmospheric Low-Visibility Forecasting. Atmosphere. 16(9). 1073–1073.
5.
Bagnall, Anthony, Matthew Middlehurst, Germain Forestier, et al.. (2024). A Hands-on Introduction to Time Series Classification and Regression. ePrints Soton (University of Southampton). 6410–6411. 2 indexed citations
6.
Guijo-Rubio, David, et al.. (2024). Convolutional- and Deep Learning-Based Techniques for Time Series Ordinal Classification. IEEE Transactions on Cybernetics. 55(2). 537–549. 2 indexed citations
7.
Pérez‐Aracil, Jorge, et al.. (2024). Fuzzy-based ensemble methodology for accurate long-term prediction and interpretation of extreme significant wave height events. Applied Ocean Research. 153. 104273–104273. 3 indexed citations
8.
Gutiérrez, Pedro Antonio, et al.. (2024). EBANO: A novel Ensemble BAsed on uNimodal Ordinal classifiers for the prediction of significant wave height. Knowledge-Based Systems. 300. 112223–112223. 3 indexed citations
9.
Guijo-Rubio, David, et al.. (2024). ORFEO: Ordinal classifier and Regressor Fusion for Estimating an Ordinal categorical target. Engineering Applications of Artificial Intelligence. 133. 108462–108462. 6 indexed citations
10.
Guijo-Rubio, David, et al.. (2023). Cluster analysis and forecasting of viruses incidence growth curves: Application to SARS-CoV-2. Expert Systems with Applications. 225. 120103–120103. 3 indexed citations
11.
Durán-Rosal, Antonio M., et al.. (2023). Generalised triangular distributions for ordinal deep learning: Novel proposal and optimisation. Information Sciences. 648. 119606–119606. 6 indexed citations
12.
Guijo-Rubio, David, et al.. (2023). An Evolutionary Artificial Neural Network approach for spatio-temporal wave height time series reconstruction. Applied Soft Computing. 146. 110647–110647. 8 indexed citations
13.
Guijo-Rubio, David, et al.. (2023). One month in advance prediction of air temperature from Reanalysis data with eXplainable Artificial Intelligence techniques. Atmospheric Research. 284. 106608–106608. 12 indexed citations
14.
Guijo-Rubio, David, et al.. (2022). COVID-19 contagion forecasting framework based on curve decomposition and evolutionary artificial neural networks: A case study in Andalusia, Spain. Expert Systems with Applications. 207. 117977–117977. 4 indexed citations
15.
Guijo-Rubio, David, et al.. (2021). Potenciando el perfil profesional Científico de Datos mediante dinámicas de competición. Universidad de Córdoba Insitutional Repository (Universidad de Córdoba). 10(2). 101–116.
16.
Rivero‐Juárez, Antonio, David Guijo-Rubio, Francisco Téllez, et al.. (2020). Using machine learning methods to determine a typology of patients with HIV-HCV infection to be treated with antivirals. PLoS ONE. 15(1). e0227188–e0227188. 4 indexed citations
17.
Guijo-Rubio, David, Antonio M. Durán-Rosal, Pedro Antonio Gutiérrez, et al.. (2020). Evolutionary artificial neural networks for accurate solar radiation prediction. Energy. 210. 118374–118374. 70 indexed citations
18.
Guijo-Rubio, David, Antonio M. Durán-Rosal, Pedro Antonio Gutiérrez, Alicia Troncoso, & César Hervás‐Martínez. (2020). Time-Series Clustering Based on the Characterization of Segment Typologies. IEEE Transactions on Cybernetics. 51(11). 5409–5422. 37 indexed citations
19.
Guijo-Rubio, David, et al.. (2020). Short- and long-term energy flux prediction using Multi-Task Evolutionary Artificial Neural Networks. Ocean Engineering. 216. 108089–108089. 15 indexed citations
20.
Comino, Francisco, David Guijo-Rubio, Manuel Ruiz de Adana, & César Hervás‐Martínez. (2019). Validation of multitask artificial neural networks to model desiccant wheels activated at low temperature. International Journal of Refrigeration. 100. 434–442. 10 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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