J. F. Torres

1.9k citations

22 papers · 1.1k indexed · 1 hit paper · h-index 11

Electrical and Electronic Engineering top 10%
Artificial Intelligence top 5%
Management Science and Operations Research top 2%
Signal Processing top 5%
Environmental Engineering top 10%

Co-authors: Francisco Martínez‐Álvarez Alicia Troncoso Dalil Hadjout Abderrazak Sebaa Federico Divina Miguel García-Torres Francisco Gómez-Vela Irena Koprinska
Topics: Energy Load and Power Forecasting (9 papers)Stock Market Forecasting Methods (4 papers)Spam and Phishing Detection (3 papers)
Cited by: Management Science and Operations Research Signal Processing Environmental Engineering
Journals: Expert Systems with Applications Energy Information Sciences
Partner nations: Spain Venezuela Algeria

In The Last Decade

J. F. Torres

20 papers receiving 1.0k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Deep Learning for Time Series Forecasting: A Survey

2020 441 citations J. F. Torres, Dalil Hadjout et al. Big Data profile →

Peers

Countries citing papers authored by J. F. Torres

Since Specialization

Citations

This map shows the geographic impact of J. F. Torres'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 J. F. Torres with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites J. F. Torres more than expected).

Fields of papers citing papers by J. F. Torres

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by J. F. Torres. 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 J. F. Torres. The network helps show where J. F. Torres may publish in the future.

Co-authorship network of co-authors of J. F. Torres

This figure shows the co-authorship network connecting the top 25 collaborators of J. F. Torres. A scholar is included among the top collaborators of J. F. Torres 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 J. F. Torres. J. F. Torres is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

#	Work	Indexed citations
1	Energy-efficient transfer learning for water consumption forecasting 2025 ·Sustainable Computing Informatics and Systems ·(unknown),J. F. Torres,Francisco Martínez‐Álvarez,Alicia Troncoso	2
2	Forecasting basal area increment in forest ecosystems using deep learning: A multi-species analysis in the Himalayas 2024 ·Ecological Informatics ·(unknown),J. F. Torres,Juan Carlos Linares,A. Troncoso,Francisco Martínez‐Álvarez	1
3	Model to Predict Inventory Demand in Retail SMEs Using CRISP-DM and Machine Learning 2024 ·J. F. Torres,(unknown),(unknown)	0
4	Water consumption time series forecasting in urban centers using deep neural networks 2024 ·Applied Water Science ·Carlos García-Soto,J. F. Torres,Miguel A. Zamora,José Palma,Alicia Troncoso	5
5	Electricity consumption forecasting with outliers handling based on clustering and deep learning with application to the Algerian market 2023 ·Expert Systems with Applications ·Dalil Hadjout,Abderrazak Sebaa,J. F. Torres,Francisco Martínez‐Álvarez	15
6	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 ·Kien-Trinh Thi Bui,J. F. Torres,David Gutiérrez‐Avilés,Viet‐Ha Nhu,Dieu Tien Bui,Francisco Martínez‐Álvarez	46
7	A methodology for understanding passenger flows combining mobile phone records and airport surveys: Application to Madrid-Barajas Airport after the COVID-19 outbreak 2022 ·Journal of Air Transport Management ·(unknown),(unknown),(unknown),(unknown),(unknown),(unknown),J. F. Torres,(unknown),Oliva G. Cantú Ros,Ricardo Herranz	14
8	A deep LSTM network for the Spanish electricity consumption forecasting 2022 ·Neural Computing and Applications ·J. F. Torres,Francisco Martínez‐Álvarez,Alicia Troncoso	91
9	Deep Learning for Time Series Forecasting: A Surveybreakdown → 2020 ·Big Data ·J. F. Torres,Dalil Hadjout,Abderrazak Sebaa,Francisco Martínez‐Álvarez,Alicia Troncoso	441
10	Hybridizing Deep Learning and Neuroevolution: Application to the Spanish Short-Term Electric Energy Consumption Forecasting 2020 ·Applied Sciences ·Federico Divina,J. F. Torres,Miguel García-Torres,Francisco Martínez‐Álvarez,Alicia Troncoso	12
11	Big data solar power forecasting based on deep learning and multiple data sources 2019 ·Expert Systems ·J. F. Torres,Alicia Troncoso,Irena Koprinska,(unknown),Francisco Martínez‐Álvarez	50
12	Behavioral Biometric Authentication in Android Unlock Patterns through Machine Learning 2019 ·J. F. Torres,(unknown),Efthimios Alepis,Constantinos Patsakis	6
13	Autonomic communication system based on cognitive techniques 2018 ·International Journal of Knowledge-based and Intelligent Engineering Systems ·José Aguilar,J. F. Torres,(unknown)	2
14	A novel spark-based multi-step forecasting algorithm for big data time series 2018 ·Information Sciences ·(unknown),J. F. Torres,Francisco Martínez‐Álvarez,Alicia Troncoso	22
15	A scalable approach based on deep learning for big data time series forecasting 2018 ·Integrated Computer-Aided Engineering ·J. F. Torres,(unknown),Alicia Troncoso,Francisco Martínez‐Álvarez	92
16	Malicious PDF Documents Detection using Machine Learning Techniques - A Practical Approach with Cloud Computing Applications 2018 ·J. F. Torres,(unknown)	3
17	Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting 2018 ·Energies ·Federico Divina,(unknown),Francisco Gómez-Vela,Miguel García-Torres,J. F. Torres	196
18	Macro Malware Detection using Machine Learning Techniques - A New Approach 2017 ·(unknown),J. F. Torres	8
19	Analysing HSTS and HPKP implementation in both browsers and servers 2017 ·IET Information Security ·(unknown),J. F. Torres	3
20	Robust division of temporal horizons in production planning 2002 ·J. F. Torres	0

About J. F. Torres

J. F. Torres is a scholar working on Management Science and Operations Research, Signal Processing and Information Systems, having authored 22 papers that have together received 1.1k indexed citations. Recurring topics across this work include Energy Load and Power Forecasting (9 papers), Stock Market Forecasting Methods (4 papers) and Spam and Phishing Detection (3 papers). The work is most often cited by research in Management Science and Operations Research (234 citations), Signal Processing (151 citations) and Environmental Engineering (148 citations). J. F. Torres has collaborated with scholars based in Spain, Venezuela and Algeria. Frequent co-authors include Francisco Martínez‐Álvarez, Alicia Troncoso, Dalil Hadjout, Abderrazak Sebaa, Federico Divina, Miguel García-Torres, Francisco Gómez-Vela, Irena Koprinska, Viet‐Ha Nhu and Kien-Trinh Thi Bui. Their work appears in journals such as Expert Systems with Applications, Energy and Information Sciences.

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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