Tiago A. E. Ferreira

1.4k total citations
79 papers, 911 citations indexed

About

Tiago A. E. Ferreira is a scholar working on Artificial Intelligence, Management Science and Operations Research and Economics and Econometrics. According to data from OpenAlex, Tiago A. E. Ferreira has authored 79 papers receiving a total of 911 indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Artificial Intelligence, 31 papers in Management Science and Operations Research and 19 papers in Economics and Econometrics. Recurrent topics in Tiago A. E. Ferreira's work include Stock Market Forecasting Methods (28 papers), Neural Networks and Applications (23 papers) and Complex Systems and Time Series Analysis (18 papers). Tiago A. E. Ferreira is often cited by papers focused on Stock Market Forecasting Methods (28 papers), Neural Networks and Applications (23 papers) and Complex Systems and Time Series Analysis (18 papers). Tiago A. E. Ferreira collaborates with scholars based in Brazil, France and Canada. Tiago A. E. Ferreira's co-authors include Paulo S. G. de Mattos Neto, Ricardo de A. Araújo, Germano C. Vasconcelos, Paulo J. L. Adeodato, Paulo Renato Alves Firmino, Adenilton J. da Silva, Francisco Madeiro, George D. C. Cavalcanti, Christophe Chesneau and Tatijana Stošić and has published in prestigious journals such as SHILAP Revista de lepidopterología, Renewable and Sustainable Energy Reviews and PLoS ONE.

In The Last Decade

Tiago A. E. Ferreira

71 papers receiving 860 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tiago A. E. Ferreira Brazil 18 287 263 202 143 123 79 911
Germán Aneiros Spain 20 190 0.7× 328 1.2× 256 1.3× 89 0.6× 46 0.4× 49 1.2k
Hans‐Georg Müller United States 13 75 0.3× 275 1.0× 73 0.4× 70 0.5× 36 0.3× 33 1.2k
Milan Stehlík Austria 17 204 0.7× 160 0.6× 26 0.1× 120 0.8× 75 0.6× 101 821
A.P. de Weijer Netherlands 14 98 0.3× 183 0.7× 109 0.5× 26 0.2× 67 0.5× 15 1.1k
Hanita Daud Malaysia 16 71 0.2× 52 0.2× 158 0.8× 20 0.1× 80 0.7× 128 880
Mayer Alvo Canada 15 120 0.4× 102 0.4× 102 0.5× 70 0.5× 29 0.2× 57 662
Estela Bee Dagum Italy 13 164 0.6× 69 0.3× 56 0.3× 190 1.3× 19 0.2× 43 580
Geurt Jongbloed Netherlands 20 114 0.4× 212 0.8× 39 0.2× 63 0.4× 37 0.3× 79 1.4k
Taeryon Choi South Korea 11 78 0.3× 274 1.0× 25 0.1× 46 0.3× 51 0.4× 52 664

Countries citing papers authored by Tiago A. E. Ferreira

Since Specialization
Citations

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

Fields of papers citing papers by Tiago A. E. Ferreira

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tiago A. E. Ferreira. 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 Tiago A. E. Ferreira. The network helps show where Tiago A. E. Ferreira may publish in the future.

Co-authorship network of co-authors of Tiago A. E. Ferreira

This figure shows the co-authorship network connecting the top 25 collaborators of Tiago A. E. Ferreira. A scholar is included among the top collaborators of Tiago A. E. Ferreira 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 Tiago A. E. Ferreira. Tiago A. E. Ferreira 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.
Ferreira, Tiago A. E., et al.. (2024). Proposal for a new non-linear model to describe growth curves. Bioscience Journal. 40. e40011–e40011. 1 indexed citations
2.
Ferreira, Tiago A. E., et al.. (2023). Brazilian wind energy generation potential using mixtures of Weibull distributions. Renewable and Sustainable Energy Reviews. 189. 113990–113990. 21 indexed citations
3.
Silva, Antônio Samuel Alves da, et al.. (2023). Sazonalidade do regime de chuva nas mesorregiões do estado de Pernambuco, Brasil. Research Society and Development. 12(12). e29121243835–e29121243835.
4.
Ferreira, Tiago A. E., et al.. (2022). A simple light-trapping device from a hyperbolic metamaterial on a catenoid. Europhysics Letters (EPL). 137(4). 45001–45001. 2 indexed citations
5.
Stošić, Tatijana, et al.. (2021). Mixture distribution and multifractal analysis applied to wind speed in the Brazilian Northeast region. Chaos Solitons & Fractals. 144. 110651–110651. 17 indexed citations
6.
Stošić, Tatijana, et al.. (2020). Comparação dos dados da velocidade do vento no Nordeste do Brasil da ERA-40 e Instituto Nacional de Meteorologia (INMET) utilizando medidas de entropia. Research Society and Development. 9(8). e446985257–e446985257. 2 indexed citations
7.
Ferreira, Tiago A. E., et al.. (2020). Long-Term Time Prediction of Cumulative Number of Deaths in Brazil, China, Germany, Italy, Spain, the United States: an application to COVID-19 S-shaped models. Research Society and Development. 9(8). e749986565–e749986565. 4 indexed citations
8.
Fernandes, Leonardo H.S., et al.. (2020). Multifractal behavior in the dynamics of Brazilian inflation indices. Physica A Statistical Mechanics and its Applications. 550. 124158–124158. 22 indexed citations
9.
Chesneau, Christophe, et al.. (2019). On the Sin-G class of distributions: theory, model and application. 7(3). 357–379. 41 indexed citations
10.
Silva, Adenilton J. da, et al.. (2019). Electronic nose dataset for detection of wine spoilage thresholds. SHILAP Revista de lepidopterología. 25. 104202–104202. 33 indexed citations
11.
Stošić, Tatijana, et al.. (2018). Information flow between Ibovespa and constituent companies. Physica A Statistical Mechanics and its Applications. 516. 233–239. 2 indexed citations
12.
Neto, Paulo S. G. de Mattos, Tiago A. E. Ferreira, Aranildo R. Lima, Germano C. Vasconcelos, & George D. C. Cavalcanti. (2017). A perturbative approach for enhancing the performance of time series forecasting. Neural Networks. 88. 114–124. 14 indexed citations
13.
Firmino, Paulo Renato Alves, Paulo S. G. de Mattos Neto, & Tiago A. E. Ferreira. (2013). Correcting and combining time series forecasters. Neural Networks. 50. 1–11. 35 indexed citations
14.
Santos, José A. L., et al.. (2012). Unfolding neutron spectra obtained from BS–TLD system using genetic algorithm. Applied Radiation and Isotopes. 71. 81–86. 8 indexed citations
15.
Lima, Aranildo R., et al.. (2010). An experimental study of fitness function and time series forecasting using artificial neural networks. 2015–2018. 11 indexed citations
16.
Araújo, Ricardo de A. & Tiago A. E. Ferreira. (2009). A Morphological-Rank-Linear evolutionary method for stock market prediction. Information Sciences. 237. 3–17. 41 indexed citations
17.
Ferreira, Tiago A. E., et al.. (2009). Combining Artificial Neural Network and Particle Swarm System for time series forecasting. 2230–2237. 10 indexed citations
18.
Ferreira, Tiago A. E., et al.. (2008). A hybrid method for tuning neural network for financial time series forecasting. international conference on Modelling and simulation. 370–375. 1 indexed citations
19.
Lima, Aranildo R. & Tiago A. E. Ferreira. (2008). A hybrid method for tuning neural network for time series forecasting. 531–532. 2 indexed citations
20.
Ferreira, Tiago A. E., et al.. (2007). Time series forecasting with Qubit Neural Networks. 13–18. 5 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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