Razvan-Gabriel Cirstea

500 total citations
7 papers, 299 citations indexed

About

Razvan-Gabriel Cirstea is a scholar working on Signal Processing, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Razvan-Gabriel Cirstea has authored 7 papers receiving a total of 299 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Signal Processing, 6 papers in Artificial Intelligence and 2 papers in Computer Networks and Communications. Recurrent topics in Razvan-Gabriel Cirstea's work include Time Series Analysis and Forecasting (6 papers), Anomaly Detection Techniques and Applications (4 papers) and Traffic Prediction and Management Techniques (2 papers). Razvan-Gabriel Cirstea is often cited by papers focused on Time Series Analysis and Forecasting (6 papers), Anomaly Detection Techniques and Applications (4 papers) and Traffic Prediction and Management Techniques (2 papers). Razvan-Gabriel Cirstea collaborates with scholars based in Denmark, Australia and United States. Razvan-Gabriel Cirstea's co-authors include Chenjuan Guo, Bin Yang, Tung Kieu, Shirui Pan, Sinno Jialin Pan, Xuanyi Dong, Yan Zhao, Christian S. Jensen and Yale Song and has published in prestigious journals such as arXiv (Cornell University), VBN Forskningsportal (Aalborg Universitet) and 2022 IEEE 38th International Conference on Data Engineering (ICDE).

In The Last Decade

Razvan-Gabriel Cirstea

7 papers receiving 294 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Razvan-Gabriel Cirstea Denmark 6 166 125 104 63 59 7 299
Jinliang Deng China 9 144 0.9× 214 1.7× 117 1.1× 51 0.8× 114 1.9× 18 371
Xiusi Chen United States 8 82 0.5× 93 0.7× 101 1.0× 41 0.7× 45 0.8× 22 264
Xuehan Wu China 7 84 0.5× 68 0.5× 123 1.2× 42 0.7× 32 0.5× 15 342
Xiaojie Feng China 7 71 0.4× 190 1.5× 90 0.9× 21 0.3× 134 2.3× 12 368
Şefki Kolozali United Kingdom 8 81 0.5× 40 0.3× 100 1.0× 17 0.3× 52 0.9× 20 339
Zulong Diao China 7 85 0.5× 230 1.8× 121 1.2× 12 0.2× 189 3.2× 17 407
Haomin Wen China 9 67 0.4× 149 1.2× 36 0.3× 9 0.1× 89 1.5× 23 238
Dongjie Wang United States 12 42 0.3× 34 0.3× 208 2.0× 33 0.5× 48 0.8× 35 356
Shouxu Jiang China 7 60 0.4× 28 0.2× 80 0.8× 30 0.5× 65 1.1× 27 374
Yuntao Du China 5 51 0.3× 28 0.2× 113 1.1× 29 0.5× 10 0.2× 14 224

Countries citing papers authored by Razvan-Gabriel Cirstea

Since Specialization
Citations

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

Fields of papers citing papers by Razvan-Gabriel Cirstea

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Razvan-Gabriel Cirstea. 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 Razvan-Gabriel Cirstea. The network helps show where Razvan-Gabriel Cirstea may publish in the future.

Co-authorship network of co-authors of Razvan-Gabriel Cirstea

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

All Works

7 of 7 papers shown
1.
Kieu, Tung, Bin Yang, Chenjuan Guo, et al.. (2022). Anomaly Detection in Time Series with Robust Variational Quasi-Recurrent Autoencoders. 2022 IEEE 38th International Conference on Data Engineering (ICDE). 1342–1354. 32 indexed citations
2.
Cirstea, Razvan-Gabriel, Bin Yang, Chenjuan Guo, Tung Kieu, & Shirui Pan. (2022). Towards Spatio- Temporal Aware Traffic Time Series Forecasting. 2022 IEEE 38th International Conference on Data Engineering (ICDE). 2900–2913. 73 indexed citations
3.
Cirstea, Razvan-Gabriel, Chenjuan Guo, Bin Yang, et al.. (2022). Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. 1994–2001. 61 indexed citations
4.
Cirstea, Razvan-Gabriel, Chenjuan Guo, & Bin Yang. (2021). Graph Attention Recurrent Neural Networks for Correlated Time Series Forecasting. arXiv (Cornell University). 11 indexed citations
5.
Cirstea, Razvan-Gabriel, Tung Kieu, Chenjuan Guo, Bin Yang, & Sinno Jialin Pan. (2021). EnhanceNet: Plugin Neural Networks for Enhancing Correlated Time Series Forecasting. VBN Forskningsportal (Aalborg Universitet). 1739–1750. 58 indexed citations
6.
Cirstea, Razvan-Gabriel, et al.. (2020). A Road Segment Attribute Completion System. VBN Forskningsportal (Aalborg Universitet). 236–237. 2 indexed citations
7.
Cirstea, Razvan-Gabriel, et al.. (2018). Correlated Time Series Forecasting using Multi-Task Deep Neural Networks. VBN Forskningsportal (Aalborg Universitet). 1527–1530. 62 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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