François-Xavier Aubet

1.4k total citations · 1 hit paper
5 papers, 309 citations indexed

About

François-Xavier Aubet is a scholar working on Computer Networks and Communications, Signal Processing and Artificial Intelligence. According to data from OpenAlex, François-Xavier Aubet has authored 5 papers receiving a total of 309 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Computer Networks and Communications, 2 papers in Signal Processing and 2 papers in Artificial Intelligence. Recurrent topics in François-Xavier Aubet's work include Software System Performance and Reliability (2 papers), Anomaly Detection Techniques and Applications (2 papers) and Time Series Analysis and Forecasting (2 papers). François-Xavier Aubet is often cited by papers focused on Software System Performance and Reliability (2 papers), Anomaly Detection Techniques and Applications (2 papers) and Time Series Analysis and Forecasting (2 papers). François-Xavier Aubet collaborates with scholars based in Germany. François-Xavier Aubet's co-authors include Marc‐Oliver Pahl, Valentín Flunkert, Jan Gasthaus, Laurent Callot, Danielle C. Maddix, Michael Schneider, Tim Januschowski, Lorenzo Stella, Konstantinos Benidis and David Salinas and has published in prestigious journals such as ACM Computing Surveys and Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence.

In The Last Decade

François-Xavier Aubet

5 papers receiving 297 citations

Hit Papers

align trajectories sort by overperformance

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: Tutorial and Literature Survey

2022 150 citations Konstantinos Benidis, Syama Sundar Rangapuram et al. ACM Computing Surveys profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
François-Xavier Aubet Germany	5	147	145	124	56	51	5	309
Manjula C. Belavagi India	4	188 1.3×	171 1.2×	125 1.0×	39 0.7×	41 0.8×	10	321
Mehmet Demirci Türkiye	10	233 1.6×	98 0.7×	68 0.5×	97 1.7×	16 0.3×	38	378
Bhoopesh Singh Bhati India	10	210 1.4×	220 1.5×	105 0.8×	20 0.4×	15 0.3×	34	415
Osama M. Elkomy Egypt	10	128 0.9×	139 1.0×	42 0.3×	49 0.9×	10 0.2×	18	332
Sudhakar Kumar India	11	78 0.5×	66 0.5×	43 0.3×	46 0.8×	16 0.3×	43	256
M. Durairaj India	8	65 0.4×	62 0.4×	33 0.3×	44 0.8×	54 1.1×	27	230
Şefki Kolozali United Kingdom	8	108 0.7×	100 0.7×	81 0.7×	35 0.6×	17 0.3×	20	339
Xuhang Ying United States	11	164 1.1×	80 0.6×	41 0.3×	209 3.7×	17 0.3×	15	365
Mirza Mohd Shahriar Maswood Bangladesh	8	179 1.2×	96 0.7×	39 0.3×	126 2.3×	106 2.1×	27	434
Huajian Mao China	8	127 0.9×	146 1.0×	98 0.8×	29 0.5×	19 0.4×	20	350

Countries citing papers authored by François-Xavier Aubet

Since Specialization

Citations

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

Fields of papers citing papers by François-Xavier Aubet

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by François-Xavier Aubet. 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 François-Xavier Aubet. The network helps show where François-Xavier Aubet may publish in the future.

Co-authorship network of co-authors of François-Xavier Aubet

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

All Works

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5 of 5 papers shown

1.

Benidis, Konstantinos, Syama Sundar Rangapuram, Valentín Flunkert, et al.. (2022). Deep Learning for Time Series Forecasting: Tutorial and Literature Survey. ACM Computing Surveys. 55(6). 1–36. 150 indexed citations breakdown →

2.

Aubet, François-Xavier, et al.. (2022). Neural Contextual Anomaly Detection for Time Series. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. 2843–2851. 41 indexed citations

3.

Aubet, François-Xavier, et al.. (2019). Deep Learning-aided Resource Orchestration for Vehicular Safety Communication. 1–8. 8 indexed citations

4.

Pahl, Marc‐Oliver & François-Xavier Aubet. (2018). All Eyes on You: Distributed Multi-Dimensional IoT Microservice Anomaly Detection. 72–80. 79 indexed citations

5.

Pahl, Marc‐Oliver, et al.. (2018). Graph-based IoT microservice security. 1–3. 31 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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