François-Xavier Aubet
- Computer Networks and Communications top 10%
- Artificial Intelligence top 10%
- Signal Processing top 5%
- Electrical and Electronic Engineering
- Management Science and Operations Research top 10%
- Co-authors
- Marc‐Oliver PahlJan GasthausValentín FlunkertLaurent CallotLorenzo StellaKonstantinos BenidisTim JanuschowskiMichael Schneider
- Topics
- Software System Performance and Reliability (2 papers)Anomaly Detection Techniques and Applications (2 papers)Time Series Analysis and Forecasting (2 papers)
- Journals
- ACM Computing SurveysProceedings of the Thirty-First International Joint Conference on Artificial Intelligence
- Partner nations
- Germany
In The Last Decade
François-Xavier Aubet
5 papers receiving 297 citations
Hit Papers
Peers
Comparison fields: 5 of 65
- Computer Networks and Communications 147
- Artificial Intelligence 145
- Signal Processing 124
- Electrical and Electronic Engineering 56
- Management Science and Operations Research 51
Countries citing papers authored by François-Xavier Aubet
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
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
| # | Work | Indexed citations |
|---|---|---|
| 1 | Deep Learning for Time Series Forecasting: Tutorial and Literature Surveybreakdown → | 150 |
| 2 | 41 | |
| 3 | 8 | |
| 4 | All Eyes on You: Distributed Multi-Dimensional IoT Microservice Anomaly Detection | 79 |
| 5 | 31 |
About François-Xavier Aubet
François-Xavier Aubet is a scholar working on Signal Processing, Computer Networks and Communications and Management Science and Operations Research, having authored 5 papers that have together received 309 indexed citations. Recurring topics across this work include Software System Performance and Reliability (2 papers), Anomaly Detection Techniques and Applications (2 papers) and Time Series Analysis and Forecasting (2 papers). The work is most often cited by research in Signal Processing (124 citations), Computer Networks and Communications (147 citations) and Artificial Intelligence (145 citations). François-Xavier Aubet has collaborated with scholars based in Germany. Frequent co-authors include Marc‐Oliver Pahl, Jan Gasthaus, Valentín Flunkert, Laurent Callot, Lorenzo Stella, Konstantinos Benidis, Tim Januschowski, Michael Schneider, David Salinas and Syama Sundar Rangapuram. Their work appears in journals such as ACM Computing Surveys and Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence.
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.