Mathieu Sinn

32 papers receiving 509 citations

Peers

Mathieu Sinn
Comparison fields: 5 of 88
  • Statistical and Nonlinear Physics 139
  • Signal Processing 86
  • Transportation 46
  • Economics and Econometrics 149
  • Building and Construction 74
Replace Giacomo Domeniconi with:
Giacomo Domeniconi Italy
Pedro Ribeiro Portugal
Xingyu Zhou China
Snehanshu Saha India
Francisco J. R. Ruiz United States
N. Sarshar Iran
Yu Lu China
Geoffrey Yeo Australia
Masahiro Takatsuka Australia
Mathieu Sinn relative to Giacomo Domeniconi Italy Giacomo Domeniconi's profile →
Citations per field
00.5×10×13.5×
Giacomo Domeniconi · 1×
Citations per year

Countries citing papers authored by Mathieu Sinn

Since Specialization
Citations

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

Fields of papers citing papers by Mathieu Sinn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mathieu Sinn. 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 Mathieu Sinn. The network helps show where Mathieu Sinn may publish in the future.

Co-authors

The 25 scholars most cited alongside Mathieu Sinn, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Mathieu Sinn Line = papers co-authored together Mathieu Sinn links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 32 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200598
2 201158
3 200745
4 201242
5 201342
6 201039
7 201033
8 201324
9
Adaptive Learning of Smoothing Functions: Application to Electricity Load Forecasting
201223
10 200917
11 201312
12 201411
13
Forecasting Uncertainty in Electricity Demand
201510
14 201110
15 20169
16 20137
17 20127
18
Asymptotic Theory for Linear-Chain Conditional Random Fields
20116
19 20126
20 20165

About Mathieu Sinn

Mathieu Sinn is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering, Economics and Econometrics, Statistical and Nonlinear Physics and Computer Vision and Pattern Recognition, having authored 32 papers that have together received 533 indexed citations. Recurring topics across this work include Energy Load and Power Forecasting (10 papers), Complex Systems and Time Series Analysis (8 papers), Chaos control and synchronization (6 papers), Time Series Analysis and Forecasting (5 papers), Anomaly Detection Techniques and Applications (4 papers), Context-Aware Activity Recognition Systems (3 papers), Financial Risk and Volatility Modeling (3 papers) and Bayesian Modeling and Causal Inference (3 papers). The work is most often cited by research in Statistical and Nonlinear Physics (139 citations), Signal Processing (86 citations), Transportation (46 citations), Economics and Econometrics (149 citations) and Building and Construction (74 citations). Mathieu Sinn has collaborated with scholars based in Ireland, United States and Canada. Frequent co-authors include Karsten Keller, Carlos Alzate, Francesco Calabrese, Johannes Textor, Ji Won Yoon, Eric Bouillet, Ulrich H. von Andrian, António Peixoto, Sarah E. Henrickson and Jürgen Westermann. Their work appears in journals such as IBM Journal of Research and Development, IEEE Transactions on Knowledge and Data Engineering, Physica A Statistical Mechanics and its Applications, Stochastics and Dynamics and Proceedings of the National Academy of 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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