Mathieu Sinn

35 papers receiving 568 citations

Peers

Mathieu Sinn
Comparison fields: 5 of 89
  • Statistical and Nonlinear Physics 144
  • Signal Processing 95
  • Transportation 53
  • Building and Construction 84
  • Economics and Econometrics 151
Replace Giacomo Domeniconi with:
Giacomo Domeniconi Italy
Xingyu Zhou China
Pedro Ribeiro Portugal
Cun‐Quan Zhang United States
Yu Lu China
Francisco J. R. Ruiz United States
G. Mukherjee India
Gengxin Sun China
Geoffrey Yeo Australia
Snehanshu Saha India
Mathieu Sinn relative to Giacomo Domeniconi Italy Giacomo Domeniconi's profile →
Citations per field
00.5×6.6×
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 37 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2005101
2 201161
3 201348
4 201246
5 200745
6 201039
7 201033
8 201329
9
Adaptive Learning of Smoothing Functions: Application to Electricity Load Forecasting
201225
10 200917
11 201312
12 201412
13 202212
14
Forecasting Uncertainty in Electricity Demand
201511
15 201110
16 20169
17 20219
18 20128
19 20137
20 20157

About Mathieu Sinn

Mathieu Sinn is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering, Economics and Econometrics, Computer Vision and Pattern Recognition and Statistical and Nonlinear Physics, having authored 37 papers that have together received 594 indexed citations. Recurring topics across this work include Energy Load and Power Forecasting (11 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), Smart Grid Energy Management (4 papers), Transportation Planning and Optimization (3 papers) and Bayesian Methods and Mixture Models (3 papers). The work is most often cited by research in Statistical and Nonlinear Physics (144 citations), Signal Processing (95 citations), Transportation (53 citations), Building and Construction (84 citations) and Economics and Econometrics (151 citations). Mathieu Sinn has collaborated with scholars based in Ireland, United States and Germany. Frequent co-authors include Karsten Keller, Carlos Alzate, Francesco Calabrese, Johannes Textor, Ji Won Yoon, Eric Bouillet, Sarah E. Henrickson, Jürgen Westermann, Ulrich H. von Andrian and António Peixoto. Their work appears in journals such as IBM Journal of Research and Development, The European Physical Journal Special Topics, Stochastics and Dynamics, IEEE Transactions on Knowledge and Data Engineering and Physica A Statistical Mechanics and its Applications.

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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