Laurent Callot

1.0k total citations · 1 hit paper
18 papers, 450 citations indexed

About

Laurent Callot is a scholar working on Management Science and Operations Research, Finance and Signal Processing. According to data from OpenAlex, Laurent Callot has authored 18 papers receiving a total of 450 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Management Science and Operations Research, 4 papers in Finance and 4 papers in Signal Processing. Recurrent topics in Laurent Callot's work include Forecasting Techniques and Applications (8 papers), Time Series Analysis and Forecasting (4 papers) and Financial Risk and Volatility Modeling (3 papers). Laurent Callot is often cited by papers focused on Forecasting Techniques and Applications (8 papers), Time Series Analysis and Forecasting (4 papers) and Financial Risk and Volatility Modeling (3 papers). Laurent Callot collaborates with scholars based in Denmark, Netherlands and United States. Laurent Callot's co-authors include Anders Kock, Jan Gasthaus, David Salinas, Tim Januschowski, Valentín Flunkert, Marcelo C. Medeiros, Michael Bohlke‐Schneider, Yuyang Wang, Syama Sundar Rangapuram and Lorenzo Stella and has published in prestigious journals such as ACM Computing Surveys, Journal of Business and Economic Statistics and International Journal of Forecasting.

In The Last Decade

Laurent Callot

18 papers receiving 424 citations

Hit Papers

align trajectories

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
Laurent Callot Denmark	8	187	115	109	92	78	18	450
Bonsoo Koo Australia	8	138 0.7×	101 0.9×	34 0.3×	68 0.7×	63 0.8×	24	466
Leonard J. Tashman United States	6	304 1.6×	140 1.2×	54 0.5×	44 0.5×	104 1.3×	8	598
Peter Nystrup Denmark	12	125 0.7×	124 1.1×	21 0.2×	128 1.4×	133 1.7×	25	370
Guillaume Chevillon France	9	112 0.6×	193 1.7×	25 0.2×	100 1.1×	47 0.6×	33	356
Gholam Ali Raissi Ardali Iran	7	207 1.1×	52 0.5×	33 0.3×	17 0.2×	133 1.7×	9	362
Yuping Song China	9	143 0.8×	123 1.1×	21 0.2×	95 1.0×	77 1.0×	37	294
Olaf Korn Germany	12	162 0.9×	291 2.5×	26 0.2×	359 3.9×	52 0.7×	47	720
Dadabada Pradeepkumar India	4	196 1.0×	94 0.8×	34 0.3×	33 0.4×	101 1.3×	6	317
Tomáš Cipra Czechia	9	79 0.4×	42 0.4×	30 0.3×	36 0.4×	25 0.3×	51	281
A.N. Refenes United Kingdom	12	239 1.3×	205 1.8×	36 0.3×	152 1.7×	61 0.8×	27	503

Countries citing papers authored by Laurent Callot

Since Specialization

Citations

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

Fields of papers citing papers by Laurent Callot

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Laurent Callot

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

All Works

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18 of 18 papers shown

Callot, Laurent, et al.. (2024). Textual Dataset Distillation via Language Model Embedding. 12557–12569. 1 indexed citations

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 →

Januschowski, Tim, et al.. (2021). Online false discovery rate control for anomaly detection in time series. arXiv (Cornell University). 34. 2 indexed citations

Flunkert, Valentín, et al.. (2020). A Simple and Effective Predictive Resource Scaling Heuristic for Large-scale Cloud Applications.. Very Large Data Bases. 1 indexed citations

Callot, Laurent, et al.. (2019). A Nodewise Regression Approach to Estimating Large Portfolios. Journal of Business and Economic Statistics. 39(2). 520–531. 16 indexed citations

Januschowski, Tim, Jan Gasthaus, Yuyang Wang, et al.. (2019). Criteria for classifying forecasting methods. International Journal of Forecasting. 36(1). 167–177. 117 indexed citations

Salinas, David, et al.. (2019). High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes. arXiv (Cornell University). 32. 6824–6834. 23 indexed citations

Januschowski, Tim, Jan Gasthaus, Yuyang Wang, Syama Sundar Rangapuram, & Laurent Callot. (2018). Deep Learning for Forecasting: Current Trends and Challenges. RePEc: Research Papers in Economics. 42–47. 12 indexed citations

Januschowski, Tim, Jan Gasthaus, Syama Sundar Rangapuram, & Laurent Callot. (2018). Deep Learning for Forecasting. RePEc: Research Papers in Economics. 35–41. 3 indexed citations

10.

Callot, Laurent, Anders Kock, & Marcelo C. Medeiros. (2016). Modeling and Forecasting Large Realized Covariance Matrices and Portfolio Choice. Journal of Applied Econometrics. 32(1). 140–158. 63 indexed citations

11.

Callot, Laurent, Niels Haldrup, & Malene Kallestrup‐Lamb. (2015). Deterministic and stochastic trends in the Lee–Carter mortality model. Applied Economics Letters. 23(7). 486–493. 10 indexed citations

12.

Callot, Laurent, et al.. (2015). Sharp Threshold Detection Based on Sup-Norm Error Rates in High-Dimensional Models. Journal of Business and Economic Statistics. 35(2). 250–264. 7 indexed citations

13.

Callot, Laurent, Anders Kock, & Marcelo C. Medeiros. (2014). Estimation and Forecasting of Large Realized Covariance Matrices and Portfolio Choice. SSRN Electronic Journal. 4 indexed citations

14.

Callot, Laurent, et al.. (2014). Vector Autoregressions with Parsimoniously Time Varying Parameters and an Application to Monetary Policy. SSRN Electronic Journal. 1 indexed citations

15.

Kock, Anders & Laurent Callot. (2012). Oracle Inequalities for High Dimensional Vector Autoregressions. SSRN Electronic Journal. 32 indexed citations

16.

Callot, Laurent & Martín Paldam. (2011). The problem of natural funnel asymmetries: a simulation analysis of meta‐analysis in macroeconomics. Research Synthesis Methods. 2(2). 84–102. 5 indexed citations

17.

Callot, Laurent. (2010). A Bootstrap Cointegration Rank Test for Panels of VAR Models. SSRN Electronic Journal. 1 indexed citations

18.

Callot, Laurent & Martín Paldam. (2010). Natural Funnel Asymmetries: A Simulation Analysis of the Three Basic Tools of Meta Analysis. SSRN Electronic Journal. 2 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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