Laurent Callot

1.0k citations
18 papers · 450 indexed · 1 hit paper · h-index 8

Laurent Callot

18 papers receiving 424 citations

Hit Papers

Deep Learning for Time Series Forecasting: Tutorial and L...150202220262023202450100150

Peers

Laurent Callot
Comparison fields: 5 of 84
  • Management Science and Operations Research 187
  • Signal Processing 109
  • Finance 92
  • General Economics, Econometrics and Finance 52
  • Statistics and Probability 35
Replace Bonsoo Koo with:
Bonsoo Koo Australia
Leonard J. Tashman United States
Peter Nystrup Denmark
Guillaume Chevillon France
Francesco Lisi Italy
Olaf Korn Germany
Gholam Ali Raissi Ardali Iran
Tomáš Cipra Czechia
Yuping Song China
Xuan Huang China
Laurent Callot relative to Bonsoo Koo Australia Bonsoo Koo's profile →
Citations per field
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Bonsoo Koo · 1×
Citations per year

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

The 20 scholars most cited alongside Laurent Callot, 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 Laurent Callot Line = papers co-authored together Laurent Callot links everyone, so they are left out of the graph.

All Works

18 of 18 papers shown
#Work
1 20241
2
Deep Learning for Time Series Forecasting: Tutorial and Literature Surveybreakdown →
2022150
3 20212
4
A Simple and Effective Predictive Resource Scaling Heuristic for Large-scale Cloud Applications.
20201
5 201916
6 2019117
7 201923
8
Deep Learning for Forecasting: Current Trends and Challenges
201812
9
Deep Learning for Forecasting
20183
10 201663
11 201510
12 20157
13 20144
14 20141
15 201232
16 20115
17
A Bootstrap Cointegration Rank Test for Panels of VAR Models
20101
18 20102

About Laurent Callot

Laurent Callot is a scholar working on Management Science and Operations Research, General Decision Sciences and Finance, having authored 18 papers that have together received 450 indexed citations. Recurring topics across this work include Forecasting Techniques and Applications (8 papers), Time Series Analysis and Forecasting (4 papers), Financial Risk and Volatility Modeling (3 papers), Stock Market Forecasting Methods (3 papers), Statistical Methods and Inference (3 papers), Monetary Policy and Economic Impact (3 papers), Advanced Statistical Methods and Models (2 papers) and Italy: Economic History and Contemporary Issues (2 papers). The work is most often cited by research in Management Science and Operations Research (187 citations), Signal Processing (109 citations) and Finance (92 citations). Laurent Callot has collaborated with scholars based in Denmark, Netherlands and United States. Frequent 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. Their work appears in journals such as ACM Computing Surveys, Journal of Business and Economic Statistics and International Journal of Forecasting.

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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2026