84 citations · 198 across the 12 of their papers we have counts for
15 papers
cCorrGAN: Conditional Correlation GAN for Learning Empirical Conditional Distributions in the Elliptope
Gautier Marti, Victor Goubet, Frank Nielsen
We propose a methodology to approximate conditional distributions in the elliptope of correlation matrices based on conditional generative adversarial networks. We illustrate the m…
Clustering patterns connecting COVID-19 dynamics and Human mobility using optimal transport
Frank Nielsen, Gautier Marti, Sumanta Ray +1
Social distancing and stay-at-home are among the few measures that are known to be effective in checking the spread of a pandemic such as COVID-19 in a given population. The patter…
CorrGAN: Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networks
Gautier Marti
We propose a novel approach for sampling realistic financial correlation matrices. This approach is based on generative adversarial networks. Experiments demonstrate that generativ…
Putting Self-Supervised Token Embedding on the Tables
Marc Szafraniec, Gautier Marti, Philippe Donnat
Information distribution by electronic messages is a privileged means of transmission for many businesses and individuals, often under the form of plain-text tables. As their numbe…
Autoregressive Convolutional Neural Networks for Asynchronous Time Series
Mikołaj Bińkowski, Gautier Marti, Philippe Donnat
We propose Significance-Offset Convolutional Neural Network, a deep convolutional network architecture for regression of multivariate asynchronous time series. The model is inspire…
A review of two decades of correlations, hierarchies, networks and clustering in financial markets
Gautier Marti, Frank Nielsen, Mikołaj Bińkowski +1
We review the state of the art of clustering financial time series and the study of their correlations alongside other interaction networks. The aim of this review is to gather in…
Exploring and measuring non-linear correlations: Copulas, Lightspeed Transportation and Clustering
Gautier Marti, Sebastien Andler, Frank Nielsen +1
We propose a methodology to explore and measure the pairwise correlations that exist between variables in a dataset. The methodology leverages copulas for encoding dependence betwe…
Clustering Financial Time Series: How Long is Enough?
Gautier Marti, Sébastien Andler, Frank Nielsen +1
Researchers have used from 30 days to several years of daily returns as source data for clustering financial time series based on their correlations. This paper sets up a statistic…
Optimal Transport vs. Fisher-Rao distance between Copulas for Clustering Multivariate Time Series
Gautier Marti, Sébastien Andler, Frank Nielsen +1
We present a methodology for clustering N objects which are described by multivariate time series, i.e. several sequences of real-valued random variables. This clustering methodolo…
On clustering financial time series: a need for distances between dependent random variables
Gautier Marti, Frank Nielsen, Philippe Donnat +1
The following working document summarizes our work on the clustering of financial time series. It was written for a workshop on information geometry and its application for image a…
A proposal of a methodological framework with experimental guidelines to investigate clustering stability on financial time series
Gautier Marti, Philippe Very, Philippe Donnat +1
We present in this paper an empirical framework motivated by the practitioner point of view on stability. The goal is to both assess clustering validity and yield market insights b…
Optimal Copula Transport for Clustering Multivariate Time Series
Gautier Marti, Frank Nielsen, Philippe Donnat
This paper presents a new methodology for clustering multivariate time series leveraging optimal transport between copulas. Copulas are used to encode both (i) intra-dependence of…
HCMapper: An interactive visualization tool to compare partition-based flat clustering extracted from pairs of dendrograms
Gautier Marti, Philippe Donnat, Frank Nielsen +1
We describe a new visualization tool, dubbed HCMapper, that visually helps to compare a pair of dendrograms computed on the same dataset by displaying multiscale partition-based la…
Comment partitionner automatiquement des marches aléatoires ? Avec application à la finance quantitative
Gautier Marti, Frank Nielsen, Philippe Very +1
We present in this paper a novel non-parametric approach useful for clustering Markov processes. We introduce a pre-processing step consisting in mapping multivariate independent a…
Toward a generic representation of random variables for machine learning
Gautier Marti, Philippe Very, Philippe Donnat
This paper presents a pre-processing and a distance which improve the performance of machine learning algorithms working on independent and identically distributed stochastic proce…