Search Results for author:
Found 8 papers, 2 papers with code
Putting Self-Supervised Token Embedding on the Tables
Information distribution by electronic messages is a privileged means of transmission for many businesses and individuals, often under the form of plain-text tables.
Autoregressive Convolutional Neural Networks for Asynchronous Time Series
We propose Significance-Offset Convolutional Neural Network, a deep convolutional network architecture for regression of multivariate asynchronous time series.
A review of two decades of correlations, hierarchies, networks and clustering in financial markets
We review the state of the art of clustering financial time series and the study of their correlations alongside other interaction networks.
Exploring and measuring non-linear correlations: Copulas, Lightspeed Transportation and Clustering
We propose a methodology to explore and measure the pairwise correlations that exist between variables in a dataset.
Optimal Transport vs. Fisher-Rao distance between Copulas for Clustering Multivariate Time Series
This clustering methodology leverages copulas which are distributions encoding the dependence structure between several random variables.
Clustering Financial Time Series: How Long is Enough?
Researchers have used from 30 days to several years of daily returns as source data for clustering financial time series based on their correlations.
Optimal Copula Transport for Clustering Multivariate Time Series
This paper presents a new methodology for clustering multivariate time series leveraging optimal transport between copulas.
Toward a generic representation of random variables for machine learning
This paper presents a pre-processing and a distance which improve the performance of machine learning algorithms working on independent and identically distributed stochastic processes.
