fév. 2025
Intervenant : | Rémi Vaucher |
Institution : | Université Lyon 2 et Halias Technologies |
Heure : | 11h00 - 12h00 |
Lieu : | 2L8 |
The theory of signatures, developed by K.T. Chen in the 1950s, studies the geometry of paths through iterations of the Stieltjes integral. This tool, originally rooted in pure differential geometry, was later introduced into probability theory and subsequently into machine learning by Terry Lyons.
Initially used in rough path theory, it gained some recognition for its application in extracting geometric features for machine learning, reaching outstanding results.
In this presentation, we will first examine the signature of a rough path and then explore how its remarkable properties can be leveraged to construct a simplicial complex that reflects explainability within a family of time series signals. Initially applied to univariate signals, we will see how this method can be extended to more complex data, such as multivariate signals with non-homogeneous dimensions.