Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Donnat, Philippe"'
Publikováno v:
Proceedings - International Conference on Time Series and Forecasting, ITISE 2018. Granada: University of Granada, pp. 1178-1192
Targeting a better understanding of credit market dynamics, the authors have studied a stochastic model named the Hawkes process. Describing trades arrival times, this kind of model allows for the capture of self-excitement and mutual interactions ph
Externí odkaz:
http://arxiv.org/abs/1902.03714
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 number grows, it becomes necessary to use an algorithm to extract text and
Externí odkaz:
http://arxiv.org/abs/1708.04120
We propose Significance-Offset Convolutional Neural Network, a deep convolutional network architecture for regression of multivariate asynchronous time series. The model is inspired by standard autoregressive (AR) models and gating mechanisms used in
Externí odkaz:
http://arxiv.org/abs/1703.04122
Publikováno v:
Chapter in Progress in Information Geometry: Theory and Applications, 245-274, 2021
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 one place the relevant material from different fields, e.g. machine le
Externí odkaz:
http://arxiv.org/abs/1703.00485
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 between two variables, state-of-the-art optimal transport for providing a r
Externí odkaz:
http://arxiv.org/abs/1610.09659
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 methodology leverages copulas which are distributions encoding the dependence s
Externí odkaz:
http://arxiv.org/abs/1604.08634
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 and signal processing. This workshop brought several experts in pure an
Externí odkaz:
http://arxiv.org/abs/1603.07822
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 statistical framework to study the validity of such practices. We first show th
Externí odkaz:
http://arxiv.org/abs/1603.04017
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 a multivariate time series, and (ii) inter-dependence between two time
Externí odkaz:
http://arxiv.org/abs/1509.08144
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 by providing through the data perturbations we propose a multi-view of
Externí odkaz:
http://arxiv.org/abs/1509.05475