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arXiv · 1506.09163

Comment partitionner automatiquement des marches aléatoires ? Avec application à la finance quantitative

Gautier Marti·Frank Nielsen·Philippe Very·Philippe Donnat
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Abstract

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 and identically distributed samples from random variables to a generic non-parametric representation which factorizes dependency and marginal distribution apart without losing any. An associated metric is defined where the balance between random variables dependency and distribution information is controlled by a single parameter. This mixing parameter can be learned or played with by a practitioner, such use is illustrated on the case of clustering financial time series. Experiments, implementation and results obtained on public financial time series are online on a web portal \url{http://www.datagrapple.com}.

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Gautier Marti, Frank Nielsen, Philippe Very, Philippe Donnat. 2015-06-30. Comment partitionner automatiquement des marches aléatoires ? Avec application à la finance quantitative. https://arxiv.org/abs/1506.09163

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