International audienceResearchers 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 that clustering correlated random variables from their observed values is statistically consistent. Then, we also give a first empirical answer to the much debated question: How long should the time series be? If too short, the clusters found can be spurious; if too long, dynamics can be smoothed out
We consider the problem of determining whether the community structure found by a clustering algorit...
National audienceNowadays, a huge amount of applications exist that natively adopt a data-streaming ...
In this work we discuss the clustering procedure of time series of financial returns in groups being...
International audienceResearchers have used from 30 days to several years of daily returns as source...
This article introduces a new procedure for clustering a large number of financial time series based...
A methodology is presented for clustering financial time series according to the association in the ...
Data clustering is one of the most popular unsupervised machine learning approaches. Clustering dat...
We discuss two methods for clustering financial time series in extreme scenarios. The procedures are...
The current ability to produce massive amounts of data and the impossibility in storing it motivated...
AbstractIn this article I present a dynamic clustering algorithm applied on financial time series da...
Time series clustering is heavily based on choosing a proper dissimilarity measure between a pair of...
The thesis is composed of three parts. Part I introduces the mathematical and statistical tools that...
One of the main problems in modelling multivariate conditional covariance time series is the paramet...
We review a correlation based clustering procedure applied to a portfolio of assets synchronously tr...
In this paper we propose a clustering procedure aimed at grouping time series with an association be...
We consider the problem of determining whether the community structure found by a clustering algorit...
National audienceNowadays, a huge amount of applications exist that natively adopt a data-streaming ...
In this work we discuss the clustering procedure of time series of financial returns in groups being...
International audienceResearchers have used from 30 days to several years of daily returns as source...
This article introduces a new procedure for clustering a large number of financial time series based...
A methodology is presented for clustering financial time series according to the association in the ...
Data clustering is one of the most popular unsupervised machine learning approaches. Clustering dat...
We discuss two methods for clustering financial time series in extreme scenarios. The procedures are...
The current ability to produce massive amounts of data and the impossibility in storing it motivated...
AbstractIn this article I present a dynamic clustering algorithm applied on financial time series da...
Time series clustering is heavily based on choosing a proper dissimilarity measure between a pair of...
The thesis is composed of three parts. Part I introduces the mathematical and statistical tools that...
One of the main problems in modelling multivariate conditional covariance time series is the paramet...
We review a correlation based clustering procedure applied to a portfolio of assets synchronously tr...
In this paper we propose a clustering procedure aimed at grouping time series with an association be...
We consider the problem of determining whether the community structure found by a clustering algorit...
National audienceNowadays, a huge amount of applications exist that natively adopt a data-streaming ...
In this work we discuss the clustering procedure of time series of financial returns in groups being...