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...
The final publication is available at Springer via DOI 10.1007/s10614-012-9327-x with the title: A G...
In this thesis we first review the scattered literature about clustering financial time series. We t...
International audienceResearchers have used from 30 days to several years of daily returns as source...
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...
Data clustering is one of the most popular unsupervised machine learning approaches. Clustering dat...
National audienceNowadays, a huge amount of applications exist that natively adopt a data-streaming ...
International audienceThe problem of clustering is considered for the case where every point is a ti...
Includes bibliographical references.Cluster analysis is becoming an increasingly popular method in m...
The current ability to produce massive amounts of data and the impossibility in storing it motivated...
The thesis is composed of three parts. Part I introduces the mathematical and statistical tools that...
International audienceNowadays, a huge amount of applications exist that natively adopt a data-strea...
Clustering is an essential branch of data mining and statistical analysis that could help us explore...
Forecasting large numbers of time series is a costly and time-consuming exercise. Before forecasting...
We consider the problem of determining whether the community structure found by a clustering algorit...
The final publication is available at Springer via DOI 10.1007/s10614-012-9327-x with the title: A G...
In this thesis we first review the scattered literature about clustering financial time series. We t...
International audienceResearchers have used from 30 days to several years of daily returns as source...
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...
Data clustering is one of the most popular unsupervised machine learning approaches. Clustering dat...
National audienceNowadays, a huge amount of applications exist that natively adopt a data-streaming ...
International audienceThe problem of clustering is considered for the case where every point is a ti...
Includes bibliographical references.Cluster analysis is becoming an increasingly popular method in m...
The current ability to produce massive amounts of data and the impossibility in storing it motivated...
The thesis is composed of three parts. Part I introduces the mathematical and statistical tools that...
International audienceNowadays, a huge amount of applications exist that natively adopt a data-strea...
Clustering is an essential branch of data mining and statistical analysis that could help us explore...
Forecasting large numbers of time series is a costly and time-consuming exercise. Before forecasting...
We consider the problem of determining whether the community structure found by a clustering algorit...
The final publication is available at Springer via DOI 10.1007/s10614-012-9327-x with the title: A G...
In this thesis we first review the scattered literature about clustering financial time series. We t...