55,914 research outputs found
Toward a generic representation of random variables for machine learning
This paper presents a pre-processing and a distance which improve the
performance of machine learning algorithms working on independent and
identically distributed stochastic processes. We introduce a novel
non-parametric approach to represent random variables which splits apart
dependency and distribution without losing any information. We also propound an
associated metric leveraging this representation and its statistical estimate.
Besides experiments on synthetic datasets, the benefits of our contribution is
illustrated through the example of clustering financial time series, for
instance prices from the credit default swaps market. Results are available on
the website www.datagrapple.com and an IPython Notebook tutorial is available
at www.datagrapple.com/Tech for reproducible research.Comment: submitted to Pattern Recognition Letter
A proposal of a methodological framework with experimental guidelines to investigate clustering stability on financial time series
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 the assets' clustering behaviour. The perturbation framework is
illustrated on an extensive credit default swap time series database available
online at www.datagrapple.com.Comment: Accepted at ICMLA 201
Benchmarking in cluster analysis: A white paper
To achieve scientific progress in terms of building a cumulative body of
knowledge, careful attention to benchmarking is of the utmost importance. This
means that proposals of new methods of data pre-processing, new data-analytic
techniques, and new methods of output post-processing, should be extensively
and carefully compared with existing alternatives, and that existing methods
should be subjected to neutral comparison studies. To date, benchmarking and
recommendations for benchmarking have been frequently seen in the context of
supervised learning. Unfortunately, there has been a dearth of guidelines for
benchmarking in an unsupervised setting, with the area of clustering as an
important subdomain. To address this problem, discussion is given to the
theoretical conceptual underpinnings of benchmarking in the field of cluster
analysis by means of simulated as well as empirical data. Subsequently, the
practicalities of how to address benchmarking questions in clustering are dealt
with, and foundational recommendations are made
Clustering Financial Time Series: How Long is Enough?
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 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.Comment: Accepted at IJCAI 201
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