2,136 research outputs found
Clustering of discretely observed diffusion processes
In this paper a new dissimilarity measure to identify groups of assets
dynamics is proposed. The underlying generating process is assumed to be a
diffusion process solution of stochastic differential equations and observed at
discrete time. The mesh of observations is not required to shrink to zero. As
distance between two observed paths, the quadratic distance of the
corresponding estimated Markov operators is considered. Analysis of both
synthetic data and real financial data from NYSE/NASDAQ stocks, give evidence
that this distance seems capable to catch differences in both the drift and
diffusion coefficients contrary to other commonly used metrics
Parsimonious Time Series Clustering
We introduce a parsimonious model-based framework for clustering time course
data. In these applications the computational burden becomes often an issue due
to the number of available observations. The measured time series can also be
very noisy and sparse and a suitable model describing them can be hard to
define. We propose to model the observed measurements by using P-spline
smoothers and to cluster the functional objects as summarized by the optimal
spline coefficients. In principle, this idea can be adopted within all the most
common clustering frameworks. In this work we discuss applications based on a
k-means algorithm. We evaluate the accuracy and the efficiency of our proposal
by simulations and by dealing with drosophila melanogaster gene expression
data
Machine Learning Methods for Monitoring of Quasi-Periodic Traffic in Massive IoT Networks
One of the central problems in massive Internet of Things (IoT) deployments
is the monitoring of the status of a massive number of links. The problem is
aggravated by the irregularity of the traffic transmitted over the link, as the
traffic intermittency can be disguised as a link failure and vice versa. In
this work we present a traffic model for IoT devices running quasi-periodic
applications and we present both supervised and unsupervised machine learning
methods for monitoring the network performance of IoT deployments with
quasi-periodic reporting, such as smart-metering, environmental monitoring and
agricultural monitoring. The unsupervised methods are based on the Lomb-Scargle
periodogram, an approach developed by astronomers for estimating the spectral
density of unevenly sampled time series
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