univtoydataset

A simulated non-stationary time series with regime changes.
This time series is used for illustration.

datasets

Provides a variety of original and flexible user-friendly
statistical latent variable models and unsupervised learning algorithms to
segment and represent time-series data (univariate or multivariate), and
more generally, longitudinal data, which include regime changes.
'samurais' is built upon the following packages, each of them is an
autonomous time-series segmentation approach: Regression with Hidden
Logistic Process ('RHLP'), Hidden Markov Model Regression ('HMMR'),
Multivariate 'RHLP' ('MRHLP'), Multivariate 'HMMR' ('MHMMR'), Piece-Wise
regression ('PWR'). For the advantages/differences of each of them, the
user is referred to our mentioned paper references.

Florian Lecocq

samurais

Statistical Models for the Unsupervised Segmentation of
Time-Series ('SaMUraiS')

Faicel Chamroukhi

Marius Bartcus

univtoydataset function

A data frame with 670 rows and 2 columns:<dl class="dl-horizontal">
<dt>x</dt><dd>The covariate variable which is the time in this time-series case.</dd>
<dt>y</dt><dd>The time series. The latter has been generated as follows:
<ul>
<li>First regime: 100 values of standard Normally distributed random numbers.</li>
<li>Second regime: 120 values of Normally distributed random numbers
with mean 7 and unit variance.</li>
<li>Third regime: 200 values of Normally distributed random numbers
with mean 4 and unit variance.</li>
<li>Fourth regime: 100 values of Normally distributed random numbers
with mean -2 and unit variance.</li>
<li>Fifth regime: 150 values of Normally distributed random numbers
with mean 3.5 and unit variance.</li>
</ul></dd>
</dl>

Format

A simulated non-stationary time series with regime changes. — univtoydataset

A data frame with 670 rows and 2 columns:<dl class='dl-horizontal'>
<dt>x</dt><dd>The covariate variable which is the time in this time-series case.</dd>
<dt>y</dt><dd>The time series. The latter has been generated as follows:
<ul>
<li>First regime: 100 values of standard Normally distributed random numbers.</li>
<li>Second regime: 120 values of Normally distributed random numbers
with mean 7 and unit variance.</li>
<li>Third regime: 200 values of Normally distributed random numbers
with mean 4 and unit variance.</li>
<li>Fourth regime: 100 values of Normally distributed random numbers
with mean -2 and unit variance.</li>
<li>Fifth regime: 150 values of Normally distributed random numbers
with mean 3.5 and unit variance.</li>
</ul></dd>
</dl>

univtoydataset: A simulated non-stationary time series with regime changes.

Description

Usage

Arguments

Format