StandWalkJump

Multivariate time series (MTS) involving short duration ECG signals recorded from a healthy 25-year-old male performing different
physical activities

datasets

An implementation of several machine learning algorithms for
multivariate time series. The package includes functions allowing the
execution of clustering, classification or outlier detection methods,
among others. It also incorporates a collection of multivariate time
series datasets which can be used to analyse the performance of new
proposed algorithms. Some of these datasets are stored in GitHub data
packages 'ueadata1' to 'ueadata8'. To access these data packages, run
'install.packages(c('ueadata1', 'ueadata2', 'ueadata3', 'ueadata4', 'ueadata5', 'ueadata6', 'ueadata7', 'ueadata8'), repos='<https://anloor7.github.io/drat/>')'.
The installation takes a couple of minutes but we strongly encourage the
users to do it if they want to have available all datasets of mlmts.
Practitioners from a broad variety of fields could
benefit from the general framework provided by 'mlmts'.

Angel Lopez-Oriona

mlmts

Machine Learning Algorithms for Multivariate Time Series

Jose A. Vilar

StandWalkJump function

A <code>list</code> with two elements, which are:<dl>
<dt><code>data</code></dt>
<dd>A list with 27 MTS.</dd><dt><code>classes</code></dt>
<dd>A numeric vector indicating the corresponding classes associated with the elements in <code>data</code>.</dd>
</dl>

Format

StandWalkJump — StandWalkJump

A <code>list</code> with two elements, which are:<dl>
<dt><code>data</code></dt>
<dd>A list with 27 MTS.</dd>

<dt><code>classes</code></dt>
<dd>A numeric vector indicating the corresponding classes associated with the elements in <code>data</code>.</dd>


</dl>

StandWalkJump: StandWalkJump

Description

Usage

Arguments

Format

Details

References