Time series clustering along with optimized techniques related
to the Dynamic Time Warping distance and its corresponding lower bounds.
Implementations of partitional, hierarchical, fuzzy, k-Shape and TADPole
clustering are available. Functionality can be easily extended with
custom distance measures and centroid definitions. Implementations of
DTW barycenter averaging, a distance based on global alignment kernels,
and the soft-DTW distance and centroid routines are also provided.
All included distance functions have custom loops optimized for the
calculation of cross-distance matrices, including parallelization support.
Several cluster validity indices are included.

Time Series Clustering Along with Optimizations for the Dynamic
Time Warping Distance

package-ubuntu-20.04-r-4.0.5#cran#dtwclust-5.5.10

<p>This helper will parse comma-separated key-value pairs
</p>

<table summary="R argblock">
<tr valign="top">
<td><code>input_text</code></td>
<td>
<p>A character.</p>
</td>
</tr>
<tr valign="top">
<td><code>into</code></td>
<td>
<p>The name of the function that will contain the resulting values.</p>
</td>
</tr>
</table>

package-ubuntu-20.04-r-4.0.5#cran#dtwclust-5.5.10#docs#parse_input

parse_input

Description

Usage

Arguments

dtwclust

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`input_text`	A character.
`into`	The name of the function that will contain the resulting values.