explore__plot

This helper will produce the plot in the Explore tab panel.

internal

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.

Alexis Sarda

dtwclust

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

explore__plot function

<dl><dt>ids</dt>
<dd>A character with the expression that should yield the integer ids.</dd>
<dt>df</dt>
<dd>The data frame with the melted time series.</dd>
<dt>series</dt>
<dd>The list of time series.</dd></dl>

Arguments

This helper will produce the plot in the Explore tab panel. — explore__plot

<dl>

<dt>ids</dt>
<dd>A character with the expression that should yield the integer ids.</dd>


<dt>df</dt>
<dd>The data frame with the melted time series.</dd>


<dt>series</dt>
<dd>The list of time series.</dd>

</dl>

explore__plot: This helper will produce the plot in the Explore tab panel.

Description

Usage

Arguments