metric.DTW

Computes the Dynamic Time Warping distance between functional data.
DTW allows for non-linear alignment of curves.

Functional data analysis tools with a high-performance 'Rust' backend.
Provides methods for functional data manipulation, depth computation,
distance metrics, regression, and statistical testing. Supports both
1D functional data (curves) and 2D functional data (surfaces).
Methods are described in Ramsay and Silverman (2005,
ISBN:978-0-387-40080-8) "Functional Data Analysis" and Ferraty and
Vieu (2006, ISBN:978-0-387-30369-7) "Nonparametric Functional Data
Analysis".

Dr. Mueller

fdars

Functional Data Analysis in 'Rust'

Simon Müller

metric.DTW function

<dl><dt>fdataobj</dt>
<dd>An object of class 'fdata'.</dd>
<dt>fdataref</dt>
<dd>An object of class 'fdata'. If NULL, computes self-distances.</dd>
<dt>p</dt>
<dd>The p in Lp distance (default 2 for L2/Euclidean).</dd>
<dt>w</dt>
<dd>Sakoe-Chiba window constraint. Default is min(ncol(fdataobj), ncol(fdataref)).
Use -1 for no window constraint.</dd>
<dt>...</dt>
<dd>Additional arguments (ignored).</dd></dl>

Arguments

Dynamic Time Warping for Functional Data — metric.DTW

<dl>

<dt>fdataobj</dt>
<dd>An object of class 'fdata'.</dd>


<dt>fdataref</dt>
<dd>An object of class 'fdata'. If NULL, computes self-distances.</dd>


<dt>p</dt>
<dd>The p in Lp distance (default 2 for L2/Euclidean).</dd>


<dt>w</dt>
<dd>Sakoe-Chiba window constraint. Default is min(ncol(fdataobj), ncol(fdataref)).
Use -1 for no window constraint.</dd>


<dt>...</dt>
<dd>Additional arguments (ignored).</dd>

</dl>

metric.DTW: Dynamic Time Warping for Functional Data

Description

Usage

Value

Arguments

Examples