distance_chi_cpp

Computed as:
<code>xy &lt;- x + y</code>
<code>y. &lt;- y / sum(y)</code>
<code>x. &lt;- x / sum(x)</code>
<code>sqrt(sum(((x. - y.)^2) / (xy / sum(xy))))</code>.
Cannot handle NA values. When <code>x</code> and <code>y</code> have zeros in the same
position, <code>NaNs</code> are produced. Please replace these zeros with
pseudo-zeros (i.e. 0.0001) if you wish to use this distance metric.

Fast C++ implementation of Dynamic Time Warping for time series dissimilarity analysis, with applications in environmental monitoring and sensor data analysis, climate science, signal processing and pattern recognition, and financial data analysis. Built upon the ideas presented in Benito and Birks (2020) <doi:10.1111/ecog.04895>, provides tools for analyzing time series of varying lengths and structures, including irregular multivariate time series. Key features include individual variable contribution analysis, restricted permutation tests for statistical significance, and imputation of missing data via GAMs. Additionally, the package provides an ample set of tools to prepare and manage time series data.

Blas M. Benito

distantia

Advanced Toolset for Efficient Time Series Dissimilarity
Analysis

distance_chi_cpp function

<dl><dt>x</dt>
<dd>(required, numeric vector).</dd>
<dt>y</dt>
<dd>(required, numeric vector) of same length as <code>x</code>.</dd></dl>

Arguments

(C++) Normalized Chi Distance Between Two Vectors — distance_chi_cpp

<dl>

<dt>x</dt>
<dd>(required, numeric vector).</dd>


<dt>y</dt>
<dd>(required, numeric vector) of same length as <code>x</code>.</dd>

</dl>

distance_chi_cpp: (C++) Normalized Chi Distance Between Two Vectors

Description

Usage

Value

Arguments

See Also

Examples