utils_prepare_time

Handles Time Column in a List of Data Frames

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

utils_prepare_time function

<dl><dt>x</dt>
<dd>(required, named list of data frames). List with named data frames. Default: NULL.</dd>
<dt>time_column</dt>
<dd>(optional if <code>lock_step = FALSE</code>, and required otherwise, column name) Name of numeric column representing time. Default: NULL.</dd>
<dt>lock_step</dt>
<dd>(optional, logical) If TRUE, all input sequences are subset to their common times according to the values in the <code>time_column</code>. Default: FALSE.</dd></dl>

Arguments

Handles Time Column in a List of Data Frames — utils_prepare_time

<dl>

<dt>x</dt>
<dd>(required, named list of data frames). List with named data frames. Default: NULL.</dd>


<dt>time_column</dt>
<dd>(optional if <code>lock_step = FALSE</code>, and required otherwise, column name) Name of numeric column representing time. Default: NULL.</dd>


<dt>lock_step</dt>
<dd>(optional, logical) If TRUE, all input sequences are subset to their common times according to the values in the <code>time_column</code>. Default: FALSE.</dd>

</dl>

utils_prepare_time: Handles Time Column in a List of Data Frames

Description

Usage

Value

Arguments

See Also