calculate_best_config

Function that returns for each candidate C the best number of groups and factors, based on the PIC.

Robust Clustering of Time Series (RCTS) has the functionality to cluster time series using both the classical and the robust interactive fixed effects framework.
The classical framework is developed in Ando & Bai (2017) <doi:10.1080/01621459.2016.1195743>. The implementation within this package excludes the SCAD-penalty on the estimations of beta.
This robust framework is developed in Boudt & Heyndels (2022) <doi:10.1016/j.ecosta.2022.01.002> and is made robust against different kinds of outliers.
The algorithm iteratively updates beta (the coefficients of the observable variables), group membership, and the latent factors (which can be common and/or group-specific) along
with their loadings. The number of groups and factors can be estimated if they are unknown.

Ewoud Heyndels

RCTS

Clustering Time Series While Resisting Outliers

calculate_best_config function

<dl><dt>df_results</dt>
<dd>dataframe with results for each estimated configuration</dd>
<dt>df_pic</dt>
<dd>dataframe with the PIC for each configuration and for each candidate C</dd>
<dt>C_candidates</dt>
<dd>candidates for C (parameter in PIC)</dd>
<dt>limit_est_groups</dt>
<dd>maximum allowed number of groups that can be estimated</dd></dl>

Arguments

Function that returns for each candidate C the best number of groups and factors, based on the PIC. — calculate_best_config

<dl>

<dt>df_results</dt>
<dd>dataframe with results for each estimated configuration</dd>


<dt>df_pic</dt>
<dd>dataframe with the PIC for each configuration and for each candidate C</dd>


<dt>C_candidates</dt>
<dd>candidates for C (parameter in PIC)</dd>


<dt>limit_est_groups</dt>
<dd>maximum allowed number of groups that can be estimated</dd>

</dl>

calculate_best_config: Function that returns for each candidate C the best number of groups and factors, based on the PIC.

Description

Usage

Value

Arguments

Examples