initialise_df_results

Initialises a dataframe that will contain an overview of metrics for each estimated configuration (for example adjusted randindex).

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

initialise_df_results function

<dl><dt>robust</dt>
<dd>robust or classical estimation</dd>
<dt>limit_est_groups</dt>
<dd>maximum allowed number of groups that can be estimated</dd></dl>

Arguments

Initialises a dataframe that will contain an overview of metrics for each estimated configuration (for example adjusted randindex). — initialise_df_results

<dl>

<dt>robust</dt>
<dd>robust or classical estimation</dd>


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

</dl>

initialise_df_results: Initialises a dataframe that will contain an overview of metrics for each estimated configuration (for example adjusted randindex).

Description

Usage

Value

Arguments

Examples