initialise_rcj

This function initialises a data frame which will eventually be filled with the optimized number of groups and group specific factors for each C and for each subset of the original dataset.

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_rcj function

<dl><dt>indices_subset</dt>
<dd>all indices of the subsets</dd>
<dt>C_candidates</dt>
<dd>candidates for C (parameter in PIC)</dd></dl>

Arguments

Initialises rcj. — initialise_rcj

<dl>

<dt>indices_subset</dt>
<dd>all indices of the subsets</dd>


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

</dl>

initialise_rcj: Initialises rcj.

Description

Usage

Value

Arguments

Examples