initialise_X

X is an array with dimensions N, T and number of observable variables.
The variables are randomly generated with mean 0 and sd 1.

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_X function

<dl><dt>NN</dt>
<dd>number of time series</dd>
<dt>TT</dt>
<dd>length of time series</dd>
<dt>vars</dt>
<dd>number of available observable variables</dd>
<dt>scale_robust</dt>
<dd>logical, defines if X will be scaled with robust metrics instead of with non-robust metrics</dd></dl>

Arguments

Creates X (the observable variables) to use in simulations. — initialise_X

<dl>

<dt>NN</dt>
<dd>number of time series</dd>


<dt>TT</dt>
<dd>length of time series</dd>


<dt>vars</dt>
<dd>number of available observable variables</dd>


<dt>scale_robust</dt>
<dd>logical, defines if X will be scaled with robust metrics instead of with non-robust metrics</dd>

</dl>

initialise_X: Creates X (the observable variables) to use in simulations.

Description

Usage

Value

Arguments