scaling_X

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

scaling_X function

<dl><dt>X</dt>
<dd>input</dd>
<dt>firsttime</dt>
<dd>Scaling before generating Y and before adding outliers: this is always with mean and sd. If this is FALSE, it indicates that
we are using the function for a second time, after adding the outliers. In the robust case it uses median and MAD, otherwise again mean and sd.</dd>
<dt>robust</dt>
<dd>logical, scaling with robust metrics instead of with non-robust measures</dd>
<dt>vars</dt>
<dd>number of observable variables</dd></dl>

Arguments

Scaling of X. — scaling_X

<dl>

<dt>X</dt>
<dd>input</dd>


<dt>firsttime</dt>
<dd>Scaling before generating Y and before adding outliers: this is always with mean and sd. If this is FALSE, it indicates that
we are using the function for a second time, after adding the outliers. In the robust case it uses median and MAD, otherwise again mean and sd.</dd>


<dt>robust</dt>
<dd>logical, scaling with robust metrics instead of with non-robust measures</dd>


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

</dl>

scaling_X: Scaling of X.

Description

Usage

Value

Arguments