Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13087/2650
Title: Detection and handling outliers in longitudinal data: wavelets decomposition as a solution
Authors: Benghoul, Maroua
Yazıcı Berna
Sezer, Ahmet
Keywords: Detection
Handling
Longitudinal data
Outliers
Wavelets decomposition
Refinement
Values
Models
Issue Date: 2022
Publisher: Taylor & Francis Inc
Abstract: Wavelets analysis has become a powerful mathematical tool to decompose a series and to provide its frequent and temporal features. Despite the fact that many researchers have begun testing this approach on financial and economic data to detect and treat outliers, it still not widely applied for longitudinal data. Therefore, this article proposes two algorithms for improving the accuracy of outlier detection in longitudinal data using Wavelets Decomposition, namely, Wavelets Decomposition for Outliers Detection and Handling across Subjects (WDODHAS), and Wavelets Decomposition for Outliers Detection and Handling within Subjects (WDODHWIS). The results show that Wavelets Decomposition is capable of detecting and handling outliers without erasing them, as well as highlighting hypothetical scenarios when these observations cannot be handled.
URI: https://doi.org/10.1080/03610918.2022.2050389
https://hdl.handle.net/20.500.13087/2650
ISSN: 0361-0918
1532-4141
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu

Show full item record

CORE Recommender

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.