Version 1
: Received: 30 April 2024 / Approved: 30 April 2024 / Online: 30 April 2024 (12:34:20 CEST)
How to cite:
Limam, E. H. M.; Bellamine, I.; Tmiri, A. Univariate Outlier Detection: Precision-Driven Algorithm for Single-Cluster Scenarios. Preprints2024, 2024042008. https://doi.org/10.20944/preprints202404.2008.v1
Limam, E. H. M.; Bellamine, I.; Tmiri, A. Univariate Outlier Detection: Precision-Driven Algorithm for Single-Cluster Scenarios. Preprints 2024, 2024042008. https://doi.org/10.20944/preprints202404.2008.v1
Limam, E. H. M.; Bellamine, I.; Tmiri, A. Univariate Outlier Detection: Precision-Driven Algorithm for Single-Cluster Scenarios. Preprints2024, 2024042008. https://doi.org/10.20944/preprints202404.2008.v1
APA Style
Limam, E. H. M., Bellamine, I., & Tmiri, A. (2024). Univariate Outlier Detection: Precision-Driven Algorithm for Single-Cluster Scenarios. Preprints. https://doi.org/10.20944/preprints202404.2008.v1
Chicago/Turabian Style
Limam, E. H. M., Insaf Bellamine and Amal Tmiri. 2024 "Univariate Outlier Detection: Precision-Driven Algorithm for Single-Cluster Scenarios" Preprints. https://doi.org/10.20944/preprints202404.2008.v1
Abstract
This study introduces a novel algorithm tailored for the precise detection of lower outliers in univariate datasets, particularly suited for scenarios with a single cluster. The approach leverages a combination of transformative techniques and advanced filtration methods to efficiently segregate anomalies from normal values. Notably, the algorithm emphasizes high precision, ensuring minimal false positives, and requires only a few parameters for configuration. Its unsupervised nature enables robust anomaly filtering without the need for extensive manual intervention. To validate its efficacy, the algorithm is rigorously tested using real-world data obtained from photovoltaic (PV) module strings with similar DC capacities, containing various anomalies. Results demonstrate the algorithm's capability to accurately identify lower outliers while maintaining computational efficiency and reliability in practical applications.
Keywords
outlier detection, machine learning, univariate data analysis, data mining
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
