Abstract
The purpose of the study is to develop a systemthat automatically processes data based on existing and newly entered data, especially with the aim of ensuring high data quality by detecting and eliminating anomalies. The quantile filtering method, Chebyshev’s inequality, Kolmogorov-Smirnov two-sampletest, and others should be noted among the methods used. In the course of the research, the theoretical aspects of the methods, various principles of detecting anomalies for different types of data were considered and analysed. Different principles and approaches applied to anomaly detection in different contexts were explored. The results of the analysis and the selection of optimal methods for detecting anomalies in various types of data are important for the effective functioningof the automatic data processing system. This will make it possible to achieve accuracy and reliability in the detection ofanomalies and ensure high quality of data used in the machine learning system.
Recommended Citation
Boyko, Nataliya and Kovalchuk, Roman
(2024)
"Detection of anomalies and Data Drift in a time-series dismissal prediction system,"
Iraqi Journal for Computer Science and Mathematics: Vol. 5:
Iss.
3, Article 20.
DOI: https://doi.org/10.30880/ijcsm.2024.05.03.12
Available at:
https://ijcsm.researchcommons.org/ijcsm/vol5/iss3/20
