IntelliPaper
Abstract
One of the first important steps in achieving informed data analysis is detection of outliers. Even in cases where the final values are often considered to be incorrect calculations or noise, they can still provide very important information in some cases. Therefore, it is very important to detect them before modeling and analysis. In this paper, we present a structured and comprehensive review of residual detection research. There are many different methods, hence the purpose of this article is to help the novice researcher to formulate his ideas and gain an easier understanding of the various lines of research in which research has been conducted on this topic.
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Conflict of Interest
The authors declare no conflict of interest.
Ethical Approval
Not applicable
Data Availability
The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].
Funding
This work did not receive any external funding.
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