title: ÐÐ»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð²ÑÐ±ÑÐ¾ÑÐ¾Ð² Ð² Ð´Ð°Ð½Ð½ÑÑ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð½ÐµÑÐ°Ð²ÐµÐ½ÑÑÐ²Ð° Ð§ÐµÐ±ÑÑÐµÐ²Ð°
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Outlier detection based on Chebyshev inequality
Creators: name: Ð§ÐµÐ¿ÑÐ»Ð¸Ñ, ÐÐ¸ÑÐ°Ð¸Ð»
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2020
Subjects: Ð°Ð½Ð¾Ð¼Ð°Ð»Ð¸Ñ
Ð²ÑÐ±ÑÐ¾Ñ
Ð½ÐµÑÐ°Ð²ÐµÐ½ÑÑÐ²Ð¾ ÑÐµÐ±ÑÑÐµÐ²Ð°
ÑÐ¾Ð±Ð°ÑÑÐ½ÑÐµ Ð¾ÑÐµÐ½ÐºÐ¸
python
anomaly
outlier
chebyshev inequality
robust estimates

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description: This paper is devoted to the study of changes in aspect [3], based on Chebyshev inequality on one-dimensional data. Its robust modification is also available. It outlines general concepts about data drops, various classic and non-classical aspects of drops. The quality of rejection of these algorithms is compared. Software implementations of model data generation methods have been developed. Python in the PyCharm development environment. As a result, the following data analysis methods were analyzed: N-sigma with its robust modifications, methods based on Chebyshev inequality and its robust modification on model data with standard normal distribution and contaminating such as âshiftâ, âscaleâ, Cauchy estimate. It was also decided to select the parameters of the algorithms for specific distributions over the grid. As a result of the research, it was concluded that when directly using Chebyshev inequality can be found in the classical N-sigma method, when using the ânon-classicalâ Chebyshev inequality, a robust rejection rule is obtained that often does not concede, and sometimes surpass all other possible algorithms . For many methods, âsuccessfulâ parameters were proposed for various distributions based on the maximum of the H-measure.