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Method for Detecting and Eliminating Data Time Series Outlier in High-Speed Process Data Sensors

Fariza Tebueva(1*), Vladimir Kopytov(2), Viacheslav Petrenko(3), Pavel Kharechkin(4), Alesia Sidorchuk(5)

(1) Department of Applied Mathematics and Computer Security in the North Caucasus Federal University, Russian Federation
(2) LLC «Infocom-S», Russian Federation
(3) Institute of Information Technologies and Telecommunications of the North Caucasus Federal University, Russian Federation
(4) LLC «Infocom-S», Russian Federation
(5) Department of Organization and Technology of Information Protection at the North Caucasus Federal University, Russian Federation
(*) Corresponding author


DOI: https://doi.org/10.15866/irecap.v7i7.13453

Abstract


The objective is to identify and eliminate measurement errors in data time series of data sensors of high-speed processes. Fractal analysis is used to identify and eliminate outliers in data time series of data sensors of high-speed processes. The proposed method provides an accurate detection of an outlier and an acceptable error in a series characteristic modification when the outlier is replaced with a predicted value. As a result, the time series becomes persistent again. The originality of the method consists in using such a hidden regularity of time series as a correlation of values to detect measurement errors. The importance of the study is determined by the immediacy of the problem of detecting and eliminating outliers in data time series of data sensors with stringent requirements imposed upon to reduce decision-making time in the event of critical situations.
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Keywords


High-Speed Processes; Measurement Errors; Outliers; Persistence

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References


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