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Anomaly Detection Based on Mining Six Local Data Features and BP Neural Network

1
School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou 510641, China
2
School of Mathematics, South China University of Technology, Guangzhou 510641, China
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(4), 571; https://doi.org/10.3390/sym11040571
Received: 8 March 2019 / Revised: 14 April 2019 / Accepted: 15 April 2019 / Published: 19 April 2019
(This article belongs to the Special Issue Symmetry in Engineering Sciences)
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Abstract

Key performance indicators (KPIs) are time series with the format of (timestamp, value). The accuracy of KPIs anomaly detection is far beyond our initial expectations sometimes. The reasons include the unbalanced distribution between the normal data and the anomalies as well as the existence of many different types of the KPIs data curves. In this paper, we propose a new anomaly detection model based on mining six local data features as the input of back-propagation (BP) neural network. By means of vectorization description on a normalized dataset innovatively, the local geometric characteristics of one time series curve could be well described in a precise mathematical way. Differing from some traditional statistics data characteristics describing the entire variation situation of one sequence, the six mined local data features give a subtle insight of local dynamics by describing the local monotonicity, the local convexity/concavity, the local inflection property and peaks distribution of one KPI time series. In order to demonstrate the validity of the proposed model, we applied our method on 14 classical KPIs time series datasets. Numerical results show that the new given scheme achieves an average F1-score over 90%. Comparison results show that the proposed model detects the anomaly more precisely. View Full-Text
Keywords: anomaly detection; local data features; BP neural network; local monotonicity; convexity/concavity; local inflection; peaks distribution anomaly detection; local data features; BP neural network; local monotonicity; convexity/concavity; local inflection; peaks distribution
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Zhang, Y.; Zhu, Y.; Li, X.; Wang, X.; Guo, X. Anomaly Detection Based on Mining Six Local Data Features and BP Neural Network. Symmetry 2019, 11, 571.

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