Anomaly Detection in High-Dimensional Time Series Data with Scaled Bregman Divergence.

IF 1.8 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Algorithms Pub Date : 2025-02-01 Epub Date: 2025-01-24 DOI:10.3390/a18020062
Yunge Wang, Lingling Zhang, Tong Si, Graham Bishop, Haijun Gong
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引用次数: 0

Abstract

The purpose of anomaly detection is to identify special data points or patterns that significantly deviate from the expected or typical behavior of the majority of the data, and it has a wide range of applications across various domains. Most existing statistical and machine learning-based anomaly detection algorithms face challenges when applied to high-dimensional data. For instance, the unconstrained least-squares importance fitting (uLSIF) method, a state-of-the-art anomaly detection approach, encounters the unboundedness problem under certain conditions. In this study, we propose a scaled Bregman divergence-based anomaly detection algorithm using both least absolute deviation and least-squares loss for parameter learning. This new algorithm effectively addresses the unboundedness problem, making it particularly suitable for high-dimensional data. The proposed technique was evaluated on both synthetic and real-world high-dimensional time series datasets, demonstrating its effectiveness in detecting anomalies. Its performance was also compared to other density ratio estimation-based anomaly detection methods.

基于比例Bregman散度的高维时间序列数据异常检测。
异常检测的目的是识别明显偏离大多数数据的预期或典型行为的特殊数据点或模式,它在各个领域具有广泛的应用。大多数现有的基于统计和机器学习的异常检测算法在应用于高维数据时面临挑战。例如,无约束最小二乘重要性拟合(uLSIF)方法是一种最先进的异常检测方法,在某些条件下会遇到无界性问题。在这项研究中,我们提出了一种基于缩放Bregman散度的异常检测算法,该算法使用最小绝对偏差和最小二乘损失进行参数学习。该算法有效地解决了无界性问题,特别适用于高维数据。在合成和真实高维时间序列数据集上对该技术进行了评估,证明了其检测异常的有效性。并与其他基于密度比估计的异常检测方法进行了性能比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Algorithms
Algorithms Mathematics-Numerical Analysis
CiteScore
4.10
自引率
4.30%
发文量
394
审稿时长
11 weeks
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