DragStream: An Anomaly And Concept Drift Detector In Univariate Data Streams

Anne Marthe Sophie Ngo Bibinbe, A. J. Mahamadou, Michael Franklin Mbouopda, E. Nguifo
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引用次数: 1

Abstract

Anomaly detection in data streams comes with different technical challenges due to the data nature. The main challenges include storage limitations, the speed of data arrival, and concept drifts. In the literature, methods for mining data streams in order to detect anomalies have been proposed. While some methods focus on tackling a specific issue, other methods handle diverse problems but may have high complexity (time and memory). In the present work, we propose DragStream, a novel subsequence anomaly and concept drift detection algorithm for univariate data streams. DragStream extends the subsequence anomaly detection method for time series data Drag to streaming data. Furthermore, the new method is inspired by the well-known Matrix Profile, Drag, and MILOF which are respectively point and subsequence anomaly detection methods for time series and data streams. We conducted intensive experiments and statistical analysis to evaluate the performance of the proposed approach against existing methods. The results show that our method is competitive in performance while being linear in time and memory complexity. Finally, we provide an open-source implementation of the new method.
DragStream:单变量数据流中的异常和概念漂移检测器
由于数据的性质,数据流中的异常检测面临着不同的技术挑战。主要的挑战包括存储限制、数据到达速度和概念漂移。在文献中,已经提出了挖掘数据流以检测异常的方法。虽然有些方法专注于解决特定问题,但其他方法处理各种问题,但可能具有很高的复杂性(时间和内存)。在本工作中,我们提出了一种新的单变量数据流子序列异常和概念漂移检测算法DragStream。DragStream将时间序列数据的子序列异常检测方法扩展到流数据。此外,该方法还受到了著名的矩阵轮廓、拖拽和MILOF的启发,这三种方法分别是时间序列和数据流的点和子序列异常检测方法。我们进行了大量的实验和统计分析,以评估所提出的方法与现有方法的性能。结果表明,该方法具有较好的性能,同时在时间和内存复杂度上保持线性。最后,我们提供了一个新方法的开源实现。
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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