计算机工程与应用 ›› 2020, Vol. 56 ›› Issue (23): 130-138.DOI: 10.3778/j.issn.1002-8331.2007-0439

• 模式识别与人工智能 • 上一篇    下一篇

核转折点裁剪表示的时间序列异常检测算法

展鹏,陈琳,曹鲁慧,许浩然,李学庆   

  1. 1.山东大学 软件学院,济南 250100
    2.山东大学 信息化工作办公室,济南 250100
  • 出版日期:2020-12-01 发布日期:2020-11-30

Time Series Anomaly Detection Based on Kernel Turning Points Clipped Representation

ZHAN Peng, CHEN Lin, CAO Luhui, XU Haoran, LI Xueqing   

  1. 1.School of Software, Shandong University, Jinan 250100, China
    2.Informatization Office, Shandong University, Jinan 250100, China
  • Online:2020-12-01 Published:2020-11-30

摘要:

裁剪表示是时间序列降维表示研究领域中一种重要的数据驱动表示方法,该类方法将原始时间序列数据转换为一组由0和1组成的序列。然而,传统裁剪表示方法忽略了时间序列中数据点对序列趋势变化的影响,同时无法自定义降维表示后的压缩率。为了解决以上问题,提出了一种基于核转折点的裁剪表示方法KTPC,并基于KTPC表示方法提出了一种高效的时间序列异常检测算法KTPC-AD。所提方法按照指定的压缩率寻找时间序列中的核转折点,将时间序列转换为由核转折点裁剪表示形成的一组0和1序列,利用KTPC-AD算法计算时间序列的异常得分,最终获得异常序列。实验结果表明,KTPC方法具有较高的表示效率,基于KTPC表示的时间序列异常检测算法不仅降低了异常检测的时间复杂度,同时有效提升了异常检测精度。

关键词: 时间序列, 异常检测, 核转折点, 降维表示

Abstract:

Clipped representation is one of the important data-driven representation approaches in the fields of time series dimensionality reduction and representation, it transforms the raw time series into a set of sequences composed of 0 and 1. However, the traditional clipped representation ignores the influence of data points on the local trend of time series, and the compression ratio is determined by the data itself, which means the user has no choice to make. In order to address the above shortcomings, an improved clipped representation based on kernel turning points, called KTPC, is proposed in this paper. Based on KTPC, a novel time series anomaly detection approach is proposed, called KTPC-AD. The proposed method first detects the kernel turning points in each time series and represents the raw sequences by KTPC, and then, the anomaly scores of each sequence are calculated by KTPC-AD. Finally, the anomaly sequences are detected. The experimental results demonstrate that KTPC achieves higher processing efficiency, and KTPC-AD not only reduces the time complexity of anomaly detection, but also effectively improves the accuracy of detecting anomalies in time series.

Key words: time series, anomaly detection, kernel turning point, dimensionality reduction representation