ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2018, Vol. 55 ›› Issue (9): 1843-1852.doi: 10.7544/issn1000-1239.2018.20180126

所属专题: 2018优青专题

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  1. (CAD & CG国家重点实验室(浙江大学) 杭州 310058) (
  • 出版日期: 2018-09-01
  • 基金资助: 
    国家重点研发计划项目(2018YFB0904503);国家“九七三”重点基础研究发展计划基金项目(2015CB352503);国家自然科学基金优秀青年科学基金项目(61422211);国家自然科学基金项目(61772456,61761136020) This work was supported by the National Key Research and Development Program of China (2018YFB0904503), the National Basic Research Program of China (973 program) (2015CB352503), the National Natural Science Foundation of China for Excellent Young Scientists (61422211), and the National Natural Science Foundation of China (61772456, 61761136020).

Visual Analysis for Anomaly Detection in Time-Series: A Survey

Han Dongming,Guo Fangzhou,Pan Jiacheng,Zheng Wenting,Chen Wei   

  1. (State Key Laboratory of CAD & CG (Zhejiang University), Hangzhou 310058)
  • Online: 2018-09-01

摘要: 时序数据中的异常检测指的是在时序上去检测分析数据中异常的特征、趋势或模式.自动化的异常检测方法常会忽略细微的、模糊的、不确定的异常.可视分析通过对数据的可视表达和可视界面,集成用户和数据挖掘的能力.首先总结异常检测的挑战;然后从异常类型(属性、拓扑和混合)和异常检测方法(直接投影法、聚类方法和机器学习方法)2个角度对面向时序数据异常检测的可视分析工作进行分类和总结;最后阐述了未来的研究方向.

关键词: 异常检测, 可视分析, 可视化, 时序数据, 数据挖掘

Abstract: Anomaly detection for time-series denotes the detection and analysis of abnormal and unusual patterns, trends and features. Automatic methods sometimes fail to detect anomalies that are subtle, fuzzy or uncertain, while visual analysis can overcome this challenge by integrating the capability of human users and data mining approaches through visual representations of the data and visual interface. In this paper, we identify the challenges of anomaly detection, and describe the existing works of visual analysis along two categories: types of anomalies (attributes, topologies and hybrids), and anomaly detection means (direct projection, clustering and machine learning). We highlight future research directions.

Key words: anomaly detection, visual analysis, visualization, time-series data, data mining