Visual Analysis for Anomaly Detection in Time-Series: A Survey
-
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.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.
-
Keywords:
- anomaly detection /
- visual analysis /
- visualization /
- time-series data /
- data mining
-
-
期刊类型引用(2)
1. 屠要峰，陈正华，韩银俊，陈兵，关东海. 基于持久性内存和SSD的后端存储MixStore. 计算机研究与发展. 2021(02): 406-417 . 
本站查看
2. 张婷，李文敬，黄帆. 基于多核PC的MAP记录表冲突规避算法. 计算机工程与设计. 2020(12): 3419-3424 . 百度学术
其他类型引用(5)
计量
- 文章访问数: 2487
- HTML全文浏览量: 13
- PDF下载量: 1107
- 被引次数: 7
下载:
