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本文为美国明尼苏达州立大学(作者:Deepthi Cheboli)的硕士论文,共83页。
本文研究了时间序列数据的异常检测问题。时间序列异常检测的一些重要应用是医疗保健、生态系统干扰、入侵检测和飞机系统健康管理。尽管人们已经在异常检测方面进行了大量的工作,但大多数技术都在寻找与正常对象不同但不考虑数据序列的单个对象。本文分析了时间序列异常检测技术的现状,并进行了综述。我们还提出了新的异常检测技术和时间序列数据转换技术。通过对跨越不同领域收集的数据集上所提出的技术进行广泛的实验评估,我们发现本文提出的技术在许多数据集上都表现良好。
This thesis deals with the problem ofanomaly detection for time series data. Some of the important applications oftime series anomaly detection are healthcare, eco-system disturbances,intrusion detection and aircraft system health management. Although there hasbeen extensive work on anomaly detection (1), most of the techniques look forindividual objects that are different from normal objects but do not considerthe sequence aspect of the data into consideration. In this thesis, we analyzethe state of the art of time series anomaly detection techniques and present asurvey. We also propose novel anomaly detection techniques and transformationtechniques for the time series data. Through extensive experimental evaluationof the proposed techniques on the data sets collected across diverse domains,we conclude that our techniques perform well across many datasets.
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