A New Method for Outlier Detection on Time Series Data

Resource type
Thesis type
(Thesis) M.Sc.
Date created
Author: Liu, Lin
Time series outlier detection has been attracting a lot of attention in research and application. In this thesis, we introduce the new problem of detecting hybrid outliers on time series data. Hybrid outliers show their outlyingness in two ways. First, they may deviate greatly from their neighbors. Second, their behaviors may also be different from that of their peers in other time series. We propose a framework to detect hybrid outliers, and two algorithms based on the framework are developed to show the feasibility of our framework. An extensive empirical study on both real data and synthetic data verifies the effectiveness and efficiency of our algorithms.
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Copyright is held by the author.
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Scholarly level
Supervisor or Senior Supervisor
Thesis advisor: Pei, Jian
Member of collection
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