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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