【摘 要】
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Dear editor,rnTime series anomaly detection, aiming for identifying unexpected observations within the given time series, has been considered as one of the most
【机 构】
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School of Software, Shandong University, Jinan 250100, China;Informatization Office, Shandong Univer
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Dear editor,rnTime series anomaly detection, aiming for identifying unexpected observations within the given time series, has been considered as one of the most challenging studies in time series data mining [1, 2].In this study, we present a novel set-based piecewise aggregate representation (SPAR) for anomaly detection, dubbed as SPAR-AD.Compared with other existing anomaly sequence detection methods, SPAR-AD not only pays close attention to recognize the significant changes of time series in the amplitude domain, but also keeps a watchful eye on identifying the corresponding variations in the temporal domain.Concretely, SPAR-AD can evenly divide a given time series into non-overlapping sequences and further calculate the corresponding anomaly scores of these sequences based on their own amplitude temporal features.Accordingly, all anomaly sequences with relatively high anomaly scores in the given time series can be detected effectively.
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