Abstract
Automatically finding interesting, novel or surprising patterns in time series data is useful in several applications, such as fault diagnosis and fraud detection. In this paper, we extend the notion of distance-based outliers to time series data and propose two algorithms to detect both global and local outliers in time series data. We illustrate these algorithms on some real datasets.
Keywords: Novelty detection, Outlier detection, Time series, Sequence mining.
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© 2005 Springer-Verlag Berlin Heidelberg
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Palshikar, G.K. (2005). Distance-Based Outliers in Sequences. In: Chakraborty, G. (eds) Distributed Computing and Internet Technology. ICDCIT 2005. Lecture Notes in Computer Science, vol 3816. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11604655_61
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DOI: https://doi.org/10.1007/11604655_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30999-4
Online ISBN: 978-3-540-32429-4
eBook Packages: Computer ScienceComputer Science (R0)