Abstract
This paper describes an algorithm for discovering variable length patterns in real-valued time series. In contrast to most existing pattern discovery algorithms, ours does not first discretize the data, runs in linear time, and requires constant memory. These properties are obtained by sampling the data stream rather than processing all of the data. Empirical results show that the algorithm performs well on both synthetic and real data when compared to an exhaustive algorithm.
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Keywords
- Dynamic Time Warping
- Sampling Algorithm
- Pattern Discovery
- Pattern Instance
- Dynamic Time Warping Distance
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© 2006 Springer-Verlag Berlin Heidelberg
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Catalano, J., Armstrong, T., Oates, T. (2006). Discovering Patterns in Real-Valued Time Series. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds) Knowledge Discovery in Databases: PKDD 2006. PKDD 2006. Lecture Notes in Computer Science(), vol 4213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11871637_44
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DOI: https://doi.org/10.1007/11871637_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45374-1
Online ISBN: 978-3-540-46048-0
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