Abstract
The problem of finding anomaly has received much attention recently. However, most of the anomaly detection algorithms depend on an explicit definition of anomaly, which may be impossible to elicit from a domain expert. Using discords as anomaly detectors is useful since less parameter setting is required. Keogh et al proposed an efficient method for solving this problem. However, their algorithm requires users to choose the word size for the compression of subsequences. In this paper, we propose an algorithm which can dynamically determine the word size for compression. Our method is based on some properties of the Haar wavelet transformation. Our experiments show that this method is highly effective.
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References
Dasgupta, D., Forrest, S.: Novelty detection in time series data using ideas from immunology (1996)
Keogh, E.: Exact indexing of dynamic time warping (2002)
Keogh, E., Lonardi, S., Chiu, B.: Finding surprising patterns in a time series database in linear time and space. In: Proceedings of The Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2002), July 2002, Edmonton, Alberta, Canada (2002)
Keogh, E., Folias, T.: The ucr time series data mining archive
Keogh, E.J., Lonardi, S., Ratanamahatana, C.A.: Towards parameter-free data mining. In: KDD 2004, pp. 206–215 (2004)
Lin, J., Keogh, E.J., Fu, A.W.-C., Herle, H.V.: Approximations to magic: Finding unusual medical time series. In: CBMS, pp. 329–334 (2005)
pong Chan, K., Fu, A.W.-C.: Efficient time series matching by wavelets. In: ICDE, pp. 126–133 (1999)
Ratanamahatana, C.A., Keogh, E.J.: Making time-series classification more accurate using learned constraints. In: SDM 2004 (2004)
Shahabi, C., Tian, X., Zhao, W.: TSA-tree: A wavelet-based approach to improve the efficiency of multi-level surprise and trend queries on time-series data. In: Statistical and Scientific Database Management, pp. 55–68 (2000)
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© 2006 Springer-Verlag Berlin Heidelberg
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Fu, A.Wc., Leung, O.TW., Keogh, E., Lin, J. (2006). Finding Time Series Discords Based on Haar Transform. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_3
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DOI: https://doi.org/10.1007/11811305_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
Online ISBN: 978-3-540-37026-0
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