Abstract
Time series data appears in numerous applications including medical data processing, financial analytics, network traffic monitoring, and Web click-stream analysis. An essential task in time series mining is efficiently finding matches between similar time series or parts of time series in a large dataset. In this work, we introduce a new definition of subseries join as a generalization of subseries matching. We then propose an efficient and robust solution to subseries join (and match) based on a non-uniform segmentation and a hierarchical feature representation. Experiments demonstrate the effectiveness of our approach and also show that this approach can better tolerate noise and phase-scaling than previous work.
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References
Lin, Y., McCool, M.D., Ghorbani, A.A.: Motif and anomaly discovery of time series based on subseries join. In: IAENG International Conference on Data Mining and Applications, ICDMA 2010 (2010)
Myers, C., Rabiner, L.: A level building programming dynamic time warping algorithm for connected word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing 29(2), 284–297 (1981)
Cilibrasi, R., Vitányi, P.M.: Clustering by compression. IEEE Transactions on Information Theory 51(4), 1523–1545 (2005)
Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time-series databases. In: Proceedings of 1994 ACM SIGMOD International Conference on Management of Data, pp. 419–429 (1994)
Moon, Y.S., Whang, K.Y., Loh, W.K.: Duality-based subsequence matching in time-series databases. In: Proceedings of the 17th International Conference on Data Engineering, pp. 263–272 (2001)
Moon, Y.S., Whang, K.Y., Han, W.S.: General match: a subsequence matching method in time-series databases based on generalized windows. In: Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data, pp. 382–393 (2002)
Lin, Y., McCool, M.D.: Nonuniform segment-based compression of motion capture data. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Paragios, N., Tanveer, S.-M., Ju, T., Liu, Z., Coquillart, S., Cruz-Neira, C., Müller, T., Malzbender, T. (eds.) ISVC 2007, Part I. LNCS, vol. 4841, pp. 56–65. Springer, Heidelberg (2007)
Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(7), 629–639 (1990)
Canny, J.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986)
Lin, Y.: Subseries join and compression of time series data based on non-uniform segmentation. PhD thesis, University of Waterloo (2008)
Shekar, S., Chawla, S.: Spatial Databases: a Tour, 1st edn. Prentice-Hall, Englewood Cliffs (2003)
Keogh, E.: The UCR time series data mining archive. Department of Computer Science and Engineering, University of California, Riverside (2006), http://www.cs.ucr.edu/~eamonn/time_series_data
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Lin, Y., McCool, M.D. (2010). Subseries Join: A Similarity-Based Time Series Match Approach. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2010. Lecture Notes in Computer Science(), vol 6118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13657-3_27
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DOI: https://doi.org/10.1007/978-3-642-13657-3_27
Publisher Name: Springer, Berlin, Heidelberg
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