Abstract
Approximately Recurring Motif (ARM) discovery is the problem of finding unknown patterns that appear frequently in real valued timeseries. In this paper, we propose a novel algorithm for solving this problem that can achieve performance comparable with the most accurate algorithms to solve this problem with a speed comparable to the fastest ones. The main idea behind the proposed algorithm is to convert the problem of ARM discovery into a density estimation problem in the single dimensionality shift-space (rather than in the original time-series space). This makes the algorithm more robust to short noise bursts that can dramatically affect the performance of most available algorithms. The paper also reports the results of applying the proposed algorithm to synthetic and real-world datasets.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Catalano, J., Armstrong, T., Oates, T.: Discovering patterns in real-valued time series. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 462–469. Springer, Heidelberg (2006)
Chiu, B., Keogh, E., Lonardi, S.: Probabilistic discovery of time series motifs. In: KDD 2003, pp. 493–498. ACM, New York (2003)
CPMD Toolbox, http://www.ii.ist.i.kyoto-u.ac.jp/~yasser/cpmd/cpmd.html
Jensen, K.L., Styczynxki, M.P., Rigoutsos, I., Stephanopoulos, G.N.: A generic motif discovery algorithm for sequenctial data. BioInformatics 22(1), 21–28 (2006)
Keogh, E., Lin, J., Fu, A.: Hot sax: efficiently finding the most unusual time series subsequence. In: IEEE ICDM, p. 8 (November 2005)
Lin, J., Keogh, E., Lonardi, S., Patel, P.: Finding motifs in time series. In: The 2nd Workshop on Temporal Data Mining, pp. 53–68 (2002)
Minnen, D., Starner, T., Essa, I., Isbell, C.: Improving activity discovery with automatic neighborhood estimation. Int. Joint Conf. on Artificial Intelligence (2007)
Minnen, D., Essa, I., Isbell, C.L., Starner, T.: Detecting Subdimensional Motifs: An Efficient Algorithm for Generalized Multivariate Pattern Discovery. In: IEEE ICDM (2007)
Mohammad, Y., Ohmoto, Y., Nishida, T.: G-steX: Greedy stem extension for free-length constrained motif discovery. In: Jiang, H., Ding, W., Ali, M., Wu, X. (eds.) IEA/AIE 2012. LNCS, vol. 7345, pp. 417–426. Springer, Heidelberg (2012)
Mohammad, Y., Nishida, T.: Constrained motif discovery in time series. New Generation Computing 27(4), 319–346 (2009)
Mohammad, Y., Nishida, T.: Robust singular spectrum transform. In: Chien, B.-C., Hong, T.-P., Chen, S.-M., Ali, M. (eds.) IEA/AIE 2009. LNCS, vol. 5579, pp. 123–132. Springer, Heidelberg (2009)
Mohammad, Y., Nishida, T.: On comparing SSA-based change point discovery algorithms. In: 2011 IEEE/SICE IIS, pp. 938–945 (2011)
Mohammad, Y., Nishida, T., Okada, S.: Unsupervised simultaneous learning of gestures, actions and their associations for human-robot interaction. In: IEEE/RSJ IROS, pp. 2537–2544. IEEE Press, Piscataway (2009)
Mueen, A., Keogh, E., Zhu, Q., Cash, S.: Exact discovery of time series motifs. In: Proc. of 2009 SIAM (2009)
Oates, T.: Peruse: An unsupervised algorithm for finding recurring patterns in time series. In: IEEE ICDM, pp. 330–337 (2002)
Patel, P., Keogh, E., Lin, J., Lonardi, S.: Mining motifs in massive time series databases. In: IEEE ICDM, pp. 370–377 (2002)
Tang, H., Liao, S.S.: Discovering original motifs with different lengths from time series. Know.-Based Syst. 21(7), 666–671 (2008)
Tompa, M., Buhler, J.: Finding motifs using random projections. In: 5th Intl. Conference on Computational Molecular Biology, pp. 67–74 (April 2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mohammad, Y., Nishida, T. (2013). Approximately Recurring Motif Discovery Using Shift Density Estimation. In: Ali, M., Bosse, T., Hindriks, K.V., Hoogendoorn, M., Jonker, C.M., Treur, J. (eds) Recent Trends in Applied Artificial Intelligence. IEA/AIE 2013. Lecture Notes in Computer Science(), vol 7906. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38577-3_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-38577-3_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38576-6
Online ISBN: 978-3-642-38577-3
eBook Packages: Computer ScienceComputer Science (R0)