Abstract
Time series motifs are pairs of previously unknown sequences in a time series database or subsequences of a longer time series which are very similar to each other. Since their formalization in 2002, discovering motifs has been used to solve problems in several application areas. In this paper, we propose a novel approach for discovering approximate motifs in time series. This approach is based on R*-tree and the idea of early abandoning. Our method is time and space efficient because it only saves Minimum Bounding Rectangles (MBR) of data in memory and needs a single scan over the entire time series database and a few times to read the original disk data in order to validate the results. The experimental results showed that our proposed algorithm outperforms the popular method, Random Projection, in efficiency.
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
Beaudoin, P., van de Panne, M., Poulin, P., Coros, S.: Motion-motif graphs. In: Proc. of Symposium on Computer Animation (2008)
Beckman, N., Kriegel, H.P., Schneider, R., Seeger, B.: The R*-tree: An efficient and robust access method for points and rectangles. In: Proc. of 1990 ACM-SIGMOD Conf., Atlantic City, NJ, pp. 322–331 (May 1990)
Buhler, J., Tompa, M.: Finding Motifs Using Random Projections. In: Proc. of the 5th Annual Int. Conf. on Computational Biology, pp. 69–76 (2001)
Castro, N., Azevedo, P.: Multiresolution Motif Discovery in Time Series. In: Proc. of SIAM Int. Conf. on Data Mining, Columbus, Ohio, USA, April 29 - May 1 (2010)
Chiu, B., Keogh, E., Lonardi, S.: Probabilistic discovery of time series motifs. In: Proc. of the 9th Int. Conf. on Knowledge Discovery and Data Mining (KDD 2003), pp. 493–498 (2003)
Ferreira, P., Azevedo, P., Silva, C., Brito, R.: Mining approximate motifs in time series. In: Proc. of the 9th Int. Conf. on Discovery Science, pp. 89–101 (2006)
Gruber, C., Coduro, M., Sick, B.: Signature Verification with Dynamic RBF Networks and Time Series Motifs. In: Proc of 10th Int. Workshop on Frontiers in Handwriting Recognition (2006)
Guttman, A.: R-trees: a dynamic index structure for spatial searching. In: Proc. of ACM SIGMOD Int. Conf. on Management of Data, June 18-21, pp. 47–57 (1984)
Jiang, Y., Li, C., Han, J.: Stock temporal prediction based on time series motifs. In: Proc. of 8th Int. Conf. on Machine Learning and Cybernetics, Baoding, China, July 12-15 (2009)
Lin, J., Keogh, E., Lonardi, S., Patel, P.: Finding motifs in time series. In: Proc. 2nd Workshop on Temporal Data Mining, Edmonton, Alberta, Canada (2002)
Meng, J., Yuan, J., Hans, H., Wu, Y.: Mining Motifs from Human Motion. In: Proc. of EUROGRAPHICS (2008)
Mueen, A., Keogh, E., Zhu, Q., Cash, S., Westover, B.: Exact Discovery of Time Series Motifs. In: Proc. of SIAM Int. Conf. on Data Mining, pp. 473–484 (2009)
Mueen, A., Keogh, E., Bigdely-Shamlo, J.: Finding Time Series Motif in Disk-Resident Data. In: Proc. of 9th Int. Conf. on Data Mining (ICDM 2009), pp. 367–376 (2009)
Tanaka, Y., Iwamoto, K., Uehara, K.: Discovery of Time Series Motif from Multi-Dimensional Data Based on MDL Principle. Machine Learning 58, 269–300 (2005)
Yankov, D., Keogh, E., Medina, J., Chiu, B., Zordan, V.: Detecting Motifs Under Uniform Scaling. In: Proc. of the 13th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 844–853 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Son, N.T., Anh, D.T. (2012). Discovering Time Series Motifs Based on Multidimensional Index and Early Abandoning. In: Nguyen, NT., Hoang, K., Jȩdrzejowicz, P. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2012. Lecture Notes in Computer Science(), vol 7653. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34630-9_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-34630-9_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34629-3
Online ISBN: 978-3-642-34630-9
eBook Packages: Computer ScienceComputer Science (R0)