Advertisement

Data Mining and Knowledge Discovery

, Volume 32, Issue 5, pp 1200–1228 | Cite as

Exploring variable-length time series motifs in one hundred million length scale

  • Yifeng Gao
  • Jessica Lin
Article
  • 259 Downloads
Part of the following topical collections:
  1. Journal Track of ECML PKDD 2018

Abstract

The exploration of repeated patterns with different lengths, also called variable-length motifs, has received a great amount of attention in recent years. However, existing algorithms to detect variable-length motifs in large-scale time series are very time-consuming. In this paper, we introduce a time- and space-efficient approximate variable-length motif discovery algorithm, Distance-Propagation Sequitur (DP-Sequitur), for detecting variable-length motifs in large-scale time series data (e.g. over one hundred million in length). The discovered motifs can be ranked by different metrics such as frequency or similarity, and can benefit a wide variety of real-world applications. We demonstrate that our approach can discover motifs in time series with over one hundred million points in just minutes, which is significantly faster than the fastest existing algorithm to date. We demonstrate the superiority of our algorithm over the state-of-the-art using several real world time series datasets.

Keywords

Time series data mining Motif discovery Variable length 

References

  1. Athanas N. Xc22831. Accessible at www.xeno-canto.org/22831. Accessed 11 Aug 2008
  2. Begum N, Keogh E (2014) Rare time series motif discovery from unbounded streams. Proc VLDB Endow 8(2):149–160CrossRefGoogle Scholar
  3. Bob P, Willem-Pier V, Sander P, Jonathon J (2005) Xeno-Canto. www.xeno-canto.org. Accessed 30 May 2005
  4. Boesman P. Xc221161. Accessible at www.xeno-canto.org/221161
  5. Calderon-F D. Xc301107. Accessible at www.xeno-canto.org/301107. Accessed 13 Dec 2015
  6. Castro N, Azevedo PJ (2010) Multiresolution motif discovery in time series. In: Proceedings of the 2010 SIAM international conference on data mining. SIAM, pp 665–676Google Scholar
  7. Chiu B, Keogh E, Lonardi S (2003) Probabilistic discovery of time series motifs. In: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 493–498Google Scholar
  8. Gao Y, Lin J, Rangwala H (2016) Iterative grammar-based framework for discovering variable-length time series motifs. In: 15th IEEE international conference on machine learning and applications (ICMLA). IEEE, pp 7–12Google Scholar
  9. Gao Y, Li Q, Li X, Lin J, Rangwala H (2017) Trajviz: a tool for visualizing patterns and anomalies in trajectory. In: Joint European conference on machine learning and knowledge discovery in databases. Springer, pp 428–431Google Scholar
  10. Giancarlo R, Scaturro D, Utro F (2009) Textual data compression in computational biology: a synopsis. Bioinformatics 25(13):1575–1586CrossRefzbMATHGoogle Scholar
  11. Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE (2000) Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220CrossRefGoogle Scholar
  12. Hughes JF, Skaletsky H, Pyntikova T, Graves TA, van Daalen SK, Minx PJ, Fulton RS, McGrath SD, Locke DP, Friedman C et al (2010) Chimpanzee and human Y chromosomes are remarkably divergent in structure and gene content. Nature 463(7280):536CrossRefGoogle Scholar
  13. Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, Haussler D (2002) The human genome browser at UCSC. Genome Res 12(6):996–1006CrossRefGoogle Scholar
  14. Keogh E, Lonardi S, Zordan VB, Lee SH, Jara M (2005a) Visualizing the similarity of human and chimp DNA (multimedia video). http://www.cs.ucr.edu/~eamonn/DNA/
  15. Keogh E, Lin J, Fu A (2005b) Hot sax: efficiently finding the most unusual time series subsequence. In: 2005 IEEE 5th international conference on data mining (ICDM), p 8Google Scholar
  16. Krabbe N. Xc235579. Accessible at www.xeno-canto.org/235579
  17. Li Y, Lin J, Oates T (2012) Visualizing variable-length time series motifs. In: Proceedings of the 2012 SIAM international conference on data mining. SIAM, pp 895–906Google Scholar
  18. Li Y, Yiu ML, Gong Z, et al. (2015) Quick-motif: an efficient and scalable framework for exact motif discovery. In: 2015 IEEE 31st international conference on data engineering (ICDE). IEEE, pp 579–590Google Scholar
  19. Lin J, Keogh E, Lonardi S, Lankford JP, Nystrom DM (2004) Visually mining and monitoring massive time series. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 460–469Google Scholar
  20. Lin J, Keogh E, Wei L, Lonardi S (2007) Experiencing sax: a novel symbolic representation of time series. Data Min Knowl Discov 15(2):107–144MathSciNetCrossRefGoogle Scholar
  21. Lines J, Davis LM, Hills J, Bagnall A (2012) A shapelet transform for time series classification. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 289–297Google Scholar
  22. Liu B, Li J, Chen C, Tan W, Chen Q, Zhou M (2015) Efficient motif discovery for large-scale time series in healthcare. IEEE Trans Ind Inform 11(3):583–590CrossRefGoogle Scholar
  23. Locke DP, Hillier LW, Warren WC, Worley KC, Nazareth LV, Muzny DM, Yang S-P, Wang Z, Chinwalla AT, Minx P et al (2011) Comparative and demographic analysis of orang-utan genomes. Nature 469(7331):529CrossRefGoogle Scholar
  24. Mohammad Y, Nishida T (2009) Constrained motif discovery in time series. New Gener Comput 27(4):319–346CrossRefzbMATHGoogle Scholar
  25. Mohammad Y, Nishida T (2014a) Exact discovery of length-range motifs. In: Intelligent information and database systems. Springer, pp 23–32Google Scholar
  26. Mohammad Y, Nishida T (2014b) Scale invariant multi-length motif discovery. In: Modern advances in applied intelligence. Springer, pp 417–426Google Scholar
  27. Mueen A (2013) Enumeration of time series motifs of all lengths. In: 2013 IEEE 13th international conference on data mining (ICDM). IEEE, pp 547–556Google Scholar
  28. Mueen A, Keogh E (2010) Online discovery and maintenance of time series motifs. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 1089–1098Google Scholar
  29. Mueen A, Keogh EJ, Zhu Q, Cash S, Westover MB (2009) Exact discovery of time series motifs. In: Proceedings of the 2009 SIAM international conference on data mining. SIAM, pp. 473–484Google Scholar
  30. Mueen A, Viswanathan K, Gupta C, Keogh E (2015) The fastest similarity search algorithm for time series subsequences under Euclidean distance. http://www.cs.unm.edu/~mueen/FastestSimilaritySearch.html
  31. Murray D, Liao J, Stankovic L, Stankovic V, Hauxwell-Baldwin R, Wilson C, Coleman M, Kane T, Firth S (2015) A data management platform for personalised real-time energy feedback. In: Proceedings of the 8th international conference on energy efficiency in domestic appliances and lighting, pp 1–15Google Scholar
  32. Nevill-Manning CG, Witten IH (1997) Identifying hierarchical strcture in sequences: a linear-time algorithm. J Artif Intell Res (JAIR) 7:67–82CrossRefzbMATHGoogle Scholar
  33. Nunthanid P, Niennattrakul V, Ratanamahatana CA (2011) Discovery of variable length time series motif. In: 2011 8th international conference on electrical engineering/electronics, computer, telecommunications and information technology (ECTI-CON). IEEE, pp 472–475Google Scholar
  34. Patel P, Keogh E, Jessica L, Lonardi S (2002) Mining motifs in massive time series databases. In: 2003 proceedings of the 2002 IEEE international conference on data mining (ICDM). IEEE, pp 370–377Google Scholar
  35. Rakthanmanon T, Campana B, Mueen A, Batista G, Westover B, Zhu Q, Zakaria J, Keogh E (2012) Searching and mining trillions of time series subsequences under dynamic time warping. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 262–270Google Scholar
  36. Senin P, Malinchik S (2013) Sax-vsm: Interpretable time series classification using sax and vector space model. In: 2013 IEEE 13th international conference on data mining (ICDM). IEEE, pp 1175–1180Google Scholar
  37. Senin P, Lin J, Wang X, Oates T, Gandhi S, Boedihardjo AP, Chen C, Frankenstein S, Lerner M (2014) Grammarviz 2.0: a tool for grammar-based pattern discovery in time series. In: Machine learning and knowledge discovery in databases. Springer, pp 468–472Google Scholar
  38. Shieh J, Keogh E (2009) iSAX: disk-aware mining and indexing of massive time series datasets. Data Min Knowl Discov 19(1):24–57MathSciNetCrossRefGoogle Scholar
  39. Shokoohi-Yekta M, Chen Y, Campana B, Hu B, Zakaria J, Keogh E (2015) Discovery of meaningful rules in time series. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1085–1094Google Scholar
  40. Skaletsky H, Kuroda-Kawaguchi T, Minx PJ, Cordum HS, Hillier L, Brown LG, Repping S, Pyntikova T, Ali J, Bieri T et al (2003) The male-specific region of the human Y chromosome is a mosaic of discrete sequence classes. Nature 423(6942):825–837CrossRefGoogle Scholar
  41. Tang H, Liao SS (2008) Discovering original motifs with different lengths from time series. Knowl Based Syst 21(7):666–671CrossRefGoogle Scholar
  42. Wang X, Lin J, Senin P, Oates T, Gandhi S, Boedihardjo AP, Chen C, Frankenstein S (2016) RPM: Representative pattern mining for efficient time series classification. In: 19th international conference on extending database technology (EDBT), pp 185–196Google Scholar
  43. Yeh C-CM, Zhu Y, Ulanova L, Begum N, Ding Y, Dau HA, Silva DF, Mueen A, Keogh E (2016) Matrix profile i: All pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In: 2016 IEEE 16th international conference on data mining (ICDM), pp 1317–1322Google Scholar
  44. Zhu Y, Schall-Zimmerman Z, Senobari NS, Yeh C-CM, Funning G, Mueen A, Brisk P, Keogh EJ (2016) Matrix profile ii: exploiting a novel algorithm and gpus to break the one hundred million barrier for time series motifs and joins. In: 2016 IEEE 16th international conference on data mining (ICDM), pp 739–748Google Scholar

Copyright information

© The Author(s) 2018

Authors and Affiliations

  1. 1.George Mason UniversityFairfaxUSA

Personalised recommendations