Data Mining and Knowledge Discovery

, Volume 30, Issue 5, pp 1053–1085 | Cite as

Generalized random shapelet forests

  • Isak Karlsson
  • Panagiotis Papapetrou
  • Henrik Boström
Article

Abstract

Shapelets are discriminative subsequences of time series, usually embedded in shapelet-based decision trees. The enumeration of time series shapelets is, however, computationally costly, which in addition to the inherent difficulty of the decision tree learning algorithm to effectively handle high-dimensional data, severely limits the applicability of shapelet-based decision tree learning from large (multivariate) time series databases. This paper introduces a novel tree-based ensemble method for univariate and multivariate time series classification using shapelets, called the generalized random shapelet forest algorithm. The algorithm generates a set of shapelet-based decision trees, where both the choice of instances used for building a tree and the choice of shapelets are randomized. For univariate time series, it is demonstrated through an extensive empirical investigation that the proposed algorithm yields predictive performance comparable to the current state-of-the-art and significantly outperforms several alternative algorithms, while being at least an order of magnitude faster. Similarly for multivariate time series, it is shown that the algorithm is significantly less computationally costly and more accurate than the current state-of-the-art.

Keywords

Multivariate time series Time series classification Time series shapelets Decision trees Ensemble methods 

References

  1. Bagnall A, Lines J (2014) An experimental evaluation of nearest neighbour time series classification. CoRR arXiv:1406.4757
  2. Bankó Z (2012) Correlation based dynamic time warping of multivariate time series. Expert Syst Appl 39(17):12814–12823CrossRefGoogle Scholar
  3. Batista GE, Wang X, Keogh EJ (2011) A complexity-invariant distance measure for time series. In: Proceedings of SIAM, SIAM international conference on data mining, pp 699–710Google Scholar
  4. Baydogan MG, Runger G (2014) Learning a symbolic representation for multivariate time series classification. Data Min Knowl Discov 29(2):400–422MathSciNetCrossRefGoogle Scholar
  5. Baydogan MG, Runger G (2015) Time series representation and similarity based on local autopatterns. Data Min Knowl Discov 30(2):1–34MathSciNetGoogle Scholar
  6. Baydogan MG, Runger G, Tuv E (2013) A bag-of-features framework to classify time series. IEEE Trans Pattern Anal Mach Intell 35(11):2796–2802CrossRefGoogle Scholar
  7. Berndt DJ, Clifford J (1994) Using dynamic time warping to find patterns in time series. In: KDD workshop, knowledge discovery and data mining, pp 359–370Google Scholar
  8. Boström H (2011) Concurrent learning of large-scale random forests. In: Proceedings of the Scandinavian conference on artificial intelligence, pp 20–29Google Scholar
  9. Boström H (2012) Forests of probability estimation trees. Int J Pattern Recognit Artif Intell 26(02):125–147MathSciNetCrossRefGoogle Scholar
  10. Bradley AP (1997) The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognit 30(7):1145–1159CrossRefGoogle Scholar
  11. Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140MathSciNetMATHGoogle Scholar
  12. Breiman L (2001) Random forests. Mach Learn 45(1):5–32MathSciNetCrossRefMATHGoogle Scholar
  13. Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees. CRC Press, Boca RatonMATHGoogle Scholar
  14. Cetin MS, Mueen A, Calhoun VD (2015) Shapelet ensemble for multi-dimensional time series. In: Proceedings of SIAM international conference on data mining, SIAM, pp 307–315Google Scholar
  15. Chen L, Ng R (2004) On the marriage of \(l_p\)-norms and edit distance. In: Proceedings of the international conference on very large data bases, ACM, pp 792–803Google Scholar
  16. Chen L, Özsu MT (2005) Robust and fast similarity search for moving object trajectories. In: Proceedings of the ACM SIGMOD international conference on management of data, ACM, pp 491–502Google Scholar
  17. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297MATHGoogle Scholar
  18. Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30MathSciNetMATHGoogle Scholar
  19. Deng H, Runger G, Tuv E, Vladimir M (2013) A time series forest for classification and feature extraction. Inf Sci 239:142–153MathSciNetCrossRefMATHGoogle Scholar
  20. Ding H, Trajcevski G, Scheuermann P, Wang X, Keogh E (2008) Querying and mining of time series data: experimental comparison of representations and distance measures. Proc VLDB Endow 1(2):1542–1552CrossRefGoogle Scholar
  21. Friedman JH (1997) On bias, variance, 0/1–loss, and the curse-of-dimensionality. Data Min Knowl Discov 1(1):55–77CrossRefGoogle Scholar
  22. Fulcher BD, Jones NS (2014) Highly comparative feature-based time-series classification. IEEE Trans Knowl Data Eng 26(12):3026–3037CrossRefGoogle Scholar
  23. Gordon D, Hendler D, Rokach L (2012) Fast randomized model generation for shapelet-based time series classification. arXiv:1209.5038
  24. Grabocka J, Schilling N, Wistuba M, Schmidt-Thieme L (2014) Learning time-series shapelets. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 392–401Google Scholar
  25. Hills J, Lines J, Baranauskas E, Mapp J, Bagnall A (2014) Classification of time series by shapelet transformation. Data Min Knowl Discov 28(4):851–881MathSciNetCrossRefMATHGoogle Scholar
  26. Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8):832–844CrossRefGoogle Scholar
  27. Hu B, Chen Y, Keogh EJ (2013) Time series classification under more realistic assumptions. In: Proceedings of SIAM international conference on data mining, SIAM, pp 578–586Google Scholar
  28. James GM (2003) Variance and bias for general loss functions. Mach Learn 51(2):115–135CrossRefMATHGoogle Scholar
  29. Kampouraki A, Manis G, Nikou C (2009) Heartbeat time series classification with support vector machines. Inf Technol Biomed 13(4):512–518CrossRefGoogle Scholar
  30. Karlsson I, Papapetrou P, Boström H (2015) Forests of randomized shapelet trees. In: Proceedings of statistical learning and data sciences, Springer, pp 126–136Google Scholar
  31. Keogh E, Zhu Q, Hu B, Y H, Xi X, Wei L, Ratanamahatana CA (2015) The ucr time series classification/clustering homepage. www.cs.ucr.edu/~eamonn/time_series_data/
  32. Lines J, Bagnall A (2014) Time series classification with ensembles of elastic distance measures. Data Min Knowl Discov 29(3):565–592MathSciNetCrossRefGoogle Scholar
  33. 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
  34. Maier D (1978) The complexity of some problems on subsequences and supersequences. J ACM 25(2):322–336MathSciNetCrossRefMATHGoogle Scholar
  35. Mueen A, Keogh E, Young N (2011) Logical-shapelets: an expressive primitive for time series classification. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 1154–1162Google Scholar
  36. Nanopoulos A, Alcock R, Manolopoulos Y (2001) Feature-based classification of time-series data. Int J Comput Res 10:49–61Google Scholar
  37. Patri OP, Sharma AB, Chen H, Jiang G, Panangadan AV, Prasanna VK (2014) Extracting discriminative shapelets from heterogeneous sensor data. In: Proceedings of IEEE international conference on big data, IEEE, pp 1095–1104Google Scholar
  38. Quinlan JR (1993) C4.5: programs for machine learning. Elsevier, AmsterdamGoogle Scholar
  39. Rakthanmanon T, Keogh E (2013) Fast shapelets: a scalable algorithm for discovering time series shapelets. In: Proceedings of SIAM international conference on data mining, SIAMGoogle Scholar
  40. Ratanamahatana CA, Keogh E (2004) Everything you know about dynamic time warping is wrong. In: 3rd workshop on mining temporal and sequential data, pp 22–25Google Scholar
  41. Rebbapragada U, Protopapas P, Brodley CE, Alcock C (2009) Finding anomalous periodic time series. Mach Learn 74(3):281–313CrossRefGoogle Scholar
  42. Rodríguez JJ, Alonso CJ (2004) Interval and dynamic time warping-based decision trees. In: Proceedings of the 2004 ACM Symposium on applied computing, ACM, pp 548–552Google Scholar
  43. Rodríguez JJ, Alonso CJ, Maestro JA (2005) Support vector machines of interval-based features for time series classification. Knowl Based Syst 18(4):171–178CrossRefGoogle Scholar
  44. Sakoe H, Chiba S (1978) Dynamic programming algorithm optimization for spoken word recognition. In: Transactions on ASSP, IEEE, pp 43–49Google Scholar
  45. Schmidhuber J (2014) Deep learning in neural networks: an overview. arXiv:1404.7828
  46. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423MathSciNetCrossRefMATHGoogle Scholar
  47. Shokoohi-Yekta M, Wang J, Keogh E (2015) On the non-trivial generalization of dynamic time warping to the multi-dimensional case. In: Proceedings of SIAM international conference on data mining, SIAM, pp 289–297Google Scholar
  48. Valentini G, Dietterich TG (2004) Bias-variance analysis of support vector machines for the development of svm-based ensemble methods. J Mach Learn Res 5:725–775MathSciNetMATHGoogle Scholar
  49. Wang X, Mueen A, Ding H, Trajcevski G, Scheuermann P, Keogh E (2013) Experimental comparison of representation methods and distance measures for time series data. Data Min Knowl Discov 26(2):275–309MathSciNetCrossRefGoogle Scholar
  50. Wistuba M, Grabocka J, Schmidt-Thieme L (2015) Ultra-fast shapelets for time series classification. CoRR arXiv:1503.05018
  51. Wu Y, Chang EY (2004) Distance-function design and fusion for sequence data. In: Proceedings of ACM international conference on information and knowledge management, ACM, pp 324–333Google Scholar
  52. Xi X, Keogh E, Shelton C, Wei L, Ratanamahatana CA (2006) Fast time series classification using numerosity reduction. In: Proceedings of the 23rd international conference on machine learning, ACM, pp 1033–1040Google Scholar
  53. Ye L, Keogh E (2009) Time series shapelets: a new primitive for data mining. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 947–956Google Scholar
  54. Ye L, Keogh E (2011) Time series shapelets: a novel technique that allows accurate, interpretable and fast classification. Data Min Knowl Discov 22(1–2):149–182MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© The Author(s) 2016

Authors and Affiliations

  1. 1.Stockholm UniversityStockholmSweden

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