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FastEE: Fast Ensembles of Elastic Distances for time series classification

  • Chang Wei TanEmail author
  • François Petitjean
  • Geoffrey I. Webb
Article
  • 74 Downloads

Abstract

In recent years, many new ensemble-based time series classification (TSC) algorithms have been proposed. Each of them is significantly more accurate than their predecessors. The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is currently the most accurate TSC algorithm when assessed on the UCR repository. It is a meta-ensemble of 5 state-of-the-art ensemble-based classifiers. The time complexity of HIVE-COTE—particularly for training—is prohibitive for most datasets. There is thus a critical need to speed up the classifiers that compose HIVE-COTE. This paper focuses on speeding up one of its components: Ensembles of Elastic Distances (EE), which is the classifier that leverages on the decades of research into the development of time-dedicated measures. Training EE can be prohibitive for many datasets. For example, it takes a month on the ElectricDevices dataset with 9000 instances. This is because EE needs to cross-validate the hyper-parameters used for the 11 similarity measures it encompasses. In this work, Fast Ensembles of Elastic Distances is proposed to train EE faster. There are two versions to it. The exact version makes it possible to train EE 10 times faster. The approximate version is 40 times faster than EE without significantly impacting the classification accuracy. This translates to being able to train EE on ElectricDevices in 13 h.

Keywords

Time series classification Scalable Similarity measures Ensembles 

Notes

Acknowledgements

This research was supported by the Australian Research Council under Grant DP190100017. François Petitjean is the recipient of an Australian Research Council Discovery Early Career Award (Project Number DE170100037) funded by the Australian Government. This material is based upon work supported by the Air Force Office of Scientific Research, Asian Office of Aerospace Research and Development (AOARD) under award number FA2386-18-1-4030. The authors would like to acknowledge the use of the UCR Time Series Classification archive that is made publicly available for time series classification benchmarks. We also would like to acknowledge the use of the source code for Ensemble of Elastic Distances that is freely available at http://www.timeseriesclassification.com/.

References

  1. Bagnall A, Lines J (2014) An experimental evaluation of nearest neighbour time series classification. technical report# cmp-c14-01. Department of Computing Sciences, University of East Anglia, Technical ReportGoogle Scholar
  2. Bagnall A, Lines J, Hills J, Bostrom A (2015) Time-series classification with COTE: the collective of transformation-based ensembles. IEEE Trans Knowl Data Eng 27(9):2522–2535CrossRefGoogle Scholar
  3. Bagnall A, Lines J, Bostrom A, Large J, Keogh E (2017) The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min Knowl Discov 31(3):606–660MathSciNetCrossRefGoogle Scholar
  4. Boreczky JS, Rowe LA (1996) Comparison of video shot boundary detection techniques. J Electron Imaging 5(2):122–129CrossRefGoogle Scholar
  5. Chen L, Ng R (2004) On the marriage of Lp-norms and edit distance. In: Proceedings of the 30th international conference on very large databases (VLDB), pp 792–803CrossRefGoogle Scholar
  6. Chen L, Özsu MT, Oria V (2005) Robust and fast similarity search for moving object trajectories. In: Proceedings of the 2005 ACM SIGMOD international conference on management of data (SIGMOD), pp 491–502Google Scholar
  7. Chen Y, Keogh E, Hu B, Begum N, Bagnall A, Mueen A, Batista G (2015) The UCR time series classification archive. www.cs.ucr.edu/~eamonn/time_series_data/
  8. Dau H, Silva D, Petitjean F, Bagnall A, Keogh E (2017) Judicious setting of dynamic time warping’s window width allows more accurate classification of time series. In: Proceedings of the 2017 IEEE international conference on big data (Big Data), pp 917–922Google Scholar
  9. Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30MathSciNetzbMATHGoogle Scholar
  10. 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. In: Proceedings of the 34th international conference on very large data bases (VLDB), pp 1542–1552CrossRefGoogle Scholar
  11. Flynn M, Large J, Bagnall T (2019) The contract random interval spectral ensemble (c-RISE): the effect of contracting a classifier on accuracy. In: Proceedings of 2019 international conference on hybrid artificial intelligence systems (HAIS), pp 381–392Google Scholar
  12. Hills J, Lines J, Baranauskas E, Mapp J, Bagnall A (2014) Classification of time series by shapelet transformation. Data Min Knowl Discov 28(4):851–881MathSciNetCrossRefGoogle Scholar
  13. Inglada J, Arias M, Tardy B, Hagolle O, Valero S, Morin D, Dedieu G, Sepulcre G, Bontemps S, Defourny P, Koetz B (2015) Assessment of an operational system for crop type map production using high temporal and spatial resolution satellite optical imagery. Remote Sens 7(9):12356–12379CrossRefGoogle Scholar
  14. Inglada J, Vincent A, Arias M, Marais-Sicre C (2016) Improved early crop type identification by joint use of high temporal resolution sar and optical image time series. Remote Sens 8(5):362CrossRefGoogle Scholar
  15. Itakura F (1975) Minimum prediction residual principle applied to speech recognition. IEEE Trans Acoust Speech Signal Process 23(1):67–72CrossRefGoogle Scholar
  16. Jeong YS, Jeong MK, Omitaomu OA (2011) Weighted dynamic time warping for time series classification. Pattern Recogn 44(9):2231–2240CrossRefGoogle Scholar
  17. Keogh E, Ratanamahatana C (2005) Exact indexing of dynamic time warping. Knowl Inf Syst 7(3):358–386CrossRefGoogle Scholar
  18. Keogh EJ, Pazzani MJ (2001) Derivative dynamic time warping. In: Proceedings of the 2001 SIAM international conference on data mining (SDM), pp 1–11Google Scholar
  19. Kim SW, Park S, Chu WW (2001) An index-based approach for similarity search supporting time warping in large sequence databases. In: Proceedings of the 17th international conference on data engineering (ICDE), pp 607–614Google Scholar
  20. Lemire D (2009) Faster retrieval with a two-pass dynamic-time-warping lower bound. Pattern Recogn 42(9):2169–2180CrossRefGoogle Scholar
  21. Lines J, Bagnall A (2015) Time series classification with ensembles of elastic distance measures. Data Min Knowl Discov 29(3):565–592MathSciNetCrossRefGoogle Scholar
  22. Lines J, Taylor S, Bagnall A (2016) HIVE-COTE: The hierarchical vote collective of transformation-based ensembles for time series classification. In: Proceedings of the 16th IEEE international conference on data mining (ICDM), pp 1041–1046Google Scholar
  23. Lucas B, Shifaz A, Pelletier C, O’Neill L, Zaidi N, Goethals B, Petitjean F, Webb GI (2019) Proximity forest: an effective and scalable distance-based classifier for time series. Data Min Knowl Discov 33(3):607–635CrossRefGoogle Scholar
  24. Marteau PF (2009) Time warp edit distance with stiffness adjustment for time series matching. IEEE Trans Pattern Anal Mach Intell 31(2):306–318CrossRefGoogle Scholar
  25. Petitjean F, Inglada J, Gançarski P (2012) Satellite image time series analysis under time warping. IEEE Trans Geosci Remote Sens 50(8):3081–3095CrossRefGoogle Scholar
  26. Petitjean F, Forestier G, Webb GI, Nicholson AE, Chen Y, Keogh E (2014) Dynamic time warping averaging of time series allows faster and more accurate classification. In: Proceedings of the 2014 IEEE international conference on data mining (ICDM), pp 470–479Google Scholar
  27. 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 (SIGKDD), pp 262–270Google Scholar
  28. Ratanamahatana C, Keogh E (2005) Three myths about DTW data mining. In: Proceedings of the 2005 SIAM international conference on data mining (SDM), pp 506–510Google Scholar
  29. Ratanamahatana CA, Keogh E (2004) Making time-series classification more accurate using learned constraints. In: Proceedings of the 2004 SIAM international conference on data mining, pp 11–22Google Scholar
  30. Sakoe H, Chiba S (1971) A dynamic programming approach to continuous speech recognition. In: Proceedings of the 7th international congress on acoustics, Budapest, Hungary, vol 3, pp 65–69Google Scholar
  31. Sakoe H, Chiba S (1978) Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans Acoust Speech Signal Process 26(1):43–49CrossRefGoogle Scholar
  32. Shen Y, Chen Y, Keogh E, Jin H (2018) Accelerating time series searching with large uniform scaling. In: Proceedings of the 2018 SIAM international conference on data mining (SDM), pp 234–242CrossRefGoogle Scholar
  33. Silva D, Batista G (2016) Speeding up all-pairwise dynamic time warping matrix calculation. In: Proceedings of the 2016 SIAM international conference on data mining (SDM), pp 837–845Google Scholar
  34. Srikanthan S, Kumar A, Gupta R (2011) Implementing the dynamic time warping algorithm in multithreaded environments for real time and unsupervised pattern discovery. In: Proceedings of the 2nd international conference on computer and communication technology (ICCCT), pp 394–398Google Scholar
  35. Stefan A, Athitsos V, Das G (2013) The move-split-merge metric for time series. IEEE Trans Knowl Data Eng 25(6):1425–1438CrossRefGoogle Scholar
  36. Tan CW, Webb GI, Petitjean F (2017) Indexing and classifying gigabytes of time series under time warping. In: Proceedings of the 2017 SIAM international conference on data mining (SDM), pp 282–290CrossRefGoogle Scholar
  37. Tan CW, Herrmann M, Forestier G, Webb GI, Petitjean F (2018) Efficient search of the best warping window for dynamic time warping. In: Proceedings of the 2018 SIAM international conference on data mining (SDM), pp 225–233CrossRefGoogle Scholar
  38. Tan CW, Petitjean F, Webb GI (2019) Elastic bands across the path: a new framework and methods to lower bound DTW. In: Proceedings of the 2019 SIAM international conference on data mining (SDM), pp 522–530CrossRefGoogle Scholar
  39. Vlachos M, Kollios G, Gunopulos D (2002) Discovering similar multidimensional trajectories. In: Proceedings of the 18th international conference on data engineering (ICDE), pp 673–684Google Scholar
  40. Vlachos M, Hadjieleftheriou M, Gunopulos D, Keogh E (2003) Indexing multi-dimensional time-series with support for multiple distance measures. In: Proceedings of the 9th ACM SIGKDD international conference on knowledge discovery and data mining (SIGKDD), pp 216–225Google Scholar
  41. Yi BK, Jagadish H, Faloutsos C (1998) Efficient retrieval of similar time sequences under time warping. In: Proceedings of the 14th international conference on data engineering (ICDE), pp 201–208Google Scholar

Copyright information

© The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Information Technology, 25 Exhibition WalkMonash UniversityMelbourneAustralia

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