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Dimension Selection Strategies for Multivariate Time Series Classification with HIVE-COTEv2.0

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Advanced Analytics and Learning on Temporal Data (AALTD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13812))

Abstract

Multivariate time series classification (MTSC) is an area of machine learning that deals with predicting a discrete target variable from multidimensional time dependent data. The possible high dimensionality of multivariate time series can affect the training time and possibly accuracy of complex classifiers, which often scale poorly in dimensions. We explore dimension filtering algorithms for high dimensional MTSC used in conjunction with the state of the art MTSC algorithm, HIVE-COTEv2.0. We apply and adapt recently proposed selection algorithms and propose new methods based on the ROCKET classifier built on single dimensions. We find that, for high dimensional MTSC problems, the best approach can on average filter between \(50\%\) and \(60\%\) of dimensions without significant loss of accuracy, reducing train time by a similar proportion.

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Notes

  1. 1.

    https://github.com/sktime.

  2. 2.

    www.timeseriesclassification.com.

  3. 3.

    http://www.timeseriesclassification.com/description.php?Dataset=PEMS-SF.

  4. 4.

    http://www.timeseriesclassification.com/description.php?Dataset=DuckDuckGeese.

References

  1. Bagnall, A., Flynn, M., Large, J., Lines, J., Middlehurst, M.: On the usage and performance of the hierarchical vote collective of transformation-based ensembles version 1.0 (HIVE-COTE v1.0). In: Lemaire, V., Malinowski, S., Bagnall, A., Guyet, T., Tavenard, R., Ifrim, G. (eds.) AALTD 2020. LNCS (LNAI), vol. 12588, pp. 3–18. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-65742-0_1

    Chapter  Google Scholar 

  2. Bierweiler, T., Labisch, D.: Four-tank batch process in smart automation. Tech. rep. (2021). https://github.com/thomasbierweiler/FaultsOf4-TankBatchProcess

  3. Bostrom, A., Bagnall, A.: Binary shapelet transform for multiclass time series classification. In: Madria, S., Hara, T. (eds.) DaWaK 2015. LNCS, vol. 9263, pp. 257–269. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-22729-0_20

    Chapter  Google Scholar 

  4. Dau, H., et al.: The UCR time series archive. IEEE/CAA J. Autom. Sin. 6(6), 1293–1305 (2019)

    Article  Google Scholar 

  5. Dempster, A., Petitjean, F., Webb, G.I.: ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels. Data Min. Knowl. Disc. 34(5), 1454–1495 (2020). https://doi.org/10.1007/s10618-020-00701-z

    Article  MathSciNet  MATH  Google Scholar 

  6. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)

    MathSciNet  MATH  Google Scholar 

  7. Dhariyal, B., Nguyen, T.L., Ifrim, G.: Fast channel selection for scalable multivariate time series classification. In: Lemaire, V., Malinowski, S., Bagnall, A., Guyet, T., Tavenard, R., Ifrim, G. (eds.) AALTD 2021. LNCS (LNAI), vol. 13114, pp. 36–54. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-91445-5_3

    Chapter  Google Scholar 

  8. Egede, J.O., et al.: Emopain challenge 2020: multimodal pain evaluation from facial and bodily expressions. In: 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), pp. 849–856 (2020)

    Google Scholar 

  9. Kathirgamanathan, B., Buckley, C., Caulfield, B., Cunningham, P.: Feature subset selection for detecting fatigue in runners using time series sensor data. In: El Yacoubi, M., Granger, E., Yuen, P.C., Pal, U., Vincent, N. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2022. LNCS, vol. 13363, pp. 541–552. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-09037-0_44

  10. Kathirgamanathan, B., Cunningham, P.: A feature selection method for multi-dimension time-series data. In: Lemaire, V., Malinowski, S., Bagnall, A., Guyet, T., Tavenard, R., Ifrim, G. (eds.) AALTD 2020. LNCS (LNAI), vol. 12588, pp. 220–231. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-65742-0_15

    Chapter  Google Scholar 

  11. Klami, A.: Proceedings of ICANN/PASCAL2 Challenge: MEG Mind Reading. Tech. rep. (2011). http://urn.fi/URN:ISBN:978-952-60-4456-9

  12. Löning, M., Bagnall, A., Ganesh, S., Kazakov, V., Lines, J., Király, F.J.: A unified interface for machine learning with time series. arXiv preprint arXiv:1909.07872 (2019)

  13. Malekzadeh, M., Clegg, R.G., Cavallaro, A., Haddadi, H.: Mobile sensor data anonymization. In: Proceedings of the International Conference on Internet of Things Design and Implementation, pp. 49–58. IoTDI 2019, ACM, New York (2019). http://doi.acm.org/10.1145/3302505.3310068

  14. Middlehurst, M., Large, J., Cawley, G., Bagnall, A.: The temporal dictionary ensemble (TDE) classifier for time series classification. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds.) ECML PKDD 2020. LNCS (LNAI), vol. 12457, pp. 660–676. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67658-2_38

    Chapter  Google Scholar 

  15. Middlehurst, M., Large, J., Bagnall, A.: The canonical interval forest (CIF) classifier for time series classification. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 188–195. IEEE (2020)

    Google Scholar 

  16. Middlehurst, M., Large, J., Flynn, M., Lines, J., Bostrom, A., Bagnall, A.: HIVE-COTE 2.0: a new meta ensemble for time series classification. Mach. Learn. 110(11), 3211–3243 (2021). https://doi.org/10.1007/s10994-021-06057-9

    Article  MathSciNet  MATH  Google Scholar 

  17. Pasos-Ruiz, A., Flynn, M., Bagnall, A.: Benchmarking multivariate time series classification algorithms. arXiv preprint arXiv:2007.13156 (2020)

  18. Ruiz, A.P., Flynn, M., Large, J., Middlehurst, M., Bagnall, A.: The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min. Knowl. Disc. 35(2), 401–449 (2020). https://doi.org/10.1007/s10618-020-00727-3

    Article  MathSciNet  MATH  Google Scholar 

  19. Satopaa, V., Albrecht, J., Irwin, D., Raghavan, B.: Finding a “kneedle” in a haystack: detecting knee points in system behavior. In: Proceedings of 31st International Conference on Distributed Computing Systems Workshops, pp. 166–171 (2011)

    Google Scholar 

  20. Yang, K., Yoon, H., Shahabi, C.: CLe Ver: a feature subset selection technique for multivariate time series. In: Ho, T.B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 516–522. Springer, Heidelberg (2005). https://doi.org/10.1007/11430919_60

    Chapter  Google Scholar 

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Authors and Affiliations

  1. School of Computing Sciences, University of East Anglia, Norwich, UK

    Alejandro Pasos Ruiz & Anthony Bagnall

Authors
  1. Alejandro Pasos Ruiz
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  2. Anthony Bagnall
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Corresponding author

Correspondence to Anthony Bagnall .

Editor information

Editors and Affiliations

  1. Inria Grenoble - Rhône-Alpes Research Centre, Villeurbanne, France

    Thomas Guyet

  2. University College Dublin, Dublin, Ireland

    Georgiana Ifrim

  3. University of Rennes, Rennes, France

    Simon Malinowski

  4. University of East Anglia, Norwich, UK

    Anthony Bagnall

  5. University of Rennes, Rennes, France

    Patrick Shafer

  6. Orange Labs, Lannion, France

    Vincent Lemaire

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Ruiz, A.P., Bagnall, A. (2023). Dimension Selection Strategies for Multivariate Time Series Classification with HIVE-COTEv2.0. In: Guyet, T., Ifrim, G., Malinowski, S., Bagnall, A., Shafer, P., Lemaire, V. (eds) Advanced Analytics and Learning on Temporal Data. AALTD 2022. Lecture Notes in Computer Science(), vol 13812. Springer, Cham. https://doi.org/10.1007/978-3-031-24378-3_9

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  • DOI: https://doi.org/10.1007/978-3-031-24378-3_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-24377-6

  • Online ISBN: 978-3-031-24378-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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