Abstract
Multivariate time series classification is a rapidly growing research field with practical applications in finance, healthcare, engineering, and more. The complexity of classifying multivariate time series data arises from its high dimensionality, temporal dependencies, and varying lengths. This paper introduces a novel ensemble classifier called RED CoMETS (Random Enhanced Co-eye for Multivariate Time Series), which addresses these challenges. RED CoMETS builds upon the success of Co-eye, an ensemble classifier specifically designed for symbolically represented univariate time series, and extends its capabilities to handle multivariate data. The performance of RED CoMETS is evaluated on benchmark datasets from the UCR archive, where it demonstrates competitive accuracy when compared to state-of-the-art techniques in multivariate settings. Notably, it achieves the highest reported accuracy in the literature for the ‘HandMovementDirection’ dataset. Moreover, the proposed method significantly reduces computation time compared to Co-eye, making it an efficient and effective choice for multivariate time series classification.
L.A. Bennett—This author was with the University of Bristol while this research was undertaken but is currently affiliated with Awerian.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abdallah, Z.S., Gaber, M.M.: Co-eye: a multi-resolution ensemble classifier for symbolically approximated time series. Mach. Learn. 109(11), 2029–2061 (2020). https://doi.org/10.1007/s10994-020-05887-3
Bagnall, A., Lines, J., Bostrom, A., Large, J., Keogh, E.: The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min. Knowl. Discov. 31(3), 606–660 (2017). https://doi.org/10.1007/s10618-016-0483-9
Bagnall, A., Keogh, E., Lines, J., Bostrom, A., Large, J., Middlehurst, M.: UEA & UCR Time Series Classification Repository. www.timeseriesclassification.com
Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018)
Baydogan, M., Runger, G.: Learning a symbolic representation for multivariate time series classification. Data Min. Knowl. Discov. 29, 1–23 (2014). https://doi.org/10.1007/s10618-014-0349-y
Benavoli, A., Corani, G., Mangili, F.: Should we really use post-hoc tests based on mean-ranks? J. Mach. Learn. Res. 17(5), 1–10 (2016). http://jmlr.org/papers/v17/benavoli16a.html
Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. (2012)
Burman, P.: A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods. Biometrika 76(3), 503 (1989). https://doi.org/10.2307/2336116
Dempster, A., Petitjean, F., Webb, G.I.: ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels. Data Min. Knowl. Discov. 34(5), 1454–1495 (2020). https://doi.org/10.1007/s10618-020-00701-z
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7(1), 1–30 (2006). http://jmlr.org/papers/v7/demsar06a.html
Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., Muller, P.A.: Deep learning for time series classification: a review. Data Min. Knowl. Discov. 33(4), 917–963 (2019). https://doi.org/10.1007/s10618-019-00619-1
Ismail Fawaz, H., et al.: InceptionTime: finding AlexNet for time series classification. Data Min. Knowl. Discov. 34(6), 1936–1962 (2020). https://doi.org/10.1007/s10618-020-00710-y
Karim, F., Majumdar, S., Darabi, H., Harford, S.: Multivariate LSTM-FCNs for time series classification. Neural Netw. 116, 237–245 (2019)
Keogh, E.J., Pazzani, M.J.: Scaling up dynamic time warping for datamining applications. In: Proceedings of the sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’00, pp. 285–289. ACM Press, New York (2000). https://doi.org/10.1145/347090.347153
Large, J., Lines, J., Bagnall, A.: A probabilistic classifier ensemble weighting scheme based on cross-validated accuracy estimates. Data Min. Knowl. Discov. 33(6), 1674–1709 (2019). https://doi.org/10.1007/s10618-019-00638-y
Le Nguyen, T., Gsponer, S., Ilie, I., O’Reilly, M., Ifrim, G.: Interpretable time series classification using linear models and multi-resolution multi-domain symbolic representations. Data Min. Knowl. Discov. 33(4), 1183–1222 (2019). https://doi.org/10.1007/s10618-019-00633-3
Lin, J., Keogh, E., Wei, L., Lonardi, S.: Experiencing SAX: a novel symbolic representation of time series. Data Min. Knowl. Discov. 15(2), 107–144 (2007). https://doi.org/10.1007/s10618-007-0064-z
Lines, J., Taylor, S., Bagnall, A.: Hive-cote: the hierarchical vote collective of transformation-based ensembles for time series classification. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 1041–1046 (2016). https://doi.org/10.1109/ICDM.2016.0133
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
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. Discov. 35(2), 401–449 (2021). https://doi.org/10.1007/s10618-020-00727-3
Schäfer, P., Högqvist, M.: SFA: A symbolic Fourier approximation and index for similarity search in high dimensional datasets. In: Proceedings of the 15th International Conference on Extending Database Technology - EDBT ’12, p. 516. ACM Press, New York, USA (2012). https://doi.org/10.1145/2247596.2247656
Ho, T.K.: Random decision forests. In: Proceedings of 3rd International Conference on Document Analysis and Recognition, pp. 278–282. IEEE Computer Society Press (1995). https://doi.org/10.1109/ICDAR.1995.598994
Tuncel, K., Baydogan, M.: Autoregressive forests for multivariate time series modeling. Pattern Recogn. 73 (2017). https://doi.org/10.1016/j.patcog.2017.08.016
Wang, Z., Yan, W., Oates, T.: Time series classification from scratch with deep neural networks: a strong baseline. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 1578–1585. IEEE (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bennett, L.A., Abdallah, Z.S. (2023). RED CoMETS: An Ensemble Classifier for Symbolically Represented Multivariate Time Series. In: Ifrim, G., et al. Advanced Analytics and Learning on Temporal Data. AALTD 2023. Lecture Notes in Computer Science(), vol 14343. Springer, Cham. https://doi.org/10.1007/978-3-031-49896-1_6
Download citation
DOI: https://doi.org/10.1007/978-3-031-49896-1_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-49895-4
Online ISBN: 978-3-031-49896-1
eBook Packages: Computer ScienceComputer Science (R0)
