Machine learning for noisy multivariate time series classification: a comparison and practical evaluation
Multivariate Time Series Classification (MTSC) is a complex problem that has seen great advances in recent years from the application of state-of-the-art machine learning techniques. However, there is still a need for a thorough evaluation of the effect of signal noise in the classification performance of MTSC techniques. To this end, in this paper, we evaluate three current and effective MTSC classifiers – DDTW, ROCKET and InceptionTime – and propose their use in a real-world classification problem: the detection of mooring line failure in offshore platforms. We show that all of them feature state-of-the-art accuracy, with ROCKET presenting very good results, and InceptionTime being marginally more accurate and resilient to noise.
Dempster, A., Petitjean, F., and Webb, G. I. (2020). ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery, 34(5):1454-1495. arXiv: 1910.13051.
Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., and Keogh, E. (2008). Querying and mining of time series data: Experimental comparison of representations and distance measures. PVLDB, 1:1542-1552.
Fawaz, H. I., Forestier, G., Weber, J., Idoumghar, L., and Muller, P.-A. (2019). Deep learning for time series classification: a review. Data Mining and Knowledge Discovery, 33(4):917-963.
Goodfellow, I., Bengio, Y., and Courville, A. (2016). Convolutional Networks, pages 329-335. MIT Press. http://www.deeplearningbook.org.
Gupta, S. and Gupta, A. (2019). Dealing with Noise Problem in Machine Learning Datasets: A Systematic Review. Procedia Computer Science, 161:466-474.
Hills, J., Lines, J., Baranauskas, E., Mapp, J., and Bagnall, A. (2013). Classification of time series by shapelet transformation. Data Mining and Knowledge Discovery, 28.
Ismail Fawaz, H., Lucas, B., Forestier, G., Pelletier, C., Schmidt, D. F., Weber, J., Webb, G. I., Idoumghar, L., Muller, P.-A., and Petitjean, F. (2020). Inceptiontime: Finding alexnet for time series classification. Data Min. Knowl. Discov., 34(6):1936--1962.
Kalapanidas, E., Avouris, N., Craciun, M., and Neagu, D. (2003). Machine learning algorithms: a study on noise sensitivity. In Proc. 1st Balcan Conference in Informatics, pages 356-365.
Kruskal, J. and Liberman, M. (1983). The symmetric time-warping problem: From continuous to discrete. Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison.
Lines, J., Taylor, S., and Bagnall, A. (2016). Hive-cote: The hierarchical vote collective of transformation-based ensembles for time series classification. In 2016 IEEE 16th international conference on data mining (ICDM), pages 1041-1046. IEEE.
Längkvist, M., Karlsson, L., and Loutfi, A. (2014). A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognition Letters, 42:11-24.
Ma, K.-t., Shu, H., Smedley, P., L'Hostis, D., and Duggal, A. (2013). A historical review on integrity issues of permanent mooring systems. In Offshore technology conference. OnePetro.
McDonald, G. C. (2009). Ridge regression. WIREs Computational Statistics, 1(1):93-100.
Nishimoto, K., Fucatu, C., and Masetti, I. (2002). Dynasim a time domain simulator of anchored fpso. Journal of Offshore Mechanics and Arctic Engineering-transactions of The Asme - J OFFSHORE MECH ARCTIC ENG, 124.
Ruiz, A. P., Flynn, M., Large, J., Middlehurst, M., and Bagnall, A. (2021). The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Mining and Knowledge Discovery, 35(2):401-449.
Saad, A. M., Schopp, F., Barreira, R. A., Santos, I. H. F., Tannuri, E. A., Gomi, E. S., and Costa, A. H. R. (2021). Using neural network approaches to detect mooring line failure. IEEE Access, 9:27678-27695.
Schäfer, P. (2015). The boss is concerned with time series classification in the presence of noise. Data Mining and Knowledge Discovery, 29(6):1-23.
Shokoohi-Yekta, M., Hu, B., Jin, H., Wang, J., and Keogh, E. (2017). Generalizing DTW to the multi-dimensional case requires an adaptive approach. Data Mining and Knowledge Discovery, 31(1):1-31.