1. Al-Naymat, G., Chawla, S., Taheri, J.: Sparsedtw: A novel approach to speed up dynamic time warping. In: Conferences in Research and Practice in Information Technology Series, vol. 101 (2012)
2. Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, AAAIWS 1994, pp. 359–370. AAAI Press (1994)
3. Dhamala, J., Azuh, E., Al-Dujaili, A., Rubin, J., O’Reilly, U.: Multivariate time-series similarity assessment via unsupervised representation learning and stratified locality sensitive hashing: Application to early acute hypotensive episode detection. CoRR abs/1811.06106 (2018)
4. Gonzalez, J., Gimenez, J., Labarta, J.: Automatic detection of parallel applications computation phases. pp. 1–11 (2009). https://doi.org/10.1109/IPDPS.2009.5161027
5. Grabocka, J., Schmidt-Thieme, L.: Neuralwarp: Time-series similarity with warping networks. CoRR abs/1812.08306 (2018)