| [1] | R. Agrawal, C. Faloutsos, and A. Swami, " Efficient similarity search in sequence databases,” in Proc. Int. Conf. Foundations of Data Organization and Algorithms. Springer, 1993, pp. 69–84. |
| [2] | E. Keogh and S. Kasetty, " On the need for time series data mining benchmarks: a survey and empirical demonstration,” Data Mining and Knowledge Discovery, vol. 7, no. 4, pp. 349–371, 2003. doi: 10.1023/A:1024988512476 |
| [3] | Y.-W. Huang and P. S. Yu, " Adaptive query processing for time-series data,” in Proc. 5th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining. ACM, 1999, pp. 282–286. |
| [4] | E. D. Kim, J. M. W. Lam, and J. Han, " Aim: approximate intelligent matching for time series data,” in Proc. Int. Conf. Data Warehousing and Knowledge Discovery. Springer, 2000, pp. 347–357. |
| [5] | N. Saito and R. R. Coifman, " Local discriminant bases and their applications,” J. Mathematical Imaging and Vision, vol. 5, no. 4, pp. 337–358, 1995. doi: 10.1007/BF01250288 |
| [6] | M. Lichman, " UCI machine learning repository,” http://archive.ics.uci.edu/ml/index.php, 2013. |
| [7] | E. Keogh and T. Folias, " The UCR time series data mining archive,” 2002. |
| [8] | Y. Chen, E. Keogh, B. Hu, N. Begum, A. Bagnall, A. Mueen, and G. Batista, " The UCR time series classification archive,” [Online]. Available: https://www.cs.ucr.edu/~eamonn/time_series_data/, 2015. |
| [9] | B. Hu, Y. Chen, and E. Keogh, " Classification of streaming time series under more realistic assumptions,” Data Mining and Knowledge Discovery, vol. 30, no. 2, pp. 403–437, 2016. doi: 10.1007/s10618-015-0415-0 |
| [10] | H. A. Dau, D. F. Silva, F. Petitjean, G. Forestier, A. Bagnall, A. Mueen, and E. Keogh, " Optimizing dynamic time warping’s window width for time series data mining applications,” Data Mining and Knowledge Discovery, 2018. |
| [11] |
T. Rakthanmanon, B. Campana, A. Mueen, G. Batista, B. Westover, Q. Zhu, J. Zakaria, and E. Keogh, " Addressing big data time series: mining trillions of time series subsequences under dynamic time warping,” Trans. Knowledge Discovery from Data (TKDD) |
| [12] | A. Bagnall, J. Lines, W. Vickers, and E. Keogh, " The UEA and UCR time series classification repository,” [Online]. Available: https://www.timeseriesclassification.com, 2018. |
| [13] | M. Taktak, S. Triki, and A. Kamoun, " SAX-based representation with longest common subsequence dissimilarity measure for time series data classification,” in Proc. IEEE/ACS 14th Int. Conf. Computer Systems and Applications (AICCSA), 2017, pp. 821–828. |
| [14] | A. Silva and R. Ishii, " A new time series classification approach based on recurrence quantification analysis and Gabor filter,” in Proc. 31st Annual ACM Symposium on Applied Computing, 2016, pp. 955–957. |
| [15] | Y. He, J. Pei, X. Chu, Y. Wang, Z. Jin, and G. Peng, " Characteristic subspace learning for time series classification,” in Proc. IEEE Int. Conf. Data Mining (ICDM), 2018, pp. 1019–1024. |
| [16] | " Supporting web page,” [Online]. Available: https://www.cs.ucr.edu/~hdau001/ucr_archive/. |
| [17] | Z. C. Lipton and J. Steinhardt, " Troubling trends in machine learning scholarship,” arXiv preprint arXiv: 1807.03341, 2018. |
| [18] | J. Paparrizos and L. Gravano, " k-Shape: efficient and accurate clustering of time series,” in Proc. ACM SIGMOD Int. Conf. Management of Data ACM Sigmod, pp. 1855-1870, 2015. [Online]. Available: http://dl.acm.org/citation.cfm?id=2723372.2737793 |
| [19] | J. Hills, J. Lines, E. Baranauskas, J. Mapp, and A. Bagnall, " Classification of time series by shapelet transformation,” Data Mining and Knowledge Discovery, vol. 28, no. 4, pp. 851–881, 2014. doi: 10.1007/s10618-013-0322-1 |
| [20] | H. A. Dau, D. F. Silva, F. Petitjean, G. Forestier, A. Bagnall, and E. Keogh, " Judicious setting of dynamic time warping’s window width allows more accurate classification of time series,” in Proc. IEEE Int. Conf. Big Data (Big Data), 2017, pp. 917–922. [Online]. Available: http://ieeexplore.ieee.org/document/8258009/ |
| [21] | S. Lu, G. Mirchevska, S. S. Phatak, D. Li, J. Luka, R. A. Calderone, and W. A. Fonzi, " Dynamic time warping assessment of high-resolution melt curves provides a robust metric for fungal identification,” PLOS ONE, vol. 12, no. 3, 2017. |
| [22] | D. F. Silva, G. E. Batista, and E. Keogh, " Prefix and suffix invariant dynamic time warping,” in Proc. IEEE Int. Conf. Data Mining(ICDM), 2017, pp. 1209–1214. |
| [23] | D. Li, T. F. Bissyande, J. Klein, and Y. L. Traon, " Time series classification with discrete wavelet transformed data,” Int. J. Software Engineering and Knowledge Engineering, vol. 26, no. 09n10, pp. 1361–1377, 2016. |
| [24] | H. Zhang, T. B. Ho, M. S. Lin, and W. Huang, " Combining the global and partial information for distance-based time series classification and clustering,” JACIII, vol. 10, no. 1, pp. 69–76, 2006. doi: 10.20965/jaciii.2006.p0069 |
| [25] | H. Zhang and T. B. Ho, " Finding the clustering consensus of time series with multi-scale transform,” Soft Computing as Transdisciplinary Science and Technology. Springer, 2005, pp. 1081–1090. |
| [26] | E. Keogh, K. Chakrabarti, M. Pazzani, and S. Mehrotra, " Dimensionality reduction for fast similarity search in large time series databases,” Knowledge and Information Systems, vol. 3, no. 3, pp. 263–286, 2001. doi: 10.1007/PL00011669 |
| [27] | P. Schäfer, " The BOSS is concerned with time series classification in the presence of noise,” Data Mining and Knowledge Discovery, vol. 29, no. 6, pp. 1505–1530, 2015. doi: 10.1007/s10618-014-0377-7 |
| [28] | D. Li, T. F. D. A. Bissyande, J. Klein, and Y. Le Traon, " Time series classification with discrete wavelet transformed data: insights from an empirical study,” in Proc. 28th Int. Conf. Software Engineering and Knowledge Engineering (SEKE), 2016. |
| [29] | U. M. Okeh and C. N. Okoro, " Evaluating measures of indicators of diagnostic test performance: fundamental meanings and formulars,” J Biom Biostat, vol. 3, no. 1, pp. 2, 2012. |
| [30] | S. Uguroglu, " Robust learning with highly skewed category distributions,” 2013. |
| [31] | K. H. Brodersen, C. S. Ong, K. E. Stephan, and J. M. Buhmann, " The balanced accuracy and its posterior distribution,” in Proc. of IEEE 20th Int. Conf. Pattern Recognition (ICPR), 2010, pp. 3121–3124. |
| [32] | J. Lines, S. Taylor, and A. Bagnall, " HIVE-COTE: the hierarchical vote collective of transformation-based ensembles for time series classification,” in Proc. 16th IEEE Int. Conf. Data Mining (ICDM), 2016, pp. 1041–1046. |
| [33] | J. Demšar, " Statistical comparisons of classifiers over multiple data sets,” J. Machine Learning Research, vol. 7, no.1, pp. 1–30, 2006. |
| [34] | S. Garcia and F. Herrera, " An extension on ‘statistical comparisons of classifiers over multiple data sets’ for all pairwise comparisons,” J. Machine Learning Research, vol. 9, no. 12, pp. 2677–2694, 2008. |
| [35] | S. L. Salzberg, " On comparing classifiers: pitfalls to avoid and a recommended approach,” Data Mining and Knowledge Discovery, vol. 1, no. 3, pp. 317–328, 1997. doi: 10.1023/A:1009752403260 |
| [36] | Student, " The probable error of a mean,” Biometrika, vol. 6, no. 1, pp. 1–25, 1908. |
| [37] | F. Wilcoxon, " Individual comparisons by ranking methods,” Biometrics Bulletin, vol. 1, no. 6, pp. 80–83, 1945. doi: 10.2307/3001968 |
| [38] | S. Siegal, Nonparametric Statistics for the Behavioral Sciences. vol. 7. New York: McGraw-hill, 1956. |
| [39] | M. Friedman, " The use of ranks to avoid the assumption of normality implicit in the analysis of variance,” J. Tmerican Statistical Association, vol. 32, no. 200, pp. 675–701, 1937. doi: 10.1080/01621459.1937.10503522 |
| [40] | M. Friedman, " A comparison of alternative tests of significance for the problem of m rankings,” The Annals of Mathematical Statistics, vol. 11, no. 1, pp. 86–92, 1940. doi: 10.1214/aoms/1177731944 |
| [41] | A. Benavoli, G. Corani, and F. Mangili, " Should we really use post-hoc tests based on mean-ranks,” J. Machine Learning Research, vol. 17, no. 5, pp. 1–10, 2016. |
| [42] | M. Hollander, D. A. Wolfe, and E. Chicken, Nonparametric Statistical Methods. John Wiley & Sons, 2013, vol. 751. |
| [43] | S. Holm, " A simple sequentially rejective multiple test procedure,” Scandinavian J. Statistics, vol. 6, no. 2, pp. 65–70, 1979. |
| [44] | S. Gharghabi, S. Imani, A. Bagnall, A. Darvishzadeh, and E. Keogh, " Matrix profile XII: MPdist: a novel time series distance measure to allow data mining in more challenging scenarios,” in Proc. IEEE Int. Conf. Data Mining (ICDM), 2018, pp. 965–970. |
| [45] | G. E. Batista, E. J. Keogh, O. M. Tataw, and V. M. A. De Souza, " CID: an efficient complexity-invariant distance for time series,” Data Mining and Knowledge Discovery, vol. 28, no. 3, pp. 634–669, 2014. doi: 10.1007/s10618-013-0312-3 |
| [46] | H. A. Dau, E. Keogh, K. Kamgar, C.-C. M. Yeh, Y. Zhu, S. Gharghabi, C. A. Ratanamahatana, Y. Chen, B. Hu, N. Begum, A. Bagnall, A. Mueen, and G. Batista, " The UCR time series classification archive,” [Online]. Available: https://www.cs.ucr.edu/~eamonn/time_series_data_2018/, 2018. |
| [47] | A. Bagnall, J. Lines, A. Bostrom, J. Large, and E. Keogh, " The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances,” Data Mining and Knowledge Discovery, vol. 31, no. 3, pp. 606–660, 2017. doi: 10.1007/s10618-016-0483-9 |
| [48] |
J. Lines, S. Taylor, and A. Bagnall, " Time series classification with HIVE-COTE: the hierarchical vote collective of transformation-based ensembles,” ACM Trans. Knowledge Discovery from Data (TKDD) |
| [49] | R. A. Fisher, " The use of multiple measurements in taxonomic problems,” Annals of Eugenics, vol. 7, no. 2, pp. 179–188, 1936. doi: 10.1111/j.1469-1809.1936.tb02137.x |
| [50] | A. Mezari and I. Maglogiannis, " Gesture recognition using symbolic aggregate approximation and dynamic time warping on motion data,” in Proc. 11th ACM EAI Int. Conf. Pervasive Computing Technologies for Healthcare, 2017, pp. 342–347. |
| [51] | M. Guillame-Bert and A. Dubrawski, " Classification of time sequences using graphs of temporal constraints,” J. Machine Learning Research, vol. 18, pp. 1–34, 2017. |
| [52] | D. Murray, J. Liao, L. Stankovic, V. Stankovic, C. Wilson, M. Coleman, and T. Kane, " A data management platform for personalised real-time energy feedback,” Eedal, pp. 1–15, 2015. |
| [53] | G. Forestier, F. Petitjean, H. A. Dau, G. I. Webb, and E. Keogh, " Generating synthetic time series to augment sparse datasets,” in Proc. IEEE Int. Conf. Data Mining (ICDM), 2017, pp. 865–870. [Online]. Available: http://ieeexplore.ieee.org/document/8215569/ |