Open Access
Issue |
RAIRO-Oper. Res.
Volume 53, Number 1, January–March 2019
ROADEF 2017
|
|
---|---|---|
Page(s) | 243 - 259 | |
DOI | https://doi.org/10.1051/ro/2018089 | |
Published online | 14 February 2019 |
- A. Bagnall, E. Keogh, J. Lines, A. Bostrom, J. Large, Time Series Classification Website. Available at: http://timeseriesclassification.com (2016). [Google Scholar]
- A. Bagnall, J. Lines, A. Bostrom, J. Large, E. Keogh, The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min. Knowl. Discov. 31 (2017) 606–660. [PubMed] [Google Scholar]
- A. Camerra, T. Palpanas, J. Shieh, E. Keogh, isax 2.0: Indexing and mining one billion time series. In: 2010 IEEE 10th International Conference on Data Mining – ICDM (2010) 58–67. [Google Scholar]
- K.S. Candan, R. Rossini, X. Wang, M.L. Sapino, sdtw: computing dtw distances using locally relevant constraints based on salient feature alignments. VLDB Endowment 5 (2012) 1519–1530. [CrossRef] [Google Scholar]
- Y. Chen, E. Keogh, B. Hu, N. Begum, A. Bagnall, A. Mueen, G. Batista, The UCR time series classification archive. Available at: http://www.cs.ucr.edu/~eamonn/time_series_data/ (2015). [Google Scholar]
- S. Chu, E.J. Keogh, D.M. Hart, M.J. Pazzani, et al., Iterative deepening dynamic time warping for time series. In: Proc. of the 2002 SIAM International Conference on Data Mining. SIAM (2002) 195–212. [Google Scholar]
- J. Cuřín, P. Fleury, J. Kleindienst, R. Kessl, Meeting state recognition from visual and aural labels. In: Learning for Multimodal Interaction, Springer, 2007, 24–25. [Google Scholar]
- T.A. Feo, M.G. Resende, Greedy randomized adaptive search procedures. J. Glob. Optim. 6 (1995) 109–133. [Google Scholar]
- O.H. Ibarra, C.E. Kim, Fast approximation algorithms for the knapsack and sum of subset problems. J. ACM (JACM) 22 (1975) 463–468. [CrossRef] [MathSciNet] [Google Scholar]
- Itakura, F., Minimum prediction residual principle applied to speech recognition. IEEE Trans. Acoust. Speech Signal Process. 23 (1975) 67–72. [CrossRef] [Google Scholar]
- Y.S. Jeong, M.K. Jeong, O.A. Omitaomu, Weighted dynamic time warping for time series classification. Pattern Recogn. 44 (2011) 2231–2240. [CrossRef] [Google Scholar]
- R.J. Kate, Using dynamic time warping distances as features for improved time series classification. Data Min. Knowl. Discov. 30 (2016) 283–312. [Google Scholar]
- E. Keogh, K. Chakrabarti, M. Pazzani, S. Mehrotra, Dimensionality reduction for fast similarity search in large time series databases. Knowl. Inform. Syst. 3 (2001) 263–286. [CrossRef] [Google Scholar]
- E.J. Keogh, M.J. Pazzani, Scaling up dynamic time warping for datamining applications. In: Sixth ACM SIGKDD. ACM (2000) 285–289. [Google Scholar]
- E.J. Keogh, M.J. Pazzani, Derivative dynamic time warping. In: 1st SIAM International Conference on Data Mining. SIAM (2001) 1–11. [Google Scholar]
- J. Lin, E. Keogh, S. Lonardi, B. Chiu, A symbolic representation of time series, with implications for streaming algorithms. In: 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery. ACM (2003) 2–11. [Google Scholar]
- B. Lkhagva, Y. Suzuki, K. Kawagoe, Extended SAX: Extension of Symbolic aggregate approximation for financial time series data representation. DEWS2006 4A–i8, 7 (2006). [Google Scholar]
- J. Longin, M. Vasilis, W. Qiang, L. Rolf, A. Chotirat, E. Keogh, Elastic partial matching of time series. In: 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal (2005). [Google Scholar]
- C. Myers, L. Rabiner, A. Rosenberg, Performance tradeoffs in dynamic time warping algorithms for isolated word recognition. IEEE Trans. Acoust. Speech Signal Process. 28 (1980) 623–635. [Google Scholar]
- T. Rakthanmanon, B. Campana, A. Mueen, G. Batista, B. Westover, Q. Zhu, J. Zakaria, E. Keogh, Searching and mining trillions of time series subsequences under dynamic time warping. In: 18th ACM SIGKDD (2012) 262–270. [Google Scholar]
- C.A. Ratanamahatana, E. Keogh, Making time-series classification more accurate using learned constraints. In: Proc. of the 2004 SIAM International Conference on Data Mining. SIAM (2004) 11–22. [Google Scholar]
- H. Sakoe, S. Chiba, Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 26 (1978) 43–49. [Google Scholar]
- S. Salvador, P. Chan, Toward accurate dynamic time warping in linear time and space. Intell. Data Anal. 11 (2007) 561–580. [CrossRef] [Google Scholar]
- V.S. Siyou Fotso, E. Mephu Nguifo, P. Vaslin, Comparaison des Algorithmes de classification. FDTW. Available at: http://fc.isima.fr/~siyou/fdtw (2016). [Google Scholar]
- Y. Sun, J. Li, J. Liu, B. Sun, C. Chow, An improvement of Symbolic aggregate approximation distance measure for time series. Neurocomputing 138 (2014) 189–198. [Google Scholar]
- L. Ulanova, N. Begum, E. Keogh, Scalable clustering of time series with u-shapelets, In: 2015 SIAM International Conference on Data Mining. SIAM (2015) 900–908. [Google Scholar]
- X. Wang, A. Mueen, H. Ding, G. Trajcevski, P. Scheuermann, E. Keogh, Experimental comparison of representation methods and distance measures for time series data. Data Min. Knowl. Discov. 26 (2013) 275–309. [Google Scholar]
- D. Yu, X. Yu, Q. Hu, J. Liu, A. Wu, Dynamic time warping constraint learning for large margin nearest neighbor classification. Inform. Sci. 181 (2011) 2787–2796. [CrossRef] [Google Scholar]
- Z. Zhang, P. Tang, R. Duan, Dynamic time warping under pointwise shape context. Inform. Sci. 315 (2015) 88–101. [CrossRef] [Google Scholar]
- J. Zhao, L. Itti, Shapedtw: shape dynamic time warping. Preprint arXiv: 1606.01601 (2016). [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.