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Time series classification is a field which has drawn much attention over the past decade.
R. Agrawal, C. Faloutsos, and A. Swami, “Efficient similarity search in sequence databases,” Foundations of Data Organization and Algorithms , pp. 69–84, 1993
1993
Earlier work this paper cites.
C. Faloutsos, M. Ranganathan, and Y. Manolopoulos, “Fast subsequence matching in time-series databases,” ACM SIGMOD Record , vol. 23, no. 2, pp. 419–429, 1994
1994
Earlier work this paper cites.
T. Dietterich, “Overfitting and undercomputing in machine learning,” ACM Comput. Surv. , vol. 27, no. 3, pp. 326–327, Sep. 1995. [Online]. Available: http://doi.acm.org/10.1145/212094.212114
1995
Earlier work this paper cites.
E. Keogh and M. Pazzani, “An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback,” in Proceedings of the 4th International Conference of Knowledge Discovery and Data Mining , 1998, pp. 239–241
1998
Earlier work this paper cites.
P. Geurts, “Pattern extraction for time series classification,” Principles of Data Mining and Knowledge Discovery , pp. 115–127, 2001
2001
Earlier work this paper cites.
R. Harris and R. Sollis, Applied time series modelling and forecasting . J. Wiley, 2003
2003
Earlier work this paper cites.
R. Povinelli, M. Johnson, A. Lindgren, and J. Ye, “Time series classification using Gaussian mixture models of reconstructed phase spaces,” Knowledge and Data Engineering, IEEE Transactions on , vol. 16, no. 6, pp. 779–783, 2004
2004
Earlier work this paper cites.
C. Ratanamahatana and E. Keogh, “Making time-series classification more accurate using learned constraints,” in Proceedings of SIAM International Conference on Data Mining . Lake Buena Vista, Florida, 2004, pp. 11–22
2004
Cited alongside, same era.
L. Chen and R. Ng, “On the marriage of lp-norms and edit distance,” in Proceedings of the Thirtieth international conference on Very large data bases-Volume 30 . VLDB Endowment, 2004, pp. 792–803
2004
Cited alongside, same era.
W. Liao et al. , “Clustering of time series data–a survey,” Pattern Recognition , vol. 38, no. 11, pp. 1857–1874, 2005
2005
Cited alongside, same era.
M. Kadous and C. Sammut, “Classification of multivariate time series and structured data using constructive induction,” Machine learning , vol. 58, no. 2, pp. 179–216, 2005
2005
Cited alongside, same era.
H. Ding, G. Trajcevski, P. Scheuermann, X. Wang, and E. Keogh, “Querying and mining of time series data: experimental comparison of representations and distance measures,” Proceedings of the VLDB Endowment , vol. 1, no. 2, pp. 1542–1552, 2008
2008
Later among the works it cites.
B. Hartmann, I. Schwab, and N. Link, “Prototype optimization for temporarily and spatially distorted time series,” in the AAAI Spring Symposia , 2010
2010
Later among the works it cites.
L. Ye and E. Keogh, “Time series shapelets: a novel technique that allows accurate, interpretable and fast classification,” Data Mining and Knowledge Discovery , pp. 1–34, 2011
2011
Later among the works it cites.
A. McGovern, D. Rosendahl, R. Brown, and K. Droegemeier, “Identifying predictive multi-dimensional time series motifs: an application to severe weather prediction,” Data Mining and Knowledge Discovery , vol. 22, pp. 232–258, 2011
2011
Later among the works it cites.
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2005
Cited alongside, same era.
X. Xi, E. Keogh, C. Shelton, L. Wei, and C. Ratanamahatana, “Fast time series classification using numerosity reduction,” in Proceedings of the 23rd international conference on Machine learning . ACM, 2006, pp. 1033–1040
2006
Cited alongside, same era.
“Ecg dataset,” http://www.cs.ucr.edu/~wli/ICDM05/
Cited in the paper.
“shapelet datasets,” http://alumni.cs.ucr.edu/~lexiangy/shapelet.html
Cited in the paper.
“logical shapelet webpage,” http://www.cs.ucr.edu/~mueen/LogicalShapelet/
Cited in the paper.
A. Mueen, E. Keogh, and N. Young, “Logical-shapelets: an expressive primitive for time series classification,” in Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 2011, pp. 1154–1162
2011
Later among the works it cites.
E. Keogh, Q. Zhu, B. Hu, H. Y., X. Xi, L. Wei, and C. A. Ratanamahatana, “The ucr time series classification/clustering homepage,” www.cs.ucr.edu/~eamonn/time_series_data/
2011
Later among the works it cites.