Highly comparative feature-based time-series classification

IEEE Transactions on Knowledge and Data Engineering

Volumn 26, Issue 12, 2014, Pages 3026-3037

  Fulcher, Ben D ^a   Jones, Nick S ^b  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

classification; data mining; Time series analysis

Indexed keywords

DATA MINING;

FEATURE-BASED; TIME SERIES CLASSIFICATIONS;

CLASSIFICATION (OF INFORMATION);

EID: 84910104155     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2014.2316504     Document Type: Article

References (42)

1. E. Keogh and S. Kasetty, "On the need for time series data mining benchmarks: A survey and empirical demonstration," Data Mining Knowl. Discovery, vol. 7, pp. 349-371, 2003.
2. G. Gan, C. Ma, and J. Wu, Data Clustering. Theory, Algorithms, and Applications, L. LaVange, ed., Philadelphia, PA, USA: SIAM, 2007.
3. T. Mitsa, Temporal Data Mining. Boca Raton, FL, USA: CRC Press, 2010.
4. B. D. Fulcher, M. A. Little, and N. S. Jones, "Highly comparative time-series analysis: The empirical structure of time series and their methods," J. Roy. Soc. Interface, vol. 10, no. 83, p. 20130048, 2013.
5. X. Wang, A. Mueen, H. Ding, G. Trajcevski, P. Scheuermann, and E. Keogh, "Experimental comparison of representation methods and distance measures for time series data," Data Mining Knowl. Discovery, vol. 26, pp. 275-309, 2013.
6. T. W. Liao, "Clustering of time series data-A survey," Pattern Recognit., vol. 38, no. 11, pp. 1857-1874, 2005.
7. L. Wang, X. Wang, C. Leckie, and K. Ramamohanarao, "Characteristic-based descriptors for motion sequence recognition," in Proc. 12th Pacific-Asia Conf. Adv. Knowl. Discovery Data Mining, 2009, pp. 369-380.
8. L. Ye and E. Keogh, "Time series shapelets: A new primitive for data mining," in Proc. 15th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2009, pp. 947-956.
9. T. Rakthanmanon and E. Keogh, "Fast shapelets: A scalable algorithm for discovering time series shapelets," in Proc. SIAM Conf. Data Mining, 2013, pp. 668-676.
10. A. Nanopoulos, R. Alcock, and Y. Manolopoulos, "Feature-based classification of time-series data," in Information Processing and Technology, Commack, NY, USA: Nova, 2001 pp. 49-61.
11. F. Mörchen, "Time series feature extraction for data mining using DWT and DFT," Philipps-Univ. Marburg, Marburg, Germany, Tech. Rep. No. 33, 2003.
12. X. Wang, K. Smith, and R. Hyndman, "Characteristic-based clustering for time series data," Data Mining Knowl. Discovery, vol. 13, pp. 335-364, 2006.
13. X. Wang, A. Wirth, and L. Wang, "Structure-based statistical features and multivariate time series clustering," in Proc. IEEE Int. Conf. Data Mining, 2007, pp. 351-360.
14. H. Deng, G. Runger, E. Tuv, and M. Vladimir, "A time series forest for classification and feature extraction," Inf. Sci., vol. 239, pp. 142-153, 2013.
15. E. Keogh, X. Xi, L. Wei, and C. A. Ratanamahatana. (2006). The UCR time series classification/clustering homepage [Online]. Available: www.cs.ucr.edu/eamonn/time-series-data/.
16. E. Keogh, Q. Zhu, B. Hu, H. Y., X. Xi, L. Wei, and C. A. Ratanamahatana. (2011). The UCR time series classification/clustering homepage [Online]. Available: www.cs.ucr.edu/eamonn/time-series-data/
17. R. T. Olszweski, "Generalized feature extraction for structural pattern recognition in time-series data," Ph.D. dissertation, School Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA, USA, 2001.
18. D. Eads, D. Hill, S. Davis, S. Perkins, J. Ma, R. Porter, and J. Theiler, "Genetic algorithms and support vector machines for time series classification," Proc. SPIE, Appl. Sci. Neural Netw., Fuzzy Syst., Evol. Comput. V, vol. 4787, pp. 74-85, 2002.
19. O. J. O. Söderkvist, "Computer vision classification of leaves from Swedish trees," Master's thesis, Dept. Elect. Eng., Linköping Univ., 2001.
20. L. Wei and E. Keogh, "Semi-supervised time series classification," in Proc. 12th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, New York, NY, USA, 2006, vol. 20 no. 23, pp. 748-753.
21. D. Berndt and J. Clifford, "Using dynamic time warping to find patterns in time series," in Proc. Knowl. Discovery Data Mining Workshop, Seattle, WA, USA, 1994, vol. 10 no. 16, pp. 359-370.
22. A. K. Jain, R. P. W. Duin, and J. Mao, "Statistical pattern recognition: a review," IEEE Trans. Pattern. Anal. Mach. Intell., vol. 22, no. 1, pp. 4-37, Jan. 2000.
23. I. Guyon and A. Elisseeff, "An introduction to variable and feature selection," J. Mach. Learn. Res., vol. 3, pp. 1157-1182, 2003.
24. I. Guyon, C. Aliferis, and A. Elisseeff, "Causal feature selection," in Computational Methods of Feature Selection Data Mining and Knowledge Discovery Series, Boca Raton, FL, USA: CRC, 2007 pp. 63-85.
25. R. Tibshirani, "Regression shrinkage and selection via the lasso," J. Roy. Statist. Soc. Ser. B (Statist. Methodol.), vol. 58, no. 1, pp. 267- 288, 1996.
26. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, "Gene selection for cancer classification using support vector machines," Mach. Learn., vol. 46, no. 1, pp. 389-422, 2002.
27. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction., 2nd ed. New York, NY, USA: Springer, 2009.
28. C. A. Ratanamahatana and E. Keogh, "Making time-series classification more accurate using learned constraints," in Proc. SIAM Int. Conf. Data Mining, 2004, pp. 11-22.
29. D. Roverso, "Multivariate temporal classification by windowed wavelet decomposition and recurrent neural networks," in Proc. 3rd ANS Int. Top. Meet. Nucl. Plant Instrum., Control Human-Mach. Interface,Washington, DC, USA, 2000, vol. 20.
30. T. Schreiber and A. Schmitz, "Surrogate time series," in Physica D, vol. 142, 2000, nos. 3/4, pp. 346-382.
31. D. T. Pham and A. B. Chan, "Control chart pattern recognition using a new type of self-organizing neural network," in Proc. Inst. Mech. Eng. Part I-J. Syst. Control Eng., vol. 212, no. 2, 1998, pp. 115-127.
32. A. Gandhi, "Content-based image retrieval: Plant species identification," Master's thesis, Dept. Comput. Sci., Oregon State Univ., Corvallis, OR, USA, 2002.
33. X. Xi, E. Keogh, C. Shelton, L. Wei, and C. A. Ratanamahatana, "Fast time series classification using numerosity reduction," in Proc. 23rd Int. Conf. Mach. Learn., 2006, pp. 1033-1040.
34. D. Wolpert and W. Macready, "No free lunch theorems for optimization," IEEE Trans. Evol. Comput., vol. 1, no. 1, pp. 67-82, 1997.
35. T. Rakthanmanon, B. Campana, A. Mueen, G. Batista, B. Westover, Q. Zhu, J. Zakaria, and E. Keogh, "Searching and mining trillions of time series subsequences under dynamic time warping," in Proc. 18th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2012, pp. 262-270.
36. 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," Proc. VLDB Endowment, vol. 1, pp. 1542-1552, 2008.
37. K. Chakrabarti, E. Keogh, S. Mehrotra, and M. Pazzani, "Locally adaptive dimensionality reduction for indexing large time series databases," ACM Trans. Database Syst., vol. 27, pp. 188-228, 2002.
38. J. Shieh and E. Keogh, "iSAX: Indexing and mining terabyte sized time series," in Proc. 14th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2008, pp. 623-631.
39. J. Lin, E. Keogh, L. Wei, and S. Lonardi, "Experiencing SAX: A novel symbolic representation of time series," Data Mining Knowl. Discovery, vol. 15, no. 2, pp. 107-144, 2007.
40. H. Kantz and T. Schreiber, Nonlinear Time Series Analysis., 2nd ed. Cambridge, U.K.: Cambridge Unive. Press, 2004.
41. G. E. Batista, X. Wang, and E. J. Keogh, "A complexity-invariant distance measure for time series," in Proc. SIAM Int. Conf. Data Mining, vol. 31, 2011, pp. 699-710.
42. J. Timmer, C. Gantert, G. Deuschl, and J. Honerkamp, "Characteristics of hand tremor time series," Biol. Cybern., vol. 70, no. 1, pp. 75-80, 1993.