Discovery of temporal associations in multivariate time series

IEEE Transactions on Knowledge and Data Engineering

Volumn 26, Issue 12, 2014, Pages 2969-2982

  Zhuang, Dennis E H ^a   Li, Gary C L ^a   Wong, Andrew K C ^a  

^a UNIVERSITY OF WATERLOO   (Canada)

Author keywords

multivariate time series; Pattern discovery; temporal associations

Indexed keywords

MULTIVARIATE TIME SERIES; PATTERN DISCOVERY; TEMPORAL ASSOCIATION;

EID: 84910051132     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2014.2310219     Document Type: Article

References (25)

1. H. Nam, K. Lee, and D. Lee, "Identification of temporal association rules from time-series microarray data sets," BMC Bioinformatics, vol. 10, no. suppl. 3, p. S6, 2009.
2. F. Duchene, C. Garbay, and V. Rialle, "Learning recurrent behaviors from heterogeneous multivariate time-series," J. Artif. Intell. Med. Archive, vol. 39, no. 1, pp. 25-47, 2007.
3. L. Romani, A.- Avila, J. Zullo, C. Traina, and A. Traina, "Mining climate and remote sensing time series to discover the most relevant climate patterns," in Proc. Brazilian Symp. Databases, 2009, pp. 181-195.
4. S. Harms and J. Deogun, "Sequential association rule mining with time lags," J. Intell. Inf. Syst., vol. 22, no. 1, pp. 7-22, 2004.
5. T. Fu, "A review on time series data mining," Eng. Appl. AI, vol. 24, pp. 164-181, 2011.
6. S. Laxman and P. S. Sastry, "A survey of temporal data mining," Sadhana, vol. 31, no. 2, pp. 173-198, 2006.
7. T. Oates, "Identifying distinctive subsequences in multivariate time series by clustering," in Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, 1999, pp. 322-326.
8. T. Oates, "PERUSE: An unsupervised algorithm for finding recurring patterns in time series," in Proc. Int. Conf. Data Mining, 2002, pp. 330-337.
9. Y. Tanaka, K. Iwamoto, and K. Uehara, "Discovery of time-series motif from multi-dimensional data based on MDL principle," Mach. Learn., vol. 58, no. 2, pp. 269-300, 2005.
10. D. Minnen, T. Starner, I. A. Essa, and C. L. Isbell Jr., "Improving activity discovery with automatic neighborhood estimation," in Proc. 20th Int. Joint Conf. Artif. Intell., 2007, pp. 2814-2819.
11. B. Y. Chiu, E. J. Keogh, and S. Lonardi, "Probabilistic discovery of time series motifs," in Proc. 9th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2003, pp. 493-498.
12. D. Minnen, C. L. Isbell Jr., I. A. Essa, and T. Starner, "Discovering multivariate motifs using subsequence density estimation and greedy mixture learning," in Proc. 22nd Nat. Conf. Artif. Intell., 2007, pp. 615-620.
13. D. Minnen, C. L. Isbell, I. A. Essa, and T. Starner, "Detecting subdimensional motifs: An efficient algorithm for generalized multivariate pattern discovery," in Proc. Int. Conf. Data Mining, 2007, pp. 601-606.
14. C. Yin, S. Tian, S. Mu, and C. Shao, "Efficient computations of gapped string kernels based on suffix kernel," Neurocomputing, vol. 71, pp. 944-962, 2008.
15. G. Das, K. Lin, H. Mannila, G. Renganathan, and P. Smyth, "Rule discovery from time series," in Proc. Int. Conf. Knowl. Discovery Data Mining, 1998, pp. 16-22.
16. G. Tatavarty, R. Bhatnagar, and B. Young, "Discovery of temporal dependencies between frequent patterns in multivariate time series," in Proc. Comput. Intell. Data Mining, 2007, pp. 688-696.
17. 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.
18. L. Bergroth, H. Hakonen, and T. Raita, "A survey of longest common subsequence algorithms," in Proc. 7th Int. Symp. String Process. Inf. Retrieval, 2000, pp. 39-48.
19. C. Li and J. Wang, "Efficiently mining closed subsequences with gap constraints," in Proc. 8th SIAM Int. Conf. Data Mining, 2008, pp. 313-322.
20. S. Aluru and P. Ko, "Lookup tables, suffix trees and suffix arrays," in Handbook of Computational Molecular Biology. Boca Raton, FL, USA: CRC Press, 2006.
21. A. K. C. Wong, D. Zhuang, G. Li, and E. Lee, "Discovery of delta closed patterns and non-induced patterns from sequences," IEEE Trans. Knowl. Data Eng., vol. 24, no. 8, pp. 1408-1421, Apr. 2011.
22. S. Theodoridis and K. Koutroumbas, Pattern Recognition, 2nd ed. San Diego, CA, USA: Academic, 2003, ed, pp. 449-455.
23. C. D. Manning, P. Raghavan, and H. Schutze, Introduction to Information Retrieval. Cambridge, U.K.: Cambridge Univ. Press, 2008 ch. 17.
24. E. Keogh, The UCR time series data mining archive, Comput. Sci. Eng. Depart., Univ. California, Riverside, CA, USA, [Online]. Available: http://www.cs.ucr.edu/~eamonn/tutorials.htm.
25. T. Bastogne, H. Noura, P. Sibille, and A. Richard, "Multivariable identification of a winding process by subspace methods for tension control," Control Eng. Practice, vol. 6, no. 9, pp. 1077-1088, 1998.