Abstract
The success of time series data mining applications, such as query by content, clustering, and classification, is greatly determined by the performance of the algorithm used for the determination of similarity between two time series. The previous research on time series matching has mainly focused on whole sequence matching and to limited extent on sequence-to-subsequence matching. Relatively, very little work has been done on subsequence-to-subsequence matching, where two time series are considered similar if they contain similar subsequences or patterns in the same time order. This paper presents an effective approach capable of handling whole sequence, sequence-to-subsequence and subsequence-to-subsequence matching. The proposed approach derives its strength from the novel two stage segmentation algorithm, which facilitates the alignment of the two time series by retaining perceptually important points of the two time series as break points.
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References
Esling, P., Agon, C.: Time-series data mining. ACM Comput. Surv. 45, 34 (2012)
Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., Keogh, E.: Querying and mining of time series data: experimental comparison of representations and distance measures. Proc. Very Large Databases 1, 1542–1552 (2008)
Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: KDD Workshop, pp. 359–370. AAAI Press, Palo Alto (1994)
Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Sig. Proc. 26(1), 43–49 (1978)
Morse, M.D., Patel, J.M.: An efficient and accurate method for evaluating time series similarity. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 569–580 (2007)
Athitsos, V., Papapetrou, P., Potamias, M., Kollios, G., Gunopulos, D.: Approximate embedding-based subsequence matching of time series. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 365–378 (2008)
Park, S., Kim, S., Chu, W.W.: Segment-based approach for subsequence searches in sequence databases. In: Symposium on Applied Computing, pp. 248–252 (2001)
Zhu, Y., Shasha, D.: Warping indexes with envelope transforms for query by humming. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 181–192 (2003)
Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time-series databases. In: Snodgrass, R.T., Winslett, M. (eds.) Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 19–429 (1994)
Wu, Y., Agrawal, D., Abbadi, A.E.: A comparison of DFT and DWT based similarity search in time-series databases. In: Proceedings of the 9th International Conference on Information and Knowledge Management, pp. 488–495 (2000)
Hung, N.Q., Anh, D.T.: An improvement of PAA for dimensionality reduction in large time series databases. In: Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence, pp. 698–707 (2008)
Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Dimensionality reduction for fast similarity search in large time series databases. Knowl. Inf. Syst. 3, 263–286 (2001)
Chakrabarti K, Keogh E, Mehrotra S, Pazzani M.: Locally adaptive dimensionality reduction for indexing large time series databases. ACM Trans. Database Syst. 27, 118–228
Keogh, E., Pazzani, M.: An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. In: 4th International Conference on Knowledge Discovery and Data Mining, pp. 239–241 (1998)
Lin, J., Keogh, E., Wei, L., Lonardi, S.: Experiencing SAX: a novel symbolic representation of time series. Data Min. Knowl. Discov. 15, 107–144 (2007)
Lemire, D.: A better alternative to piecewise linear time series segmentation. In: SIAM Data Mining (2007)
Bettaiah, V., Ranganath, H.S.: An analysis of time series representation methods. In: Proceedings of the 2014 ACM Southeast Regional Conference, Article 16, p 6 (2014)
Bettaiah, V., Ranganath, H.S: Two stage segmentation for efficient time series matching. In: 2nd International Conference on Research in Science, Engineering and Technology, pp. 29–35 (2014)
Bettaiah, V., Ranganath, H.S: An effective subsequence-to-subsequence time series matching approach. In: Science and Information (SAI) Conference, pp. 112–122 (2014)
The UCR Time Series Classification/Clustering. http://www.cs.ucr.edu/eamonn/time_series_data/. Retrieved Aug 2013
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Bettaiah, V., Ranganath, H.S. (2015). Alignment of Time Series for Subsequence-to-Subsequence Time Series Matching. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems in Science and Information 2014. SAI 2014. Studies in Computational Intelligence, vol 591. Springer, Cham. https://doi.org/10.1007/978-3-319-14654-6_12
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DOI: https://doi.org/10.1007/978-3-319-14654-6_12
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