Abstract
There has been a lot of interest in matching and retrieval of similar time sequences in time series databases. Most of previous work is concentrated on similarity matching and retrieval of time sequences based on the Euclidean distance. However, the Euclidean distance is sensitive to the absolute offsets of time sequences. In addition, the Euclidean distance is not a suitable similarity measurement in terms of shape. In this paper, we propose an indexing scheme for efficient matching and retrieval of time sequences based on the minimum distance. The minimum distance can give a better estimation of similarity in shape between two time sequences. Our indexing scheme can match time sequences of similar shapes irrespective of their vertical positions and guarantees no false dismissals. We experimentally evaluated our approach on real data(stock price movement).
This work was supported by the Brain Korea 21 Project in 2001
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Lee, S., Kwon, D., Lee, S. (2002). Efficient Pattern Matching of Time Series Data. In: Hendtlass, T., Ali, M. (eds) Developments in Applied Artificial Intelligence. IEA/AIE 2002. Lecture Notes in Computer Science(), vol 2358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48035-8_57
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DOI: https://doi.org/10.1007/3-540-48035-8_57
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