Abstract
Similarity measure is a basic task in time series data mining and attracts much attention in the last decade. This paper considers time series similarity measure from an information theoretic perspective. Based on the function of degree of disagreement (FDOD), a new time series similarity measure method is proposed. The empirical result indicates that the method of this paper can solve the unequal time series and has less time complexity. Meanwhile, it also can measure the similarity between multivariate time series.
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References
Keogh, E., Kasetty, S.: On the need for time series data mining benchmarks: a survey and empirical demonstration. Data Mining and Knowledge Discovery 7(4), 349–371 (2003)
Yang, Q., Wu, X.D.: 10 challenging problems in data mining research. International Journal of Information Technology & Decision Making 5(4), 597–604 (2006)
Liao, T.W.: Clustering of time series data-a survey. Pattern Recognition 38, 1857–1874 (2005)
Liao, T.W.: A clustering procedure for exploratory mining of vector time series. Pattern Recognition 40, 2550–2562 (2007)
Lin, J., Keogh, E., Li, W.: Experiencing SAX: a novel symbolic representation of time series. Data Mining and Knowledge Discovery 15, 107–144 (2007)
Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. Knowledge and Information Systems 3(3), 263–286 (2000)
Fang, W.W.: The Disagreement Degree of Multi-person judgments in additive structure. Mathematical Social Sciences 28(2), 85–111 (1994)
Fang, W.W.: The characterization of a measure of information discrepancy. Information Sciences 125, 207–232 (2000)
Fang, W.W., Roberts, F.S., Ma, Z.: A measure of discrepancy of multiple sequences. Information Sciences 137, 75–102 (2001)
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Guo, C., Zhang, Y. (2011). Time Series Similarity Measure Based on the Function of Degree of Disagreement. In: Xiong, H., Lee, W.B. (eds) Knowledge Science, Engineering and Management. KSEM 2011. Lecture Notes in Computer Science(), vol 7091. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25975-3_10
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DOI: https://doi.org/10.1007/978-3-642-25975-3_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25974-6
Online ISBN: 978-3-642-25975-3
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