In this paper, a novel method is proposed to discover frequent pattern from time series. It first segments time series based on perceptually important points, then converted it into meaningful symbol sequences by the relative scope, finally used a new mining model to find frequent patterns. Compared with the previous methods, the method is simpler and more efficient.
CITATION STYLE
Zeng, H., Shen, Z., & Hu, Y. (2003). Mining sequence pattern from time series based on inter-relevant successive trees model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2639, pp. 734–737). Springer Verlag. https://doi.org/10.1007/3-540-39205-x_127
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