文献类型：期刊文献

英文题名：Time series classification based on triadic time series motifs

作者：Xie, Wen-Jie[1,2];Han, Rui-Qi[3];Zhou, Wei-Xing[2,4]

机构：[1]East China Univ Sci & Technol, Dept Finance, 130 Meilong Rd, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Res Ctr Econophys, 130 Meilong Rd, Shanghai 200237, Peoples R China;[3]East China Univ Sci & Technol, Dept Math, Shanghai 200237, Peoples R China;[4]East China Univ Sci & Technol, Dept Math, Dept Finance, 130 Meilong Rd, Shanghai 200237, Peoples R China

年份：2019

卷号：33

期号：21

外文期刊名：INTERNATIONAL JOURNAL OF MODERN PHYSICS B

收录：;EI(收录号：20200515178);WOS:【SCI-EXPANDED(收录号:WOS:000487187900009)】；

基金：This work was supported by the National Natural Science Foundation of China (Grant Nos. 11505063, 71532009 and U1811462) and the Fundamental Research Funds for the Central Universities (Grant No. 222201818006).

语种：英文

外文关键词：Time series analysis; classification; time series motifs; motif profiles; dynamic time wrapping

摘要：It is of great significance to identify the characteristics of time series to quantify their similarity and classify different classes of time series. We define six types of triadic time-series motifs and investigate the motif occurrence profiles extracted from the time series. Based on triadic time series motif profiles, we further propose to estimate the similarity coefficients between different time series and classify these time series with high accuracy. We validate the method with time series generated from nonlinear dynamic systems (logistic map, chaotic logistic map, chaotic Henon map, chaotic Ikeda map, hyperchaotic generalized Henon map and hyperchaotic folded-tower map) and retrieved from the UCR Time Series Classification Archive. Our analysis shows that the proposed triadic time series motif analysis performs better than the classic dynamic time wrapping method in classifying time series for certain datasets investigated in this work.