Finding Discordant Subsequence in Multivariate Time Series
Discordant subsequence in multivariate time series (MTS) is the subsequence that is least similar to all other MTS subsequences. In this paper, an algorithm of finding discordant subsequence in MTS, based on solving set, is proposed. Subsequences can be extracted by use of a sliding window. An extended Frobenius norm is used to compute the distance between MTS subsequences. The time complexity of the algorithm is subquadratic in the length of the MTS. We conduct experiments on two real-world datasets, stock market dataset and BCI (Brain Computer Interface) dataset. The experiment results show the efficiency and effectiveness of the algorithm.
multivariate time series discordant subsequence extended Frobenius norm solving set
Xiaoqing Weng Junyi Shen
Institute of Computer Software, Xian Jiaotong University, Xian, China Computer Center of Hebei University of Economics and Trade, Shijiazhuang, China
国际会议
2007 IEEE International Conference on Automation and Lofistics
山东济南
英文
2007-08-18（万方平台首次上网日期，不代表论文的发表时间）