Finding Discordant Subsequence in Multivariate Time Series

Author

Weng, Xiaoqing ; Shen, Junyi

Author_Institution

Xi ´´an Jiaotong Univ., Xi´´an

fYear

2007

fDate

18-21 Aug. 2007

Firstpage

1731

Lastpage

1735

Abstract

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.

Keywords

data mining; set theory; time series; brain computer interface dataset; discordant subsequence; multivariate time series; sliding window; stock market dataset; time complexity; Aggregates; Automation; Brain computer interfaces; Data mining; Euclidean distance; Logistics; Principal component analysis; Software; Stock markets; Time measurement; discordant subsequence; extended Frobenius norm; multivariate time series; solving set;

fLanguage

English

Publisher

ieee

Conference_Titel

Automation and Logistics, 2007 IEEE International Conference on

Conference_Location

Jinan

Print_ISBN

978-1-4244-1531-1

Type

conf

DOI

10.1109/ICAL.2007.4338852

Filename

4338852