Weighted Local Complexity Invariance for Time Series Classification

Authors

Yitong Li, 1.Guangzhou Institute of Technology, Xidian University, Guangzhou 510555, China;Follow
Xiaotao Liu, 1.Guangzhou Institute of Technology, Xidian University, Guangzhou 510555, China;Follow
Jing Liu, 1.Guangzhou Institute of Technology, Xidian University, Guangzhou 510555, China;
Kai Wu, 2.Department of Artificial Intelligence, Xidian University, Xi'an 710071, China;

Abstract

Abstract: Aiming at the misclassification of existing algorithms for long or unevenly distributed time series, the local complexity information is extracted and weighted local complexity-invariant distance (WLCID) is proposed, which includes the local complexity representation model and the weighted global complexity integration model. Sliding window is used to split up time series, and combined with the complexity-invariant distance, the local complexity information can be extracted. As to the class representation model, the integration weights are quantified with the normalized cumulative between-class distance, with the perspective that the subsequence contributes more greatly with larger between-class distance. Compared with other similar algorithms, the proposed method is good at dealing with the data with uneven complexity distribution and can also performs better in most of the test datasets processing. Besides classification tasks, the improvement in the accuracy of clustering tasks also shows its ability to represent the complexity information of the morphological characteristics of time series.

Recommended Citation

Li, Yitong; Liu, Xiaotao; Liu, Jing; and Wu, Kai (2022) "Weighted Local Complexity Invariance for Time Series Classification," Journal of System Simulation: Vol. 34: Iss. 10, Article 9.
DOI: 10.16182/j.issn1004731x.joss.21-0456
Available at: https://dc-china-simulation.researchcommons.org/journal/vol34/iss10/9

First Page

2194

Revised Date

2021-09-01

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.21-0456

Last Page

2203

CLC

TP391.9

Recommended Citation

Yitong Li, Xiaotao Liu, Jing Liu, Kai Wu. Weighted Local Complexity Invariance for Time Series Classification[J]. Journal of System Simulation, 2022, 34(10): 2194-2203.

Corresponding Author

Xiaotao Liu,xtliu@xidian.edu.cn

DOI

10.16182/j.issn1004731x.joss.21-0456