1. Learning Kullback-Leibler Divergence-Based Gaussian Model for Multivariate Time Series Classification
- Author
-
Lei Li, Ying He, Gongqing Wu, Xianyu Bao, Xuegang Hu, and Huicheng Zhang
- Subjects
Multivariate statistics ,Kullback–Leibler divergence ,graphical lasso ,General Computer Science ,Kullback-Leibler divergence ,Computer science ,02 engineering and technology ,symbols.namesake ,Lasso (statistics) ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,Statistics::Methodology ,General Materials Science ,Time series ,Hidden Markov model ,Divergence (statistics) ,multivariate time series ,business.industry ,General Engineering ,Gaussian model ,Pattern recognition ,Covariance ,classification ,symbols ,020201 artificial intelligence & image processing ,Artificial intelligence ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,business ,Gaussian network model ,lcsh:TK1-9971 - Abstract
The multivariate time series (MTS) classification is an important classification problem in which data has the temporal attribute. Because relationships between many variables of the MTS are complex and time-varying, existing methods perform not well in MTS classification with many attribute variables. Thus, in this paper, we propose a novel model-based classification method, called Kullback-Leibler Divergence-based Gaussian Model Classification (KLD-GMC), which converts the original MTS data into two important parameters of the multivariate Gaussian model: the mean vector and the inverse covariance matrix. The inverse covariance is the most important parameter, which can obtain the information between the variables. So that the more variables, the more information could be obtained by the inverse covariance, KLD-GMC can deal with the relationship between variables well in the MTS. Then the sparse inverse covariance of each subsequence is solved by Graphical Lasso. Furthermore, the Kullback-Leibler divergence is used as the similarity measurement to implement the classification of unlabeled subsequences, because it can effectively measure the similarity between different distributions. Experimental results on classical MTS datasets demonstrate that our method can improve the performance of multivariate time series classification and outperform the state-of-the-art methods.
- Published
- 2019