Sequence-As-Feature Representation for Subspace Classification of Multivariate Time Series

Author(s):  
Liang Yuan ◽  
Lifei Chen ◽  
Rong Xie ◽  
Huihuang Hsu
Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2708
Author(s):  
Achilleas Anastasiou ◽  
Peter Hatzopoulos ◽  
Alex Karagrigoriou ◽  
George Mavridoglou

In this work, we focus on the development of new distance measure algorithms, namely, the Causality Within Groups (CAWG), the Generalized Causality Within Groups (GCAWG) and the Causality Between Groups (CABG), all of which are based on the well-known Granger causality. The proposed distances together with the associated algorithms are suitable for multivariate statistical data analysis including unsupervised classification (clustering) purposes for the analysis of multivariate time series data with emphasis on financial and economic data where causal relationships are frequently present. For exploring the appropriateness of the proposed methodology, we implement, for illustrative purposes, the proposed algorithms to hierarchical clustering for the classification of 19 EU countries based on seven variables related to health resources in healthcare systems.


2015 ◽  
Vol 54 ◽  
pp. 61.e29-64 ◽  
Author(s):  
Zhenlong Li ◽  
Xue Jin ◽  
Xiaohua Zhao

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