Abstract
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.
Highlights
In time series analysis and, generally, in statistics we are interested in the correlation between variables which is often investigated with the aim of discovering the degree and the extent of their association
Such techniques are highly useful in cases where financial variables and/or economic indicators are measured across groups, and the purpose of the analysis is the classification into clusters based on the degree of closeness among groups
We proposed three new distance measures for measuring the distance between multivariate time series by way of causal relationship
Summary
In time series analysis and, generally, in statistics we are interested in the correlation between variables which is often investigated with the aim of discovering the degree and the extent of their association. The proposed algorithms are suitable for classification purposes for both univariate and multivariate time series where causal relationships are frequently present. Such techniques are highly useful in cases where financial variables and/or economic indicators are measured across groups (regions, zones, countries, etc.), and the purpose of the analysis is the classification into clusters based on the degree of closeness among groups. Monitoring, spread prevention and healthcare efficiency are classical issues of importance for health officials In such studies, multivariate techniques for time series analysis and health indicators play a key role. The last section is devoted to some general concluding remarks
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
