Using Multivariate Adaptive Regression Splines in the Construction of Simulated Soccer Team's Behavior Models

Building fuzzy time series model from unsupervised learning technique and genetic algorithm

This paper proposes a new model to interpolate time series and forecast it effectively for the future. The important contribution of this study is the combination of optimal techniques for fuzzy clustering problem using genetic algorithm and forecasting model for fuzzy time series. Firstly, the proposed model finds the suitable number of clusters for a series and optimizes the clustering problem by the genetic algorithm using the improved Davies and Bouldin index as the objective function. Secondly, the study gives the method to establish the fuzzy relationship of each element to the established clusters. Finally, the developed model establishes the rule to forecast for the future. The steps of the proposed model are presented clearly and illustrated by the numerical example. Furthermore, it has been realized positively by the established MATLAB procedure. Performing for a lot of series (3007 series) with the differences about characteristics and areas, the new model has shown the significant performance in comparison with the existing models via some parameters to evaluate the built model. In addition, we also present an application of the proposed model in forecasting the COVID-19 victims in Vietnam that it can perform similarly for other countries. The numerical examples and application show potential in the forecasting area of this research.

The Relative Importance of Globalization and Public Expenditure on Life Expectancy in Europe: An Approach Based on MARS Methodology

BACKGROUND

There has been a widespread debate about the overall impact of globalization on population, not just economically, but also in terms of health status. Moreover, the current health crisis is going to force governments to review the structure of the public budget to most effectively alleviate the negative economic and health effects on the population.

OBJECTIVE

The aim of this paper is to analyze the relative importance of globalization and the public budget composition-specifically the participation of public expenditure on healthcare, social services and environment in gross domestic product (GDP)-on life expectancy at birth in European countries during the period 1995-2017.

METHODS

The Multivariate Adaptive Regression Splines (MARS) methodology was applied to analyze the socioeconomic determinants of life expectancy at birth.

RESULTS

Our findings show that globalization has no relative importance as an explanatory variable of life expectancy in European countries, while government expenditure on social protection is the most relevant followed by public expenditure on health, gross national income per capita, education level of the population and public expenditure on environmental protection.

CONCLUSION

European strategies intended to impact on health outcome should spend more attention to the composition of public budget.