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Web of Proceedings - Francis Academic Press
Web of Proceedings - Francis Academic Press

The Research of Digital Currency

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DOI: 10.25236/icemeet.2019.439

Author(s)

Hongxiao Wei, Xinyi Liu

Corresponding Author

Hongxiao Wei

Abstract

We use K-mean clustering analysis to process the GDP data of 189 countries with the software SPSS. Based on the principle of Minimum Variance and the method of exponentially weighted, we successfully select three countries as the representatives of 189 countries. To figure out the most contributing indicator, we successfully achieved the dimensionality reduction of data by using the principal component analysis with the software PYTHON. We use Multiple Linear Regression to determine the parameters of our equation. And the equations of the three representative countries are obtained through the analysis results of EVIEWS. Then we use analytic hierarchy process (AHP) to find one suitable formulas for a particular country. We also performed the Sensitivity Analysis on the test results. Our second model can monitor tendency of GDP, making sure that it develops healthily and sustainably. How it was created and the way it works are as follows. We design a model using time series analysis with R. When leaders input the data of their country in recent years, the model will give them two sequence diagrams, and we have designed a set of inspection process to help them finding their current situation and making decisions. We randomly tested our model using real economic data from Canada, and the error analysis of the model suggest that the model has a high reliability. In addition to the two models above, we created an interesting model based on the theory of Population Competition. This model show the competition between the digital currency economy and the traditional currency economy in a vivid way. Besides the models above, we also did some theoretical analysis. And we design an Online Questionnaire and hand it out online in order to make our analysis more practical.

Keywords

Digital currency, Data fitting, Data dimension reduction