Abstract
In order to explore the relationship between exchange rate fluctuations and oil prices, this paper combines the copula function to study the correlation between exchange rate fluctuations and oil price changes, conducts a more comprehensive study of the copula function, and applies the algorithm to some practical classification problems. Moreover, this paper improves some defects in the algorithm and combines some new learning frameworks in machine learning to generalize the copula function to a variety of learning models. In addition, this paper studies how to use the coverage algorithm to construct classifiers under various problems and proposes corresponding improvement strategies according to the characteristics of various problems. Finally, this paper builds a correlation analysis algorithm model and uses simulation research to verify that there is a relatively obvious correlation between exchange rate fluctuations and oil price changes.
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