Study of the intelligent system of classification of currency volatility according to the results of machine learning

Abstract

The process of classification and analysis of currency volatility data using machine learning methods is considered. Machine learning models and methods are studied, namely the naive Bayesian classifier and the support vector method for solving classification problems based on the generated data set. The purpose of the study is to develop and research an intelligent system that is able to effectively classify the level of volatility in currency markets. The work is aimed at studying different machine learning methods and classifiers based on different measures of similarity, with the aim of determining the optimal approach for volatility classification.