Information Support of the Entrepreneurship Model Complex with the Application of Cloud Technologies

Abstract

The study has proved that the use of V-statistics is a reliable tool for assessing the forecast horizon of the entrepreneurship model. According to the results obtained, the forecast time horizon for most of the studied cryptocurrencies does not exceed 30 days when using daily observations. Computer experiments have confirmed the effectiveness of the implementation of machine learning methods and algorithms for solving the problems of forecasting the short-term dynamics of financial instruments, for example, cryptocurrency. In particular, a BART model has been developed or ARIMA-ARFIMA ensembles can be used as the basis for algorithms for automated trading systems designed for online trading. The developed methodological approach and recommendations on the practical application of the system of economic and mathematical models based on the tools of binary autoregressive trees and data mining allow developing a short-term forecast of innovative financial instruments in order to make effective investment decisions on the crypto market. A comprehensive analysis of the state and dynamics of the cryptocurrency market, their features, advantages, and disadvantages allows concluding that despite significant fluctuations in their exchange value and gaps in legislative regulation, digital currencies are a modern stage in the evolution of means of payment. Therefore, crypto assets and IT technologies on their platforms quickly adapt in the modern globalized world and will occupy a worthy place in the innovative digital economy.