Digital Currency Price Analysis Via Deep Forecasting Approaches for Business Risk Mitigation

Abstract

Bitcoin, the most well-known of all the cryptocurrencies, have attracted a lot of attention thus far, and their prices have been quite volatile. While some research employ traditional statistical and econometric methods to discover the factors that drive Bitcoin prices, experimenting on the development of prediction models to be utilized as decision support aids in investment approaches is uncommon. The sudden rise and fall of cryptocurrency rates affects the economies and future perspectives of various businesses. In order to minimize business risks, to track the differences and avoid serious economic loss, prediction of daily digital currency rates becomes a crucial task. Our study performs a comparative analysis of Bitcoin price prediction utilizing efficient neural network techniques such as LSTM and GRU. A better RNN-based approach is derived as a result of the study. This approach will assist to facilitate a secure environment for businesses and to alarms to carryout risk management tasks for business risk mitigation purposes.