Analysis and Prediction of Bitcoin Price using Bernoulli RBM-based Deep Belief Networks

Abstract

Digital currency aims to decentralize online transactions with high efficiency and increased security. These operations are carried out using blockchain based systems to eliminate the need for centralized verification and authorization. With the emergence of these systems come a vast number of cryptocurrencies such as bitcoins and ether that are widely used. The need for the analysis of the trend of digital currency arises because of the frequent fluctuation of their value in the markets. In this paper, the prediction of the trend of one such cryptocurrency, bitcoins, is performed using a Deep Belief Network model that is pre-trained using Restricted Boltzmann Machines for studying the data for 2019 with different time intervals: minute-by-minute, hour-by-hour and day-by-day. This would help to identify the trend of the cryptocurrency bitcoin for analyzing the demand supply dynamics of their market capital. The trained model was evaluated with the measures of MAE, MSE and compared with the existing state-of-the-art models. The minute-by-minute prediction model performed best with a RMSE of 25.87 and a MAE of 14.83.