A Bayesian Regularized Neural Network for Analyzing Bitcoin Trends

R. Sujatha, V. Mareeswari, Jyotir Moy Chatterjee, Abd Allah A. Mousa, Aboul Ella Hassanien
2021 IEEE Access  
Bitcoin is a decentralized digital currency without a central bank or single administrator sent from user to user on the peer-to-peer bitcoin blockchain network without intermediaries' need. In this Bitcoin trend analysis work, initial attributes are considered from five sectors based on financial, social, token, network, and that count to thirteen attributes. The thirteen attributes considered are price, volume, market cap, a mean dollar invested age, social volume, social dominance,
more » ... t activity, transaction volume, token age consumed, token velocity, token circulation, market value to realized value, and realized cap. We apply the attribute selection and trend analysis mapped with potential seven attributes: Price, Volume, Market Cap, Social Dominance, Development Activity, Market Value to Realized Value & Realized Cap. We have conducted Nonlinear Autoregressive with External Input analysis considering seven attributes. The work employed three training algorithms to train a neural network as Levenberg-Marquard, Bayesian Regularization, and Scaled Conjugate Gradient algorithm. The Error histogram and regression plots results indicate that the Bayesian Regularized Neural Network is showing good performance and thus provides a better forecast. INDEX TERMS Bitcoin, market cap, neural network, realized cap, nonlinear autoregressive with external input (narx), neural network (NN), Levenberg-Marquard (LM), Bayesian Regularization (BR), Scaled Conjugate Gradient (SCG), Bayesian Regularized Neural Network (BRNN).
doi:10.1109/access.2021.3063243 fatcat:2esozlavpzeg5ounnlqucypbrq