An Empirical Study on Modeling and Prediction of Bitcoin Prices With Bayesian Neural Networks Based on Blockchain Information

TLDR

Bitcoin has attracted significant attention across economics, cryptography, and computer science because it uniquely blends encryption technology with a digital monetary system. The study investigates the impact of Bayesian neural networks on Bitcoin price prediction by analyzing Bitcoin time series and selecting blockchain-derived features to enhance forecasting. The authors train BNNs using the selected blockchain features and compare their performance against linear and nonlinear benchmark models in an empirical study. The results demonstrate that BNNs accurately predict Bitcoin price time series and capture its high volatility.

Abstract

Bitcoin has recently attracted considerable attention in the fields of economics, cryptography, and computer science due to its inherent nature of combining encryption technology and monetary units. This paper reveals the effect of Bayesian neural networks (BNNs) by analyzing the time series of Bitcoin process. We also select the most relevant features from Blockchain information that is deeply involved in Bitcoin's supply and demand and use them to train models to improve the predictive performance of the latest Bitcoin pricing process. We conduct the empirical study that compares the Bayesian neural network with other linear and non-linear benchmark models on modeling and predicting the Bitcoin process. Our empirical studies show that BNN performs well in predicting Bitcoin price time series and explaining the high volatility of the recent Bitcoin price.