This paper explores the association between Bitcoin’s market price and a set of internal and external factors by employing the Bayesian structural time series approach (BSTS). The idea behind BSTS is to create a superposition of layers such as cycles, trend, and explanatory variables that are allowed to vary stochastically over time, additionally, it is possible to perform a variable ... Bayesian Regression and Bitcoin  ... It is important to understand what drives the fluctuations of the Bitcoin exchange price and to what extent they are predictable. In this paper, we study ... Shah et al. utilized a “latent source model” Bayesian regression created by Chen et al. , which is designed to leverage binary classification to predict Bitcoin price variations. Using a support vector machine algorithm, Georgoula et al.  examined the relationship between Bitcoin price and determinants including economic variables, technological factors, and sentimenFt. Bayesian regression to Bitcoin price prediction, which achieved high proﬁtability. Current work, however, does not explore or disclose the relationship between Bitcoin price and other features in the space, such as market capitalization 1. or Bitcoin mining speed. We sought to explore additional features surrounding the Bitcoin network to understand relationships in the problem space, if any ... In this paper, we discuss the method of Bayesian regression and its efficacy for predicting price variation of Bitcoin, a recently popularized virtual, cryptographic currency.
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