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Cryptocurrencies have gained popularity across various sectors, especially infinance and investment. Despite their growing popularity, cryptocurrencies canbe a high-risk investment due to their price volatility. The inherentvolatility in cryptocurrency prices, coupled with the effects of externalglobal economic factors, makes predicting their price movements challenging. Toaddress this challenge, we propose a dynamic Bayesian network (DBN)-basedapproach to uncover potential causal relationships among various featuresincluding social media data, traditional financial market factors, andtechnical indicators. Six popular cryptocurrencies, Bitcoin, Binance Coin,Ethereum, Litecoin, Ripple, and Tether are studied in this work. The proposedmodel's performance is compared to five baseline models of auto-regressiveintegrated moving average, support vector regression, long short-term memory,random forests, and support vector machines. The results show that while DBNperformance varies across cryptocurrencies, with some cryptocurrenciesexhibiting higher predictive accuracy than others, the DBN significantlyoutperforms the baseline models.

Dynamic Bayesian Networks for Predicting Cryptocurrency Price Directions: Uncovering Causal Relationships