ARMAX-modelling with Google Trends data for prediction of changes in volume in cryptocurrencies
This thesis examines the use of ARMAX models for predicting cryptocurrency trading volumes, with Google Trends data serving as an exogenous variable. The spec- ulative nature of cryptocurrencies suggests that market sentiment, reflected in search trends, could influence trading activity. To test this hypothesis, the predictive performance of ARMAX models is compared to ARMA models across six crypt