The Development of a Unified Predictive Model to Predict Closed Price for a Variety of Cryptocurrencies

Predicting the closing price of a cryptocurrency typically involves utilizing an individual predictive model for each particular cryptocurrency. However, a unified model has the potential to predict the closing price of many cryptocurrencies, providing convenience and facilitating comparative research. Thus, this study utilized feature-based data fusion to integrate historical data of cryptocurrencies from the same time period using a data fusion, called as the average method. The predictive models were developed using two machine learning algorithms, i.e. Support Vector Regression (SVR) with RBF kernel and Random Forest (RF). This study used three types of cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), and Litecoin (LTC). After evaluating the effectiveness of several predictive models utilizing MAE, RMSE, and MAPE for short-term (5-day) predictions, it was found that the unified predictive model yielded similar outcomes to the models specifically d eveloped f or the given particular cryptocurrency. This method offers the ability to help investors in identifying general price movements among multiple cryptocurrencies and decreasing the amount of time needed for observations. However, the unified p redictive models developed by the random forest algorithm surpass other models in successfully predicting the short-term (5-day) closing prices of cryptocurrencies.