Forecasting Trends in Cryptocurrencies through the Application of Association Rule Mining Techniques

Abstract

Data mining in the stock market and cryptocurrencies is the most used. In this paper, we applied a data mining approach to implement association rules. Our significant contribution is to ascertain a robust correlation between four cryptocurrencies: Bitcoin, Litecoin, Ethereum, and Monero. Specifically, this paper used data mining techniques to predict and discover association rules between four cryptocurrencies (Bitcoin, Litecoin, Ethereum, and Monero) to identify optimal points for selling and buying. Our suggested models utilized the apriori algorithm to forecast and determine association rules in our datasets. Our significant contribution is to ascertain a robust correlation between four cryptocurrencies: Bitcoin, Litecoin, Ethereum, and Monero. Specifically, we aim to ascertain the current link between Bitcoin and other items during the next 24 hours. In addition, if there is a current buy or sell of Bitcoin, we can forecast, for instance, the movement of Litecoin over the next three hours. We have already carried out this prediction for the other items. Our objective is to propose a prediction model to generate and discover associations between the cryptocurrency, Bitcoin, Litecoin, Ethereum, and Monero. In our research, we used apriori algorithm to produce the association rules. We evaluated the quality of these rules using two metrics: Support and lift. Experiment analysis proves that our method successfully generates a strong association rule. We have already carried out this prediction for the other items