Different Cryptocurrencies’ Transaction Forecasting Using Machine Learning

With the increasing geopolitical and economic issues in recent years, traditional currencies and stock markets have been facing challenges, causing global currency values to fall and investors to lose wealth. In this context, digital currencies, particularly cryptocurrencies, have gained significant attention due to their stable performance in the market. This article introduces an innovative method for creating a cryptocurrency transaction forecast model, DL forecast, using deep neural networks for learning cryptocurrency transaction network representations. The objective of this research is to investigate the potential of deep learning techniques in forecasting cryptocurrency transactions, detecting illicit transactions, and identifying interesting transaction behaviors. To achieve this aim, we used Python to develop the DL forecast model and trained it on a large dataset of cryptocurrency transactions. The model achieved an accuracy score of over 98% on the test dataset, demonstrating its effectiveness in forecasting cryptocurrency transactions. Furthermore, we showed that the DL forecast model can be employed for the purpose of identifying specific intriguing transaction patterns, overseeing valid transactions, and recognizing unauthorized entities within the realm of cryptocurrency. The results of this study have significant implications for the cryptocurrency industry, providing insights into the practical applications of deep learning techniques in the domain. The DL forecast model can be utilized by regulators, exchanges, and law enforcement agencies to improve monitoring and detection of illicit activities, such as those conducted on dark marketplaces, ransom ware operators, and fraudsters. Additionally, the model can assist in identifying legitimate transactions and behaviors, such as those conducted by regulated exchanges, merchants, and wallet services.