Analysis of Machine Learning and Deep Learning to Forecast Prices on Several Crypto Exchanges

Abstract

These days, virtual currencies-the term used to describe cryptocurrencies-are more well-known and have aroused the interest of numerous people. As a result of using blockchain technology, which decentralizes banking, daily cryptocurrency trading has become quite popular among observers, investors, customers, and many other groups. However, due to the daily fluctuations in the value of cryptocurrencies, prediction techniques enable stakeholders to look into the future and identify potential threats to their crucial investment operations. The increasing popularity of cryptocurrencies has made price predictions more promising for investors along with researchers. As artificial intelligence (AI) has advanced to such a level, forecasting the price of cryptocurrencies has grown in significance. In this study, we develop an approach based on machine learning and deep learning as a form of AI to forecast the price of various cryptocurrency exchanges, such as Bitcoin (BTC), Ethereum (ETH), BinanceCoin (BNB), and FTX (FTT), based on their various pricing points. The results from the machine learning models demonstrated that linear regression performed better in forecasting all types of cryptocurrency, with a maximum R2 score of 0.9477, 0.9232, 0.9204, and 0.8925 for BTC, ETH, BNB, and FTT, respectively. However, our study found that the Gated Recurrent Unit (GRU), a deep learning-based model, was the most effective algorithm for predicting the prices of all four types of cryptocurrencies. With GRU, we were able to predict the price of ETH with an R2 score as high as 0.9983, and we were also able to predict the prices of BTC, BNB, and FTT with R2 scores of 0.9969, 0.9772, and 0.9873, respectively. The proposed approach demonstrated superior results with minimal prediction errors on estimating the price of all the different crypto exchanges when the outcomes of our research were dealt with those of the existing studies.