Predicting Prices of Different Cryptocurrencies Based on LSTM Models

Abstract

For more than a decade, as the number and value of cryptocurrencies exploded, more and more investors flocked to the cryptocurrency market with the expectation of positive returns. The price of cryptocurrencies, on the other hand, is extremely volatile. As a result, there is a great need to develop an accurate price prediction model to assist investors in making decisions and profit. This paper focuses on developing an LSTM-based prediction model for Bitcoin, Ethereum, EOS, and Solana cryptocurrency price prediction and calculating their RMSE and MAPE. Furthermore, four models are compared using this calculated MAPE. Based on the comparison results, the impact of cryptocurrency volatility, liquidity, and technology level on the accuracy of the LSTM prediction model is also examined. The paper concludes that the LSTM model can predict the price of Bitcoin more accurately because Bitcoin has the least volatility, the most liquidity and uses the oldest but most secure consensus mechanism.