RMSE calculation of LSTM models for predicting prices of different cryptocurrencies

Cryptocurrencies are becoming popular day by day and their use in financial applications has also increased; hence they are recognized as a method for payments. For decades, trading in cryptocurrency has been popular and constantly increasing in financial markets; hence investors are looking forward to their positive returns through these cryptocurrencies. However, volatility of cryptocurrency prices is very significant; the volatility in financial markets records the changes in the price of a cryptocurrency. Steady increases or decreases in price within a certain range can be healthy. There can also be rapidchanges in prices in either direction. Healthy volatility mainly opens profit opportunities. Investors can invest accordingly and earn maximum profit. So, there is a need to assess the investment concerning the profit scenario. An accurate price prediction model helps investors to make decisions and earn profits. Hence, there is a strong need for developing an accurate price prediction model. In this research work, an LSTM model is used to predict five major cryptocurrencies that are; Ethereum, Cardano ADA, EOS, NEO, and Tron. For measuring model performance, RMSE (Root Mean Squared Error) has been computed for all five cryptocurrencies. It has been observed that Tron cryptocurrency has the least RMSE and Ethereum has the worst RMSE. Hence, it can be concluded that the LSTM model is the best fit for the Tron dataset and can predict accurate prices for the future; therefore, the investor can make an investment and earnprofit.