Predictive Modelling of Bitcoin and Ethereum: A deep Dive into LSTM and Bi-LSTM

Abstract

Cryptocurrencies have seen a meteoric rise in popularity, embodying a new era of financial innovation and decentralized digital assets. The inherent volatility and intricate price behaviors in cryptocurrency markets pose significant forecasting challenges. This paper conducts a comprehensive comparative analysis of two leading cryptocurrencies, Bitcoin and Ethereum price dynamics utilizing predictive modeling techniques. The cryptocurrencies have been implemented using LSTM and Bi-LSTM models to predict price movements by identifying linear and non-linear relationships in historical price data. The performance of these models is assessed using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), which serve as benchmarks for predictive accuracy. The findings underscore the effectiveness of these models in predicting prices and provide insights into their operational efficiency, thereby enhancing real-time trading strategies and investment decisions. This research makes a substantial contribution to the financial technology sector by enhancing the understanding of how predictive models can effectively navigate the complexities of cryptocurrency markets, thus aiding investors and policymakers in making informed decisions.