Evaluating LSTM and GRU Models for Cryptocurrency Price Forecasting in Financial Markets
Isha Mehra, Manoj Kumar Tyagi, Shilpa Kanthalia, T. Bhavya Shree, Rajnish Kumar Chaturvedi, Prabhishek Singh
Abstract
Cryptocurrencies are a type of digital money distinguished by a decentralized system that uses encryption to authenticate transactions and keep records, obviating the need for a central authority. A key element of these digital assets is the decentralization of power from a single entity to a dispersed network. The extreme price volatility of cryptocurrencies, on the other hand, has a significant influence on international commerce, making precise price forecasting critical for investors and traders. In this study, the investigation is made on how deep learning models, especially the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), may be used to solve the problem of excessive price swings in cryptocurrencies like as Bitcoin, Ethereum, Litecoin, and Dogecoin. Previous research has looked at forecasting methods such as ARIMA and Support Vector Machines (SVM), but the findings have fallen short of the promising results obtained by the deep learning models studied in this work. Our major objective is to create strong forecasting models based on LSTM and GRU, with an emphasis on their ability to make credible forecasts for bitcoin values. We find that GRU consistently beats LSTM for the majority of the cryptocurrencies under consideration by comparing their performance using two separate error prediction approaches, namely mean absolute percentage error (MAPE) and root mean square error (RMSE). This study's findings help to enhance prediction methods for understanding and reducing the impact of cryptocurrency price volatility on international commerce. Deep learning algorithms for projecting bitcoin values give significant insights for investors and decision-makers navigating the volatile and ever-changing terrain of the crypto market.