Predictive Modeling in Cryptocurrency: A case Study of Bitcoin using RNN

Abstract

Cryptocurrencies represent a digital form of currency, facilitating electronic transactions without the existence of physical hard notes. Distinguished from fiat currency, these digital assets operate on a decentralized system, allowing users to access services without third-party intervention. The utilization of cryptocurrencies significantly influences international relations and trade, primarily due to their notable price volatility. Within the realm of virtual currencies, notable examples include Bitcoin, Ripple, Ethereum, Ethereum Classic, Litecoin, among others. Our research places particular emphasis on Bitcoin, a widely accepted cryptocurrency among diverse stakeholders, including investors, researchers, traders, and policy-makers. Our primary objective is to implement efficient deep learning-based prediction models, specifically focusing on LSTM and GRU architectures. These models are tailored to effectively address the challenge presented by the inherent price volatility of Bitcoin and aim to achieve heightened prediction accuracy.Our research aims to conduct a comprehensive evaluation of LSTM and GRU, sophisticated deep learning methodologies widely employed in the realm of time series analysis. By concentrating on Bitcoin, we contribute valuable insights to the understanding of cryptocurrency market dynamics, with the ultimate goal of enhancing accuracy in predicting price fluctuations.