PREDICTING ETHEREUM PRICE FLUCTUATIONS USING ARTIFICIAL NEURAL NETWORKS

Abstract

This research investigates the growing demand from investors, traders, and government entities for accurate forecasting of ethereum prices. As one of the pioneering crypto currencies, ethereum has gained traction, in part, due to its promise of freedom from centralized government control. Our methodology employs cutting-edge artificial neural networks (ANN) to forecast ethereum prices. The key advantage lies in the adaptability of these networks to capture the dynamic and often unpredictable patterns inherent in crypto currency markets. To enhance forecast accuracy and timeliness, we explore various lag configurations within intervals, specific time demonstrating the model's effectiveness through outcomes. Our the predictions resulting undergo thorough validation, with a focus on assessing the root mean square error as a critical performance metric. Consistently, the outcomes from our proposed artificial neural network model outperform traditional prediction methods, providing valuable insights for individuals, industries, and governmental bodies navigating the complex landscape of crypto currencies