Empowering global ethereum price prediction with EtherVoyant: a state-of-the-art time series forecasting model

Abstract

Ethereum has emerged as a major platform for decentralized apps and smart contracts with the heightened interest in cryptocurrencies in recent years. Investors and market participants in the cryptocurrency space will find it increasingly important to use reliable price prediction models as Ethereum's popularity grows. To better estimate Ethereum prices around the world, we propose "EtherVoyant," a novel hybrid forecasting model that combines the advantages of ARIMA and SARIMA methods. To improve its forecasting abilities, EtherVoyant uses Ethereum price history to train ARIMA and SARIMA components independently before fusing their predictions. With the help of feature engineering and data preparation, we further improve the model so that it can deal with real-world difficulties like missing values and seasonality in the data. We also investigate hyperparameter optimization for the model's best possible performance. We compare EtherVoyant's forecasts against those of the more conventional ARIMA and SARIMA models to determine its efficacy. By providing more precise and trustworthy price forecasts, our trial results suggest that EtherVoyant is superior to the individual models. The importance of this study resides in the fact that it will lead to the creation of a sophisticated time series forecasting model that will be useful to cryptocurrency investors, traders, and decision-makers. We hope that by making EtherVoyant available on a worldwide scale, we will help advance the field of cryptocurrency analytics and encourage wider adoption of blockchain-based assets.