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

Author First name, Last name, Institution

Umar Islam, Iqra National University
Babar Shah, Zayed University
Abdullah A. Al-Atawi, University of Tabuk
Gioia Arnone, DISAQ University of Neaples “PARTHENOPE”
Mohamed R. Abonazel, Faculty of Graduate Studies for Statistical Research
Ijaz Ali, Iqra National University
Fernando Moreira, Instituto de Engenharia Electrónica e Telemática de Aveiro

Document Type

Article

Source of Publication

Neural Computing and Applications

Publication Date

1-1-2024

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.

DOI Link

10.1007/s00521-024-10169-3

ISSN

0941-0643

Publisher

Springer Science and Business Media LLC

Disciplines

Computer Sciences

Keywords

ARIMA, DL, Ethereum, EtherVoyant, ML, SARIMA

Scopus ID

85202185382

Recommended Citation

Islam, Umar; Shah, Babar; Al-Atawi, Abdullah A.; Arnone, Gioia; Abonazel, Mohamed R.; Ali, Ijaz; and Moreira, Fernando, "Empowering global ethereum price prediction with EtherVoyant: a state-of-the-art time series forecasting model" (2024). All Works. 6774.
https://zuscholars.zu.ac.ae/works/6774

Indexed in Scopus

yes

Open Access

no