A Context-Enriched Hybrid ARIMAX–Deep Learning Framework for Robust Cryptocurrency Price Forecasting

Gerasimos Vonitsanos, Andreas Kanavos, Phivos Mylonas

2025

Abstract

The inherent volatility and nonlinear dynamics of cryptocurrency markets pose substantial challenges to accurate price forecasting. This paper proposes a novel context-enriched hybrid modeling framework that integrates classical time series analysis with deep learning techniques to enhance prediction accuracy for Bitcoin price movements. A comprehensive evaluation is conducted on ARIMA, ARIMAX, Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) networks using high-resolution market data from 2019 to 2024. The framework leverages exogenous variables-such as trading volume, market capitalization, and moving averages-to enrich model inputs and capture contextual signals. Experimental results demonstrate that hybrid configurations, particularly ARIMAX-based models, consistently achieve the lowest Root Mean Squared Error (RMSE) and highest coefficient of determination (R2), closely tracking real market trends. These findings confirm the effectiveness of combining statistical rigor with the nonlinear learning capabilities of deep architectures. Furthermore, the study highlights the potential of extending this approach with ensemble strategies for even greater robustness. This work contributes to the development of accurate, data-driven forecasting tools for decision-making in highly dynamic and speculative digital asset markets.

Paper Citation

in Harvard Style

Vonitsanos G., Kanavos A. and Mylonas P. (2025). A Context-Enriched Hybrid ARIMAX–Deep Learning Framework for Robust Cryptocurrency Price Forecasting. In Proceedings of the 21st International Conference on Web Information Systems and Technologies - Volume 1: WEBIST; ISBN 978-989-758-772-6, SciTePress, pages 257-266. DOI: 10.5220/0013713700003985

in Bibtex Style

@conference{webist25,
author={Gerasimos Vonitsanos and Andreas Kanavos and Phivos Mylonas},
title={A Context-Enriched Hybrid ARIMAX–Deep Learning Framework for Robust Cryptocurrency Price Forecasting},
booktitle={Proceedings of the 21st International Conference on Web Information Systems and Technologies - Volume 1: WEBIST},
year={2025},
pages={257-266},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013713700003985},
isbn={978-989-758-772-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Web Information Systems and Technologies - Volume 1: WEBIST
TI - A Context-Enriched Hybrid ARIMAX–Deep Learning Framework for Robust Cryptocurrency Price Forecasting
SN - 978-989-758-772-6
AU - Vonitsanos G.
AU - Kanavos A.
AU - Mylonas P.
PY - 2025
SP - 257
EP - 266
DO - 10.5220/0013713700003985
PB - SciTePress