A Multisource Fusion Framework for Cryptocurrency Price Movement Prediction
Abstract
Predicting cryptocurrency price trends remains a major challenge due to the volatility and complexity of digital asset markets. Artificial intelligence (AI) has emerged as a powerful tool to address this problem. This study proposes a multisource fusion framework that integrates quantitative financial indicators, such as historical prices and technical indicators, with qualitative sentiment signals derived from X (formerly Twitter). Sentiment analysis is performed using Financial Bidirectional Encoder Representations from Transformers (FinBERT), a domain-specific BERT-based model optimized for financial text, while sequential dependencies are captured through a Bidirectional Long Short-Term Memory (BiLSTM) network. Experimental results on a large-scale Bitcoin dataset demonstrate that the proposed approach substantially outperforms single-source models, achieving an accuracy of approximately 96.8\%. The findings underscore the importance of incorporating real-time social sentiment alongside traditional indicators, thereby enhancing predictive accuracy and supporting more informed investment decisions.
Cite
@article{arxiv.2409.18895,
title = {A Multisource Fusion Framework for Cryptocurrency Price Movement Prediction},
author = {Saeed Mohammadi Dashtaki and Reza Mohammadi Dashtaki and Mehdi Hosseini Chagahi and Behzad Moshiri and Md. Jalil Piran},
journal= {arXiv preprint arXiv:2409.18895},
year = {2025}
}