融合价格数据与社交媒体情绪，提升比特币走势预测准确率
A Multisource Fusion Framework for Cryptocurrency Price Movement Prediction
- 整合历史价格、技术指标与推特情绪信号
- 在比特币数据上达到96.8%预测准确率
- 适合量化交易与金融决策研究者参考
由于数字资产市场的波动性和复杂性，预测加密货币价格趋势仍是重大挑战。人工智能（AI）已成为应对该问题的强大工具。本文提出一种多源融合框架，将量化金融指标（如历史价格和技术指标）与来自X（原推特）的定性情绪信号相结合。情绪分析采用针对金融文本优化的领域专用BERT模型FinBERT，序列依赖关系通过双向长短期记忆网络（BiLSTM）捕捉。在大规模比特币数据集上的实验结果表明，所提方法显著优于单源模型，预测准确率约为96.8%。研究结果强调了结合实时社交情绪与传统指标的重要性，从而提升预测精度，支持更明智的投资决策。
原文摘要 · Abstract (English)
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.
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