Abstract
In the distributed systems landscape, Blockchain has catalyzed the rise of cryptocurrencies, merging enhanced security and decentralization with significant investment opportunities. Despite their potential, current research on cryptocurrency trend forecasting often falls short by simplistically merging sentiment data without fully considering the nuanced interplay between financial market dynamics and external sentiment influences. This paper presents a novel Dual Attention Mechanism (DAM) for forecasting cryptocurrency trends using multimodal time-series data. Our approach, which integrates critical cryptocurrency metrics with sentiment data from news and social media analyzed through CryptoBERT, addresses the inherent volatility and prediction challenges in cryptocurrency markets. By combining elements of distributed systems, natural language processing, and financial forecasting, our method outperforms conventional models like LSTM and Transformer by up to 20\% in prediction accuracy. This advancement deepens the understanding of distributed systems and has practical implications in financial markets, benefiting stakeholders in cryptocurrency and blockchain technologies. Moreover, our enhanced forecasting approach can significantly support decentralized science (DeSci) by facilitating strategic planning and the efficient adoption of blockchain technologies, improving operational efficiency and financial risk management in the rapidly evolving digital asset domain, thus ensuring optimal resource allocation.
Abstract (translated)
在分布式系统领域，区块链催生了加密货币的出现，将增强的安全性和去中心化与重要的投资机会相结合。尽管具有潜力，但当前关于加密货币趋势预测的研究往往止步于简单地将情感数据进行合并，而没有全面考虑金融市场动态与外部情感影响之间的复杂相互作用。本文提出了一种新颖的双注意力机制（DAM）用于预测加密货币趋势，利用多模态时间序列数据。我们的方法将加密货币指标与通过CryptoBERT分析的新闻和社会媒体中的情感数据相结合，解决了加密货币市场的固有波动性和预测挑战。通过结合分布式系统、自然语言处理和金融预测的元素，我们的方法在预测准确性上比传统的LSTM和Transformer模型提高了20%以上。这一进步加深了对分布式系统的理解，并在金融市场中具有实际意义，为加密货币和区块链技术利益相关者带来好处。此外，我们增强的预测方法可以显著支持去中心化科学（DeSci），通过促进战略规划以及区块链技术的有效采用，提高运营效率和金融风险管理，从而确保最优资源分配。
URL
https://arxiv.org/abs/2405.00522
