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.

翻译：在分布式系统领域，区块链推动了加密货币的兴起，将增强的安全性与去中心化特性及重大投资机遇融为一体。尽管具备潜在价值，当前关于加密货币趋势预测的研究往往存在不足——在融合情感数据时过于简化，未能充分考虑金融市场动态与外部情感影响之间错综复杂的相互作用。本文提出一种新颖的双重注意力机制（DAM），利用多模态时间序列数据预测加密货币趋势。该方法将关键加密货币指标与通过CryptoBERT分析的新闻及社交媒体情感数据相整合，针对加密货币市场的固有波动性与预测挑战提出解决方案。通过融合分布式系统、自然语言处理与金融预测的多重元素，我们的方法在预测精度上较LSTM和Transformer等传统模型提升高达20%。这一进展不仅深化了对分布式系统的理解，还对金融市场具有实际应用价值，惠及加密货币与区块链技术的相关利益方。此外，增强型预测方法能通过推动战略规划与区块链技术的高效采纳，显著支撑去中心化科学（DeSci），在快速演变的数字资产领域提升运营效率与金融风险管理水平，从而确保资源的最优配置。