DAM：用于多模态时间序列加密货币趋势预测的通用双注意力机制
DAM: A Universal Dual Attention Mechanism for Multimodal Timeseries Cryptocurrency Trend Forecasting
AI总结：
本文提出双注意力机制（DAM），融合加密货币指标与新闻社交媒体情绪数据，预测加密货币趋势，较LSTM和Transformer准确率提升达20%，支持去中心化科学应用。
摘要：
在分布式系统领域，区块链技术推动了加密货币的兴起，将增强的安全性和去中心化与显著的投资机会相结合。尽管具有潜力，当前加密货币趋势预测的研究往往因简单合并情绪数据而未能充分考虑金融市场动态与外部情绪影响之间细微的相互作用而有所不足。本文提出了一种新颖的双注意力机制（DAM），利用多模态时间序列数据预测加密货币趋势。我们的方法将关键加密货币指标与通过CryptoBERT分析的来自新闻和社交媒体的情绪数据相结合，解决了加密货币市场固有的波动性和预测挑战。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率上比LSTM和Transformer等传统模型高出最多20%。这一进展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，惠及加密货币和区块链技术的利益相关者。此外，我们增强的预测方法可通过促进战略规划和区块链技术的有效采用，显著支持去中心化科学（DeSci），在快速演变的数字资产领域提高运营效率和金融风险管理，从而确保最优资源分配。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
