Since Bitcoin first appeared on the scene in 2009, cryptocurrencies have become a worldwide phenomenon as important decentralized financial assets. Their decentralized nature, however, leads to notable volatility against traditional fiat currencies, making the task of accurately forecasting the crypto-fiat exchange rate complex. This study examines the various independent factors that affect the volatility of the Bitcoin-Dollar exchange rate. To this end, we propose CoMForE, a multimodal AdaBoost-LSTM ensemble model, which not only utilizes historical trading data but also incorporates public sentiments from related tweets, public interest demonstrated by search volumes, and blockchain hash-rate data. Our developed model goes a step further by predicting fluctuations in the overall cryptocurrency value distribution, thus increasing its value for investment decision-making. We have subjected this method to extensive testing via comprehensive experiments, thereby validating the importance of multimodal combination over exclusive reliance on trading data. Further experiments show that our method significantly surpasses existing forecasting tools and methodologies, demonstrating a 19.29% improvement. This result underscores the influence of external independent factors on cryptocurrency volatility.

翻译：自2009年比特币首次问世以来，加密货币已成为全球范围内重要的去中心化金融资产。然而，其去中心化特性导致其相对于传统法定货币具有显著波动性，使得准确预测加密货币与法币汇率变得复杂。本研究探讨了影响比特币-美元汇率波动性的多种独立因素。为此，我们提出了CoMForE——一种多模态AdaBoost-LSTM集成模型，该模型不仅利用历史交易数据，还整合了相关推文中的公众情绪、搜索量所体现的公众兴趣以及区块链哈希率数据。我们开发的模型进一步通过预测整体加密货币价值分布的波动，从而提升了其在投资决策中的价值。我们通过全面实验对该方法进行了广泛测试，验证了多模态组合相较于仅依赖交易数据的重要性。进一步的实验表明，我们的方法显著超越了现有预测工具与方法，实现了19.29%的性能提升。这一结果凸显了外部独立因素对加密货币波动性的影响。