DAM: A Universal Dual Attention Mechanism for Multimodal Timeseries Cryptocurrency Trend Forecasting

In the realm of distributed systems, blockchain technology has spurred the development of cryptocurrencies, merging enhanced security and decentralization with significant investment potential. Despite their promise, existing cryptocurrency trend forecasting often oversimplifies sentiment data, failing to fully capture the complex interplay between financial market dynamics and external sentiment influences. This paper introduces a novel Dual Attention Mechanism (DAM) for forecasting cryptocurrency trends using multimodal time-series data. Our approach integrates key cryptocurrency metrics with sentiment data from news and social media, analyzed through CryptoBERT, effectively addressing 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, such as LSTM and Transformer, by up to 20% in prediction accuracy. This advancement not only deepens the understanding of distributed systems but also has practical implications for stakeholders in the cryptocurrency and blockchain sectors.