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

TL;DR

This paper introduces DAM, a dual attention mechanism that effectively combines multimodal time-series data, including sentiment analysis, to improve cryptocurrency trend forecasting accuracy over traditional models.

Contribution

The paper proposes a novel Dual Attention Mechanism (DAM) that integrates financial metrics and sentiment data for enhanced cryptocurrency trend prediction.

Findings

DAM outperforms LSTM and Transformer models by up to 20% in accuracy.

Incorporates sentiment analysis from news and social media using CryptoBERT.

Addresses volatility and complexity in cryptocurrency markets.

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.