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

ArXiv ID: 2405.00522 “View on arXiv”

Authors: Unknown

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.

Keywords: Dual Attention Mechanism, Cryptocurrency Forecasting, Multimodal Time-Series, CryptoBERT, Distributed Systems, Cryptocurrencies

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 5.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced deep learning architectures like attention mechanisms and LSTMs, suggesting significant mathematical complexity, but also includes concrete empirical components like data sourcing, model comparison, and a GitHub link, indicating moderate to high rigor.

  flowchart TD
    Start(["Research Goal:<br>Cryptocurrency Trend Forecasting<br>with Sentiment & Financial Data"]) --> Inputs
    
    subgraph Inputs ["Data/Inputs"]
        A["Financial Time-Series<br>(Price, Volume)"]
        B["Sentiment Data<br>News & Social Media"]
        C["CryptoBERT<br>Pre-trained Model"]
    end
    
    Inputs --> Methodology
    
    subgraph Methodology ["Key Methodology Steps"]
        D["Dual Attention Mechanism<br>Integrates Multimodal Data"]
        E["Model Architecture<br>LSTM/Transformer Baseline"]
    end
    
    Methodology --> Process
    
    subgraph Process ["Computational Processes"]
        F["Training & Optimization<br>Outperforms Baselines by 20%"]
    end
    
    Process --> Outcomes(["Key Findings & Outcomes<br>Enhanced Forecasting Accuracy<br>Supports DeSci & Risk Management"])