# DAM: A Universal Dual Attention Mechanism for Multimodal Timeseries Cryptocurrency Trend Forecasting arXiv: https://arxiv.org/abs/2405.00522 ยท HTML: https://mlsanity.com/paper/2405.00522/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Builds on 34 of its 93 references that are in the map. - First posted on arXiv 1 May 2024. ## Builds on (most cited first, 30 of 34) - [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022) - [Are Transformers Effective for Time Series Forecasting?](https://mlsanity.com/paper/2205.13504/) (2022) - [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014) - [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019) - [Transformers in Time Series: A Survey](https://mlsanity.com/paper/2202.07125/) (2022) - [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021) - [A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction](https://mlsanity.com/paper/1704.02971/) (2017) - [Large Language Models in Finance: A Survey](https://mlsanity.com/paper/2311.10723/) (2023) - [Temporal Pattern Attention for Multivariate Time Series Forecasting](https://mlsanity.com/paper/1809.04206/) (2018) - [LSTM-MSNet: Leveraging Forecasts on Sets of Related Time Series with Multiple Seasonal Patterns](https://mlsanity.com/paper/1909.04293/) (2019) - [NeuralProphet: Explainable Forecasting at Scale](https://mlsanity.com/paper/2111.15397/) (2021) - [Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary Time-Series](https://mlsanity.com/paper/2202.02403/) (2022) - [Improving Stock Market Prediction via Heterogeneous Information Fusion](https://mlsanity.com/paper/1801.00588/) (2018) - [A cross-modal fusion network based on self-attention and residual structure for multimodal emotion recognition](https://mlsanity.com/paper/2111.02172/) (2021) - [Transformer-Based Deep Learning Model for Stock Price Prediction: A Case Study on Bangladesh Stock Market](https://mlsanity.com/paper/2208.08300/) (2022) - [Multimodal deep learning for short-term stock volatility prediction](https://mlsanity.com/paper/1812.10479/) (2018) - [Real-Time Prediction of BITCOIN Price using Machine Learning Techniques and Public Sentiment Analysis](https://mlsanity.com/paper/2006.14473/) (2020) - [Information Fusion in Attention Networks Using Adaptive and Multi-level Factorized Bilinear Pooling for Audio-visual Emotion Recognition](https://mlsanity.com/paper/2111.08910/) (2021) - [Empirical Analysis of EIP-1559: Transaction Fees, Waiting Time, and Consensus Security](https://mlsanity.com/paper/2201.05574/) (2022) - [Show me your NFT and I tell you how it will perform: Multimodal representation learning for NFT selling price prediction](https://mlsanity.com/paper/2302.01676/) (2023) - [Decoding Social Sentiment in DAO: A Comparative Analysis of Blockchain Governance Communities](https://mlsanity.com/paper/2311.14676/) (2023) - [A Dataset of Uniswap daily transaction indices by network](https://mlsanity.com/paper/2312.02660/) (2023) - [Analyzing Reward Dynamics and Decentralization in Ethereum 2.0: An Advanced Data Engineering Workflow and Comprehensive Datasets for Proof-of-Stake Incentives](https://mlsanity.com/paper/2402.11170/) (2024) - [SoK: Blockchain Decentralization](https://mlsanity.com/paper/2205.04256/) (2022) - [Blockchain Network Analysis: A Comparative Study of Decentralized Banks](https://mlsanity.com/paper/2212.05632/) (2022) - [The Design Principle of Blockchain: An Initiative for the SoK of SoKs](https://mlsanity.com/paper/2301.00479/) (2023) - [On the Mechanics of NFT Valuation: AI Ethics and Social Media](https://mlsanity.com/paper/2307.10201/) (2023) - ["Centralized or Decentralized?": Concerns and Value Judgments of Stakeholders in the Non-Fungible Tokens (NFTs) Market](https://mlsanity.com/paper/2311.10990/) (2023) - [Cryptocurrency Valuation: An Explainable AI Approach](https://mlsanity.com/paper/2201.12893/) (2022) - [A Data Science Pipeline for Algorithmic Trading: A Comparative Study of Applications for Finance and Cryptoeconomics](https://mlsanity.com/paper/2206.14932/) (2022) ## Built on by (most cited first, 2 of 2) - [Global Public Sentiment on Decentralized Finance: A Spatiotemporal Analysis of Geo-tagged Tweets from 150 Countries](https://mlsanity.com/paper/2409.00843/) (2024) - [StableEval Arena: A Cost-Aware Agentic Benchmark for Stablecoin Price Stability Prediction](https://mlsanity.com/paper/2609.18949/) (2026) ## Abstract (arXiv) 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.