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An Explainable Multimodal Framework for Real-Time Bitcoin Forecasting
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AI
DOI：10.1145/3777490.3777508.png)
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Abstract
En 中文
High-frequency crypto forecasting requires systems that are accurate, explainable, and designed for human decision-making. Bitcoin presents a unique challenge for Human-Centred AI (HCAI) due to its volatility and sensitivity to heterogeneous technical, fundamental, and sentiment signals. This paper presents an explainable multimodal framework for Bitcoin forecasting at 15-minute resolution. We align five modalities-market data, on-chain metrics, the Fear & Greed Index (FGI), news, and Reddit-onto a unified, leakage-safe 15-minute grid. We evaluate tree-based, sequential, and Multimodal Fusion Block (MFB) models for next-interval log-return prediction using chronological splits. Results show that while short-horizon prediction remains challenging, multimodal features consistently improve over structured baselines, particularly during event-driven periods. To ensure transparency, the framework integrates a dual-layer explanation system: SHapley Additive exPlanations (SHAP) attributions combined with large language model (LLM) narratives, ensuring outputs are both technically faithful and human-accessible. This work unlocks the black box of complex predictive architectures, transforming opaque multimodal signals into transparent, actionable decision support for high-frequency trading.
Keywords:
Bitcoin Forecasting
Multimodal Data
Machine Learning
Deep Learning
Explainable AI
SHAP
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