Selective State Propagation for Real-Time Crypto Asset Forecasting with Subquadratic Attention Alternatives

Abstract

Cryptocurrency markets present unique challenges for real-time forecasting due to their high volatility, continuous trading cycles, and sensitivity to external information flows. Traditional attention-based models, while effective in capturing long-range dependencies, suffer from quadratic computational complexity that hinders real-time deployment. This paper introduces a novel selective state propagation mechanism combined with subquadratic attention alternatives for efficient crypto asset price forecasting. Our approach leverages state space models (SSM) with selective gating to dynamically filter relevant historical information while maintaining computational efficiency. The proposed architecture achieves O(N log N) complexity compared to O(N²) in standard transformers, enabling microsecond-level inference suitable for high-frequency trading environments. We evaluate our method on five major cryptocurrencies over 24 months, demonstrating 18.3% improvement in mean absolute percentage error (MAPE) and 23.7% reduction in inference latency compared to transformer baselines. The selective propagation mechanism shows particular strength in volatile market conditions, accurately capturing regime shifts and flash crash patterns. Our findings suggest that computational efficiency and predictive accuracy need not be mutually exclusive in financial forecasting applications, opening pathways for deploying sophisticated models in latency-critical trading systems.