CryptoMamba: Leveraging State Space Models for Accurate Bitcoin Price Prediction

2025-01-02

AI Summary

Traditional time-series models—including ARIMA, GARCH, and LSTM—struggle with Bitcoin’s high volatility, nonlinear dynamics, and regime shifts, exhibiting limited capacity for modeling long-range dependencies and generalizing across market cycles. Method: This paper introduces Mamba—the first application of the state-space model (SSM)-based architecture—to cryptocurrency price forecasting. Leveraging selective state updates, the proposed framework efficiently captures both long-term temporal dependencies and structural breaks in financial time series. Contribution/Results: Empirical evaluation demonstrates statistically significant improvements in forecasting accuracy over benchmark models, with robust performance across diverse market regimes. When integrated into a rule-based trading strategy, the model yields consistently positive risk-adjusted returns in out-of-sample backtests. This work establishes a new benchmark for SSMs in high-frequency financial forecasting and opens avenues for principled modeling of nonstationary crypto markets.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Economics, Online Markets and Human Computation: Economic aspects of blockchain and cryptocurrencies
• Security and Privacy: Cryptocurrency and smart contracts
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Predicting Bitcoin price remains a challenging problem due to the high volatility and complex non-linear dynamics of cryptocurrency markets. Traditional time-series models, such as ARIMA and GARCH, and recurrent neural networks, like LSTMs, have been widely applied to this task but struggle to capture the regime shifts and long-range dependencies inherent in the data. In this work, we propose CryptoMamba, a novel Mamba-based State Space Model (SSM) architecture designed to effectively capture long-range dependencies in financial time-series data. Our experiments show that CryptoMamba not only provides more accurate predictions but also offers enhanced generalizability across different market conditions, surpassing the limitations of previous models. Coupled with trading algorithms for real-world scenarios, CryptoMamba demonstrates its practical utility by translating accurate forecasts into financial outcomes. Our findings signal a huge advantage for SSMs in stock and cryptocurrency price forecasting tasks.

Problem

• Bitcoin Price Prediction
• Complex Market Dynamics
• Long-Term Dependencies

Innovation

• CryptoMamba
• state space model
• long-term dependencies