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.

本文研究了比特币价格预测中的挑战，特别是由于加密货币市场的高波动性和复杂的非线性动态，传统模型难以捕捉数据的转变和长程依赖性。我们提出了一种新的基于Mamba的状态空间模型（SSM）架构CryptoMamba，其实验结果表明，该模型在不同市场条件下提供了更准确的预测并展现出更强的泛化能力，具有实际应用的潜力和优势。

CryptoMamba：利用状态空间模型进行准确的比特币价格预测