用状态空间模型提升比特币价格预测准确率
CryptoMamba: Leveraging State Space Models for Accurate Bitcoin Price Prediction
- 基于Mamba的SSM架构捕捉金融时序的长期依赖
- 在不同市场条件下预测误差显著低于传统模型
- 适合量化交易与金融科技领域应用
由于加密货币市场的高波动性和复杂的非线性动态，比特币价格预测仍是难题。传统时间序列模型（如ARIMA、GARCH）和循环神经网络（如LSTM）难以捕捉数据中的状态转换和长程依赖。本文提出CryptoMamba，一种基于Mamba的状态空间模型（SSM）架构，能有效建模金融时序数据的长期依赖。实验表明，CryptoMamba不仅预测更准确，且在不同市场条件下的泛化能力更强，超越了以往模型的局限。结合交易算法应用于真实场景，其精准预测可转化为实际收益。研究结果表明，状态空间模型在股票与加密货币价格预测中具有显著优势。
原文摘要 · Abstract (English)
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.
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