用全球流动性数据提升比特币长期价格预测准确率
Expert System for Bitcoin Forecasting: Integrating Global Liquidity via TimeXer Transformers
- 引入18国货币供应量数据，滞后12周作为外部变量
- 70天预测误差比纯时间序列模型降低89%以上
- 适合量化交易与宏观对冲策略研究者参考
比特币价格预测受极端波动性和非平稳性影响，传统单变量时间序列模型在长周期下表现不佳。本文通过整合18个主要经济体的全球M2流动性数据（滞后12周），作为领先外生变量，构建时间序列预测模型。基于TimeXer架构，对比LSTM、N-BEATS、PatchTST及标准单变量TimeXer模型，在2020年1月至2025年8月的每日比特币价格数据上进行实验。结果表明，引入宏观经济条件可显著稳定长期预测。在70天预测周期下，所提模型TimeXer-Exog的均方误差（MSE）为1.08e8，较单变量TimeXer基线降低超过89%。研究证实，将深度学习模型与全球流动性条件结合，能有效提升比特币长期价格预测性能。
原文摘要 · Abstract (English)
Bitcoin price forecasting is characterized by extreme volatility and non-stationarity, often defying traditional univariate time-series models over long horizons. This paper addresses a critical gap by integrating Global M2 Liquidity, aggregated from 18 major economies, as a leading exogenous variable with a 12-week lag structure. Using the TimeXer architecture, we compare a liquidity-conditioned forecasting model (TimeXer-Exog) against state-of-the-art benchmarks including LSTM, N-BEATS, PatchTST, and a standard univariate TimeXer. Experiments conducted on daily Bitcoin price data from January 2020 to August 2025 demonstrate that explicit macroeconomic conditioning significantly stabilizes long-horizon forecasts. At a 70-day forecast horizon, the proposed TimeXer-Exog model achieves a mean squared error (MSE) 1.08e8, outperforming the univariate TimeXer baseline by over 89 percent. These results highlight that conditioning deep learning models on global liquidity provides substantial improvements in long-horizon Bitcoin price forecasting.
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