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.

翻译：比特币价格预测具有极端波动性和非平稳性，传统单变量时间序列模型在长期预测中往往失效。本文通过整合来自18个主要经济体的全球M2流动性作为领先外生变量（采用12周滞后结构），填补了这一关键空白。利用TimeXer架构，我们将流动性条件预测模型（TimeXer-Exog）与包括LSTM、N-BEATS、PatchTST及标准单变量TimeXer在内的先进基准模型进行比较。基于2020年1月至2025年8月的比特币日价格数据进行的实验表明，显式的宏观经济条件设定能显著提升长期预测的稳定性。在70天预测范围内，所提出的TimeXer-Exog模型实现了1.08e8的均方误差（MSE），较单变量TimeXer基线模型性能提升超过89%。这些结果凸显了将深度学习模型与全球流动性条件相结合，可显著改善比特币价格的长期预测能力。