比特币预测专家系统：通过时间Xer变换器整合全球流动性
Expert System for Bitcoin Forecasting: Integrating Global Liquidity via TimeXer Transformers
AI总结：
本文提出通过整合全球流动性提升比特币长期价格预测的专家系统，采用TimeXer架构实现条件化预测模型，实验表明其在长期内预测表现优于传统模型。
摘要：
比特币价格预测以极端波动性和非平稳性为特征，往往无法被传统单变量时间序列模型在长期内预测。本文通过将全球M2流动性（从18个主要经济体汇总而来）作为领先外生变量，以12周滞后结构整合进模型中，填补了这一关键空白。使用TimeXer架构，我们比较了条件化预测模型（TimeXer-Exog）与包括LSTM、N-BEATS、PatchTST和标准单变量TimeXer在内的最新基准模型。在2020年1月至2025年8日的每日比特币价格数据上进行的实验表明，显式的宏观经济条件化显著稳定了长期内预测。在70天预测范围内，所提出的TimeXer-Exog模型达到均方误差（MSE）1.08e8，比单变量TimeXer基线模型高出超过89%。这些结果表明，将深度学习模型条件化于全球流动性可显著提升比特币价格预测的长期内预测能力。
英文摘要：
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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