Forecasting cryptocurrency prices is hindered by extreme volatility and a methodological dilemma between information-scarce univariate models and noise-prone full-multivariate models. This paper investigates a partial-multivariate approach to balance this trade-off, hypothesizing that a strategic subset of features offers superior predictive power. We apply the Partial-Multivariate Transformer (PMformer) to forecast daily returns for BTCUSDT and ETHUSDT, benchmarking it against eleven classical and deep learning models. Our empirical results yield two primary contributions. First, we demonstrate that the partial-multivariate strategy achieves significant statistical accuracy, effectively balancing informative signals with noise. Second, we experiment and discuss an observable disconnect between this statistical performance and practical trading utility; lower prediction error did not consistently translate to higher financial returns in simulations. This finding challenges the reliance on traditional error metrics and highlights the need to develop evaluation criteria more aligned with real-world financial objectives.

翻译：加密货币价格预测受极端波动性和方法论困境所阻碍：信息稀缺的单变量模型与噪声充斥的完全多元模型之间存在矛盾。本文研究了一种部分多元方法以平衡这一权衡，假设战略性特征子集能提供更优的预测能力。我们应用部分多元Transformer（PMformer）预测BTCUSDT和ETHUSDT的日收益率，并以十一种经典模型和深度学习模型作为基准进行对比。实证结果产生两项主要贡献：首先，我们证明部分多元策略在统计精度上表现显著，能有效平衡信息信号与噪声；其次，我们通过实验发现并讨论了统计性能与实际交易效用之间的明显脱节——在模拟交易中，较低的预测误差并未一致转化为更高的财务收益。这一发现对传统误差指标的依赖性提出质疑，并强调需要建立更符合现实金融目标的评估标准。