部分多变量变换器作为加密货币时间序列预测工具
Partial multivariate transformer as a tool for cryptocurrencies time series prediction
AI总结：
本文提出部分多变量变换器用于加密货币时间序列预测，通过实验证明其在统计准确性上的优势，并指出预测误差与实际交易收益之间的矛盾。
摘要：
加密货币价格预测受到极端波动的阻碍，并存在一种方法学困境：信息稀缺的单变量模型与噪声多变量模型之间。本文研究了一种部分多变量方法，以平衡这种权衡，假设战略特征子集能提供更强大的预测能力。我们应用部分多变量变换器（PMformer）来预测BTCUSDT和ETHUSDT的每日回报，将其与十一类经典和深度学习模型进行比较。我们的实证结果得出两个主要贡献。首先，我们证明部分多变量策略实现了显著的统计准确性，有效平衡了信息信号与噪声。其次，我们实验并讨论了这种统计性能与实际交易实用性之间的可观察断层；较低的预测误差并不一致地转化为更高的财务回报。这一发现挑战了传统误差度量的依赖，并突显了开发更符合现实金融目标的评估标准的必要性。
英文摘要：
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.
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