用精选特征子集提升加密货币价格预测精度
Partial multivariate transformer as a tool for cryptocurrencies time series prediction
- 选取关键特征构建部分多变量模型，平衡信息与噪声
- 在比特币和以太坊数据上显著降低预测误差
- 揭示模型准确率与实际交易收益不一致的矛盾
加密货币价格预测受极端波动影响，传统单变量模型信息不足，全多变量模型又易受噪声干扰。本文提出部分多变量变压器（PMformer）方法，通过有选择地使用特征子集来平衡这一矛盾。在BTCUSDT和ETHUSDT的日收益率预测中，对比11种经典与深度学习模型，结果表明该策略显著提升了统计准确性，有效捕捉了有用信号并抑制噪声。同时发现，尽管预测误差降低，但未稳定带来更高交易回报，揭示了传统误差指标与实际金融目标间的脱节。这一发现提示需发展更贴近真实投资目标的评估体系。
原文摘要 · Abstract (English)
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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