用基础模型提升金融多变量预测，效果优于单变量
Multivariate Financial Forecasting using the Chronos Time Series Foundation Models
- 用时间序列基础模型对比多变量与单变量输入
- 多变量输入在利率和股票上均显著降低误差
- 跨市场混合数据会引入噪声，反而降低精度
使用开源时间序列基础模型Chronos-2，评估预训练模型在经济与金融预测中的表现，重点比较多变量（MV）输入相对于单变量（UV）基线的准确性。研究涵盖七大科技股、美国国债利率及合并数据集，采用2000—2025年滚动月度评估，调整输入窗口与预测时长，报告均方根误差（RMSE）与平均绝对百分比误差（MAPE）。在所有数据集中，多变量预测始终优于单变量，尤其在利率预测中提升明显，股票预测也有显著改善。逐系列对比显示，每项指标均实现多变量优势，且误差分布更集中。同时提供参数热图与时间序列可视化。然而，将股票与利率时间序列混合建模会降低预测精度，表明引入无关上下文会削弱性能。总体表明，基础模型能有效利用跨序列信息提升金融预测，但需在相关序列联合建模并遵循严格滚动协议下效果最佳。本研究还展示了人工智能在金融研究中的应用潜力。
原文摘要 · Abstract (English)
Using Chronos-2, an open-source time-series foundation model, we evaluate pretrained time-series models for economic and financial forecasting with an emphasis on whether multivariate (MV) inputs improve accuracy relative to univariate (UV) baselines. The study covers two panels -- the Magnificent-7 equities and U.S. Treasury interest rates -- as well as a combined panel, using rolling monthly evaluations from 2000--2025. We vary input window lengths and forecast horizons and report RMSE and MAPE. Across datasets, MV forecasts consistently outperform UV forecasts, with especially strong gains for interest rates and meaningful improvements for equities. Series-level comparisons show MV improvements in every case, and error dispersion is generally lower under MV inputs. We also provide parameter-heatmap and time-series visualizations. However, mixing time series across equity and interest rate markets reduces forecast accuracy, indicating that adding noisy context degrades model performance. Overall, the results indicate that foundation models can leverage cross-series information to improve forecast accuracy in finance, and that the benefits are strongest when related series are modeled jointly under disciplined rolling protocols. Other than using an open-source foundation model, this paper also showcases how AI may be used for financial research.
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