首个专用于金融时序预测的通用模型，无需微调即可跨领域准确预测。
FinCast: A Foundation Model for Financial Time-Series Forecasting
- 构建首个金融时序基础模型，统一处理多领域、多周期数据
- 零样本下在股票、商品等多类资产上表现超越现有方法
- 适合需要快速部署、少标注的金融预测场景
金融时序预测对经济稳定、政策制定和可持续投资至关重要，但受时间非平稳性（随时间分布变化）、多领域差异（如股票、商品、期货模式不同）及多时间粒度（秒级、小时、日、周）影响，仍具挑战。现有深度学习方法常因过拟合且需大量领域微调。为此，我们提出FinCast，首个专为金融时序预测设计的基础模型，基于大规模金融数据训练。其展现出强大零样本性能，无需领域微调即可有效捕捉多样化模式。全面实证与定性评估表明，FinCast优于现有最先进方法，凸显其卓越泛化能力。
原文摘要 · Abstract (English)
Financial time-series forecasting is critical for maintaining economic stability, guiding informed policymaking, and promoting sustainable investment practices. However, it remains challenging due to various underlying pattern shifts. These shifts arise primarily from three sources: temporal non-stationarity (distribution changes over time), multi-domain diversity (distinct patterns across financial domains such as stocks, commodities, and futures), and varying temporal resolutions (patterns differing across per-second, hourly, daily, or weekly indicators). While recent deep learning methods attempt to address these complexities, they frequently suffer from overfitting and typically require extensive domain-specific fine-tuning. To overcome these limitations, we introduce FinCast, the first foundation model specifically designed for financial time-series forecasting, trained on large-scale financial datasets. Remarkably, FinCast exhibits robust zero-shot performance, effectively capturing diverse patterns without domain-specific fine-tuning. Comprehensive empirical and qualitative evaluations demonstrate that FinCast surpasses existing state-of-the-art methods, highlighting its strong generalization capabilities.
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