KiT：基于扩散Transformer的金融时间序列预测基础模型

中文摘要 AI 辅助

金融K线预测是量化投资的基础，但由于极低的信噪比以及市场和工具之间的巨大异质性，这一任务仍然极具挑战性。现有方法大多尝试引入深度学习来捕捉隐藏的时间特征，但多数采用自回归形式，导致推理过程中误差累积。同时，通用时间序列基础模型并不针对K线数据的独特结构进行定制，在下游K线预测任务上表现不佳。为解决这些问题，我们提出了KiT，一种K线扩散Transformer基础模型，并通过流匹配将未来预测重构为条件路径生成：给定历史上下文窗口，模型生成一组合理的未来OHLCV轨迹集合。我们在涵盖多个市场和时间尺度的数十亿根K线上，以多种参数规模对KiT进行预训练。在三个市场和七个分辨率上，KiT实现了平均收益RankIC为0.057，平均波动率RankIC为0.66，在每个时间尺度上均领先，并优于特定任务的金融预测器和通用时间序列基础模型。代码将在以下网址提供：此https URL。

英文摘要

Financial candlestick forecasting is fundamental to quantitative investment, yet it remains exceptionally challenging due to extremely low signal-to-noise ratios and vast heterogeneity across markets and instruments. Existing approaches have largely attempted to introduce deep learning to capture hidden temporal features, but most adopt an auto-regressive formulation, which leads to error accumulation during inference. Meanwhile, general-purpose time-series foundation models are not tailored to the unique structure of k-line data and yield unsatisfactory performance on downstream candlestick forecasting tasks. To tackle these problems, we introduce KiT, a K-line Diffusion Transformer foundation model, and reformulate future prediction as conditional path generation via flow matching: given a historical context window, the model generates an ensemble of plausible future OHLCV trajectories. We pre-train KiT at multiple parameter scales on billions of candlestick bars spanning multiple markets and timescales. Across three markets and seven resolutions, KiT attains a mean return RankIC of 0.057 and a mean volatility RankIC of 0.66, leading at every timescale and outperforming both task-specific financial forecasters and general time-series foundation models. Code will be available at: https://github.com/Luciferbobo/KiT.

发表机构

• University of California, Los Angeles（加州大学洛杉矶分校）
• Southeast University（东南大学）

机构由 AI 辅助整理，请以论文原文为准。