DualCast：用于双模态金融时间序列预测的双路径语言模型

中文摘要 AI 辅助

金融时间序列预测必须在捕捉异质资产价格动态的同时，整合预测时可用的新闻信息。我们提出了DualCast，一个双路径框架，通过离散金融词汇表扩展冻结的语言模型。每个对数收益补丁由一个学习到的摘要令牌和三个残差形状令牌表示，在保留局部漂移和波动性的同时，允许形状模式在资产间共享。为提高码本利用率，我们开发了自适应频率均衡残差向量量化，在不影响重建精度的情况下重新平衡过载的码字。快速路径仅在冻结的Qwen3-8B骨干上训练新的金融令牌嵌入和输出头。可切换的LoRA适配器启用慢速路径，该路径以快速预测和预测原点可用的新闻为条件，生成修订预测。修订器通过监督微调初始化，并进一步使用返回空间组相对策略优化目标进行优化，该目标奖励对快速预测的改进。在涵盖五分钟、每日和每周分辨率的股票和能源价格的零样本评估中，慢速路径在12个数据集-时间跨度设置中的8个（包括每个最长时间跨度设置）实现了比较方法中最低的平均绝对百分比误差。新闻消融实验表明在大多数测试设置中有额外收益，尽管其幅度因市场而异。因此，DualCast结合了快速数值预测器与可选的文本条件修订机制。

英文摘要

Financial time-series forecasting must capture price dynamics across heterogeneous assets while incorporating news available at prediction time. We introduce DualCast, a dual-path framework that extends a frozen language model with a discrete financial vocabulary. Each log-return patch is represented by a learned summary token and three residual shape tokens, preserving local drift and volatility while allowing shape patterns to be shared across assets. To improve codebook utilization, we develop adaptive frequency-equalizing residual vector quantization, which rebalances overloaded codewords without compromising reconstruction accuracy. The fast path trains only the new financial-token embeddings and output heads on a frozen Qwen3-8B backbone. A toggleable LoRA adapter enables a slow path that conditions on the fast forecast and news available at the forecast origin to produce a revised prediction. The reviser is initialized by supervised fine-tuning and further optimized with a return-space group relative policy optimization objective that rewards improvements over the fast forecast. In zero-shot evaluations covering equities and energy prices at five-minute, daily, and weekly resolutions, the slow path achieves the lowest mean absolute percentage error among the compared methods in 8 of 12 dataset-horizon settings, including every longest-horizon setting. News ablations indicate additional gains in most tested settings, although their magnitude varies across markets. DualCast thus combines a fast numerical forecaster with an optional text-conditioned revision mechanism.

发表机构

• Tsinghua University（清华大学）
• Northwestern Polytechnical University（西北工业大学）
• Inspur Yunzhou Industrial Internet Co. Ltd（浪潮云舟工业互联网有限公司）

机构由 AI 辅助整理，请以论文原文为准。