SCENARIODIFF：面向多模态时间序列预测的场景级引导框架——扩展版
SCENARIODIFF: A Scenario-level Guidance Framework for Multimodal Time Series Forecasting--Extended Version
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中文总结 AI 辅助
该研究针对现有多模态时间序列预测方法上下文影响难解释控制的问题，提出SCENARIODIFF分层框架，通过三类智能体生成结构化信号引导多模态扩散Transformer，锚点混合采样优化轨迹，在Time-MMD基准的事件驱动领域表现优异。
中文摘要 AI 辅助
新闻、报告和日志等文本上下文可为时间序列预测提供有价值的信号，尤其是当未来动态由历史值中尚未显现的外部事件驱动时。现有多模态预测方法常要么要求大语言模型（LLMs）直接预测数值，要么隐式融合文本与时间序列，导致上下文影响难以解释和控制。我们提出SCENARIODIFF，这是一种针对含噪声、弱对齐文档的多模态时间序列预测的分层上下文推理框架。SCENARIODIFF将上下文信息组织为三个层级：历史上下文智能体（Historical Context Agent）从原始文档中提取逐步证据；场景智能体（Scenario Agent）生成预测 horizon 的定性场景描述；锚点引导智能体（Anchor Guidance Agent）为与事件相关的未来区域生成稀疏锚点。这些结构化信号对多模态扩散 Transformer（Multimodal Diffusion Transformer）进行条件约束，而锚点混合采样（Anchor Blended Sampling）无需重新训练即可局部优化生成的轨迹。在Time-MMD基准上的实验表明，SCENARIODIFF在事件驱动领域尤其有效，证明了显式分层场景引导对多模态时间序列预测的价值。我们的完整实现可在该https URL获取。
英文摘要
Textual context such as news, reports, and logs can provide valuable signals for time series forecasting, especially when future dynamics are driven by external events that are not yet visible in historical values. Existing multimodal forecasting methods often either ask large language models (LLMs) to predict numerical values directly or fuse text and time series implicitly, making contextual influence difficult to interpret and control. We propose SCENARIODIFF, a hierarchical contextual reasoning framework for multimodal time series forecasting under noisy and weakly aligned documents. SCENARIODIFF organizes contextual information into three levels: a Historical Context Agent extracts stepwise evidence from raw documents, a Scenario Agent produces a qualitative scenario description for the forecast horizon, and an Anchor Guidance Agent generates sparse anchor points for event-relevant future regions. These structured signals condition a Multimodal Diffusion Transformer, while Anchor Blended Sampling locally refines generated trajectories without retraining. Experiments on the Time-MMD benchmark show that SCENARIODIFF is especially effective in event-driven domains, demonstrating the value of explicit hierarchical scenario guidance for multimodal time series forecasting. Our full implementation is available at https://anonymous.4open.science/r/ScenarioDiff_ICDM-2C4C
发表机构
- VinUniversity
- FPT Software AI Center, FPT Corporation（FPT软件AI中心，FPT集团）
- VNU University of Engineering and Technology（VNU工程技术大学）
- Aalborg University（奥尔堡大学）
机构由 AI 辅助整理，请以论文原文为准。
