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Life sciences · Preprint
arXiv · August 17, 2026
Posted before peer review. The findings may change or fail to hold.
SCENARIODIFF is a proposed hierarchical machine learning framework that incorporates textual context (news, reports, logs) into multimodal time series forecasting via structured scenario guidance and diffusion-based generation. The work is presented as a preprint on arXiv and has not been peer reviewed; it reports improved performance on a proprietary benchmark in event-driven domains but provides no numerical results, confidence intervals, or statistical comparisons in the abstract.
Preprint.
SCENARIODIFF demonstrates effectiveness in event-driven domains using hierarchical scenario guidance for multimodal time series forecasting Explicit three-level contextual reasoning (Historical, Scenario, and Anchor Guidance Agents) conditions a Multimodal Diffusion Transformer
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This is an unrefereed arXiv preprint presenting a machine learning framework for time series forecasting; it has not undergone peer review and describes a computational method rather than a clinical or biological study.
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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
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