用时间戳和文本引导扩散模型，提升时序预测精度。
Multimodal Conditioned Diffusive Time Series Forecasting
- 引入时间戳与文本作为额外条件，指导时序建模
- 在8个真实数据集上达到当前最优性能
- 适合需要多源信息融合的时序预测任务
扩散模型在图像和文本处理中取得显著成功，并已扩展至时序预测（TSF）等特定领域。现有基于扩散的时序预测方法主要聚焦于单一数值序列建模，忽略了时序数据中丰富的多模态信息。为有效利用这些信息进行预测，我们提出一种多模态条件扩散模型用于时序预测（MCD-TSF），联合使用时间戳和文本作为额外引导，尤其用于预测阶段。具体而言，时间戳与时序数据结合，在时间维度聚合信息时建立不同数据点间的时空与语义关联；文本作为时序历史的补充描述，自适应地对齐数据点，并以无分类器方式动态控制。在涵盖八个领域的多个真实世界基准数据集上的大量实验表明，所提出的MCD-TSF模型实现了领先性能。
原文摘要 · Abstract (English)
Diffusion models achieve remarkable success in processing images and text, and have been extended to special domains such as time series forecasting (TSF). Existing diffusion-based approaches for TSF primarily focus on modeling single-modality numerical sequences, overlooking the rich multimodal information in time series data. To effectively leverage such information for prediction, we propose a multimodal conditioned diffusion model for TSF, namely, MCD-TSF, to jointly utilize timestamps and texts as extra guidance for time series modeling, especially for forecasting. Specifically, Timestamps are combined with time series to establish temporal and semantic correlations among different data points when aggregating information along the temporal dimension. Texts serve as supplementary descriptions of time series' history, and adaptively aligned with data points as well as dynamically controlled in a classifier-free manner. Extensive experiments on real-world benchmark datasets across eight domains demonstrate that the proposed MCD-TSF model achieves state-of-the-art performance.
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