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UniCast: A Unified Multimodal Prompting Framework for Time Series Forecasting
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本文提出一种名为UniCast的新型多模态时间序列预测框架,通过联合利用时间序列、视觉和文本模态,显著提升预测性能。实验证明,该框架在多个时间序列预测基准上优于现有模型,突显多模态上下文在提升通用时间序列预测器中的重要性。

arXiv:2508.11954v1 Announce Type: new Abstract: Time series forecasting is a foundational task across domains, such as finance, healthcare, and environmental monitoring. While recent advances in Time Series Foundation Models (TSFMs) have demonstrated strong generalisation through large-scale pretraining, existing models operate predominantly in a unimodal setting, ignoring the rich multimodal context, such as visual and textual signals, that often accompanies time series data in real-world scenarios. This paper introduces a novel parameter-efficient multimodal framework, UniCast, that extends TSFMs to jointly leverage time series, vision, and text modalities for enhanced forecasting performance. Our method integrates modality-specific embeddings from pretrained Vision and Text Encoders with a frozen TSFM via soft prompt tuning, enabling efficient adaptation with minimal parameter updates. This design not only preserves the generalisation strength of the foundation model but also enables effective cross-modal interaction. Extensive experiments across diverse time-series forecasting benchmarks demonstrate that UniCast consistently and significantly outperforms all existing TSFM baselines. The findings highlight the critical role of multimodal context in advancing the next generation of general-purpose time series forecasters.

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时间序列预测 多模态框架 UniCast 预测性能 模态交互
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