UniCast: A Unified Multimodal Prompting Framework for Time Series Forecasting

• URL: http://arxiv.org/abs/2508.11954v1
• Date: Sat, 16 Aug 2025 07:33:27 GMT
• Title: UniCast: A Unified Multimodal Prompting Framework for Time Series Forecasting
• Authors: Sehyuk Park, Soyeon Caren Han, Eduard Hovy,
• Abstract summary: Time series forecasting is a foundational task across domains, such as finance, healthcare, and environmental monitoring.<n>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.<n>This paper introduces a novel parameter-efficient multimodal framework, UniCast, that extends TSFMs to jointly leverage time series, vision, text modalities for enhanced forecasting performance.
• Score: 9.836278124939453
• License: http://creativecommons.org/licenses/by/4.0/
• 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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