Context-Aware Probabilistic Modeling with LLM for Multimodal Time Series Forecasting

• URL: http://arxiv.org/abs/2505.10774v2
• Date: Tue, 29 Jul 2025 14:43:48 GMT
• Title: Context-Aware Probabilistic Modeling with LLM for Multimodal Time Series Forecasting
• Authors: Yueyang Yao, Jiajun Li, Xingyuan Dai, MengMeng Zhang, Xiaoyan Gong, Fei-Yue Wang, Yisheng Lv,
• Abstract summary: We propose CAPTime, a context-aware probabilistic multimodal time series forecasting method.<n>Our method first encodes temporal patterns using a pretrained time series encoder, then aligns them with textual contexts via learnable interactions.<n> Experiments on diverse time series forecasting tasks demonstrate the superior accuracy and generalization of CAPTime.
• Score: 24.56167831047955
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time series forecasting is important for applications spanning energy markets, climate analysis, and traffic management. However, existing methods struggle to effectively integrate exogenous texts and align them with the probabilistic nature of large language models (LLMs). Current approaches either employ shallow text-time series fusion via basic prompts or rely on deterministic numerical decoding that conflict with LLMs' token-generation paradigm, which limits contextual awareness and distribution modeling. To address these limitations, we propose CAPTime, a context-aware probabilistic multimodal time series forecasting method that leverages text-informed abstraction and autoregressive LLM decoding. Our method first encodes temporal patterns using a pretrained time series encoder, then aligns them with textual contexts via learnable interactions to produce joint multimodal representations. By combining a mixture of distribution experts with frozen LLMs, we enable context-aware probabilistic forecasting while preserving LLMs' inherent distribution modeling capabilities. Experiments on diverse time series forecasting tasks demonstrate the superior accuracy and generalization of CAPTime, particularly in multimodal scenarios. Additional analysis highlights its robustness in data-scarce scenarios through hybrid probabilistic decoding.

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