将大模型与状态空间模型结合，实现文本+时间序列的联合预测与不确定性量化。
LLM-Integrated Bayesian State Space Models for Multimodal Time-Series Forecasting
- 用状态空间模型捕捉隐变量动态，融合数值与文本输入
- 在TextTimeCorpus上比前代方法提升13.20%，支持灵活时序窗口
- 可生成可读性预测摘要，适合需解释性的多模态时序任务
真实世界预测需融合结构化时间序列与非结构化文本信息，但现有方法受限于固定输入/输出窗口，且无法量化不确定性。本文提出LLM集成贝叶斯状态空间模型（LBS），一种新型概率框架用于多模态时序预测。整体由两部分构成：(1) 状态空间模型（SSM）主干，从隐状态中生成数值与文本观测；(2) 预训练大语言模型（LLM），经适配后编码文本输入以估计后验状态，并解码与隐轨迹一致的文本预测。该设计支持灵活回溯与预测窗口、提供严谨不确定性量化，并因SSM对动态系统的天然归纳偏置而提升泛化能力。在TextTimeCorpus基准测试中，LBS较前代最优方法提升13.20%，并生成人类可读的预测摘要。本工作首次统一LLM与SSM实现数值与文本联合预测，为多模态时序推理提供新范式。
原文摘要 · Abstract (English)
Forecasting in the real world requires integrating structured time-series data with unstructured textual information, but existing methods are architecturally limited by fixed input/output horizons and are unable to model or quantify uncertainty. We address this challenge by introducing LLM-integrated Bayesian State space models (LBS), a novel probabilistic framework for multimodal temporal forecasting. At a high level, LBS consists of two components: (1) a state space model (SSM) backbone that captures the temporal dynamics of latent states from which both numerical and textual observations are generated and (2) a pretrained large language model (LLM) that is adapted to encode textual inputs for posterior state estimation and decode textual forecasts consistent with the latent trajectory. This design enables flexible lookback and forecast windows, principled uncertainty quantification, and improved temporal generalization thanks to the well-suited inductive bias of SSMs toward modeling dynamical systems. Experiments on the TextTimeCorpus benchmark demonstrate that LBS improves the previous state-of-the-art by 13.20% while providing human-readable summaries of each forecast. Our work is the first to unify LLMs and SSMs for joint numerical and textual prediction, offering a novel foundation for multimodal temporal reasoning.
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