MM-ISTS: Cooperating Irregularly Sampled Time Series Forecasting with Multimodal Vision-Text LLMs

ArXi:2603.05997v1 Announce Type: cross Irregularly sampled time series (ISTS) are widespread in real-world scenarios, exhibiting asynchronous observations on uneven time intervals across variables. Existing ISTS forecasting methods often solely utilize historical observations to predict future ones while falling short in learning contextual semantics and fine-grained temporal patterns. To address these problems, we achieve MM-ISTS, a multimodal framework augmented by vision-text large language models, that bridges temporal, visual, and textual modalities, facilitating ISTS forecasting.