arXiv:2605.29401v1 Announce Type: new Abstract: Time-Series Foundation Models (TSFMs) excel at zero-shot unimodal forecasting using numerical data, but unlike LLMs they cannot consume multimodal, non-numerical context that often shape real-world trajectories. In this work, we bridge this gap and argue for a multimodal time-series forecasting approach that post-trains LLMs to act as context-guided revisors over strong numerical TSFM priors. We introduce PostTime, a post-training recipe combining Supervised Fine-Tuning (SFT) and Reinforcement Learning with Verifiable Rewards (RLVR), along with a
The proliferation of advanced LLMs and the recognition of their contextual reasoning capabilities are pushing researchers to integrate them into specialized forecasting domains like time-series analysis.
This development allows time-series forecasting models to leverage rich, non-numerical contextual data, moving beyond purely quantitative inputs and enabling more nuanced and accurate predictions for complex real-world events.
Traditional time-series models, previously limited to numerical data, can now be augmented by LLMs to incorporate multimodal context, significantly broadening their applicability and predictive power.
- · AI researchers
- · Data science platforms
- · Industries reliant on complex forecasting (e.g., finance, logistics)
- · LLM developers
- · Purely numerical time-series model providers
- · Less adaptable forecasting tools
Increased accuracy and robustness in multimodal time-series forecasting across various applications.
New market opportunities for companies specializing in integrating LLMs with traditional analytical models.
Enhanced automation of decision-making processes in complex operational environments due to more informed predictions.
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Read at arXiv cs.LG