Rethinking Post-Training Recipes for Multimodal Time-Series Forecasting

• URL: http://arxiv.org/abs/2605.29401v1
• Date: Thu, 28 May 2026 05:52:28 GMT
• Title: Rethinking Post-Training Recipes for Multimodal Time-Series Forecasting
• Abstract summary: We argue for a multimodal time-series forecasting approach that post-trains LLMs to act as context-guided revisors over strong numerical TSFM priors.<n>We introduce PostTime, a post-training recipe combining Supervised Fine-Tuning (SFT) and Supervisement Learning with Verifiable Rewards (RLVR)<n>We evaluate this approach on the TimesX multimodal forecasting benchmark using a Gemma-3-4B LLM and TimesFM-2.5 TSFM.
• Score: 30.02814339453953
• License: http://creativecommons.org/licenses/by/4.0/
• 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 methodology to generate automated reasoning traces for forecast revisions. PostTime teaches an LLM to generate context-conditioned forecast interventions -- decisions to revise, preserve, or ignore the TSFM prior based on the multimodal context. We evaluate this approach on the TimesX multimodal forecasting benchmark using a Gemma-3-4B LLM and TimesFM-2.5 TSFM, and show that it significantly outperforms standalone TSFMs, LLM-only baselines, and existing multimodal forecasting approaches.

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