LLM as Forecasting Planner: Training-Free Text Conditioning for Time-Series Foundation Models

arXiv:2607.24892v1 Announce Type: new Abstract: Text-conditioned time-series forecasting predicts a series from both its numerical history and natural-language context, allowing forecasts to account for events and constraints that the past alone cannot reveal. This requires both reliable numerical forecasting and the ability to interpret contextual information. Time-series foundation models (TSFMs) provide strong numerical forecasts, while large language models (LLMs) can reason over text, but c...