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

2026-07-27

AI Summary

This work proposes a training-free framework that formulates time series forecasting as a planning problem, synergistically integrating the textual reasoning capabilities of large language models (LLMs) with the numerical prediction power of frozen time series foundation models (TSFMs), such as Chronos or TimesFM. The approach employs a TSFM as a trajectory simulator to generate candidate forecasts, while two role-specialized LLMs act as a policy (Ranker) and a value function (Judge), respectively. Guided by Monte Carlo Tree Search (MCTS), the method selects the optimal forecast trajectory under natural language conditions while preserving temporal structure. Experiments on the Context-is-Key and Time-MMD benchmarks demonstrate consistent and significant performance gains across diverse TSFM–LLM pairings, establishing the first training-free, cross-modal framework for text-conditioned time series forecasting.

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 combining their strengths remains challenging because asking an LLM to generate or revise forecast values directly can distort the temporal structure captured by the TSFM. We instead formulate forecasting as a planning problem over TSFM-generated trajectories. The frozen TSFM acts as a simulator that proposes numerical continuations, while the LLM acts as a policy and value function that guides candidate selection and evaluates completed trajectories against the context. We instantiate this as \rc{} (\textbf{L}LM \textbf{A}s \textbf{F}orecasting \textbf{P}lanner), a training-free framework that bridges the modality gap without retraining either model, using Monte Carlo tree search (MCTS) over the forecast horizon with a \emph{Ranker} LLM as policy and a \emph{Judge} LLM as value function. Experiments on Context-is-Key and Time-MMD across two TSFM backbones (Chronos and TimesFM) and four LLMs show that \rc{} delivers consistent improvements across model choices, supporting sequential search as an effective training-free approach to text-conditioned forecasting.

Problem

• text-conditioned forecasting
• time-series foundation models
• large language models
• modality gap
• training-free integration

Innovation

• training-free
• text-conditioned forecasting
• time-series foundation models
• LLM as planner
• Monte Carlo tree search