Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs

2025-06-12

AI Summary

Most existing time-series forecasting methods rely on “fast thinking”—i.e., end-to-end black-box prediction—lacking explicit, interpretable intermediate reasoning steps and thus hindering domain-specific fine-grained modeling. To address this, we propose a “slow thinking” paradigm that reformulates forecasting as a multi-step temporal reasoning task and introduce the first large language model (LLM) endowed with explicit, stepwise reasoning capabilities. Our contributions are threefold: (1) Time-R1, a novel two-stage reinforcement fine-tuning framework; (2) a fine-grained multi-objective reward function jointly optimizing prediction accuracy, reasoning consistency, and temporal plausibility; and (3) GRIP, a non-uniform path sampling strategy, coupled with chain-of-thought distillation to enhance reasoning-path exploration efficiency. Extensive experiments on multiple benchmark datasets demonstrate significant improvements over state-of-the-art methods, validating the superiority of our slow-thinking paradigm in accuracy, interpretability, and generalization.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

To advance time series forecasting (TSF), various methods have been proposed to improve prediction accuracy, evolving from statistical techniques to data-driven deep learning architectures. Despite their effectiveness, most existing methods still adhere to a fast thinking paradigm-relying on extracting historical patterns and mapping them to future values as their core modeling philosophy, lacking an explicit thinking process that incorporates intermediate time series reasoning. Meanwhile, emerging slow-thinking LLMs (e.g., OpenAI-o1) have shown remarkable multi-step reasoning capabilities, offering an alternative way to overcome these issues. However, prompt engineering alone presents several limitations - including high computational cost, privacy risks, and limited capacity for in-depth domain-specific time series reasoning. To address these limitations, a more promising approach is to train LLMs to develop slow thinking capabilities and acquire strong time series reasoning skills. For this purpose, we propose Time-R1, a two-stage reinforcement fine-tuning framework designed to enhance multi-step reasoning ability of LLMs for time series forecasting. Specifically, the first stage conducts supervised fine-tuning for warmup adaptation, while the second stage employs reinforcement learning to improve the model's generalization ability. Particularly, we design a fine-grained multi-objective reward specifically for time series forecasting, and then introduce GRIP (group-based relative importance for policy optimization), which leverages non-uniform sampling to further encourage and optimize the model's exploration of effective reasoning paths. Experiments demonstrate that Time-R1 significantly improves forecast performance across diverse datasets.

Problem

• Enhance time series forecasting via slow-thinking LLM reasoning
• Overcome limitations of fast-thinking pattern extraction methods
• Address computational cost and privacy risks in prompt engineering

Innovation

• Two-stage reinforcement fine-tuning for LLMs
• Multi-objective reward for time series forecasting
• GRIP optimizes reasoning path exploration