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

📖 Abstract

Time-R1 introduces the study of slow-thinking reasoning for time series forecasting. We propose a two-stage reinforcement fine-tuning framework combining supervised warmup and policy optimization with GRIP, a group-based sampling strategy for multi-step reasoning. Our model significantly improves forecasting accuracy across diverse datasets, demonstrating the effectiveness of training LLMs for structured temporal reasoning.

This repository contains the official code for the paper:

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

🌟 Overview

Overview of the Time-R1.

Large Language Models (LLMs) demonstrate impressive capabilities but often lack time series reasoning for forecasting tasks. Ours addresses this by introducing a novel two-stage reinforcement fine-tuning (RFT) curriculum, guided by a custom-designed multi-objective reward framework that explicitly shapes temporal reasoning. Our approach progressively develops:

1. (Stage 1: SFT for Warmup Adaption) Foundational skills through supervised fine-tuning, where LLMs learn temporal analysis using synthetic CoT data, ensuring proper structure and formatting.
2. (Stage 2: RL for Exploring Effective Reasoning Patterns) Advanced forecasting via RL, with rewards based on ground truth alignment, multi-horizon accuracy, and domain principles. GRIP (Group-based Relative Importance for Policy Optimization) enhances reasoning paths through non-uniform sampling and adaptive weighting.

GRIP Optimization Framework.

Experiments show that Time-R1 significantly improves forecasting accuracy and generalization across multiple real-world datasets.

📚 Resources

• Training Dataset: Scripts and configurations for preparing training and evaluating datasets are provided.
• Time-R1 Model: The codebase supports the training and inference of the Time-R1 model after two-stage RFT.
• Source Code: Complete source code for training and evaluation.

⚙️ Key Features

• Slow-Thinking Time Series Reasoning: Trains LLMs to perform deliberate, step-by-step temporal analysis for forecasting tasks.
• Two-Stage RFT Framework: Combines warm-up supervised fine-tuning (SFT) with reinforcement learning (RL) for progressive capability building.
• GRIP: Group-based Reward Optimization: Introduces non-uniform sampling and adaptive weighting to enhance reasoning path exploration and model robustness.
• Fine-Grained Multi-Objective Rewards: Designed to improve temporal coherence, multi-horizon accuracy, and alignment with domain-specific forecasting principles.
• Strong Forecasting Performance: Extensive experiments on real-world datasets demonstrate significant improvements over baseline methods through the slow-thinking paradigm.

🚀 Quick Start

Installation

We recommend using Python 3.10+ and setting up a clean environment via conda, with system pre-requisites including CUDA ≥ 12.4 and cuDNN ≥ 9.8.0 before training or inference.

• CUDA: Version ≥ 12.4
• cuDNN: Version ≥ 9.8.0

conda create -n time-r1 python==3.10
conda activate time-r1

# Install verl framework
cd Time-R1
pip install --no-deps -e .
pip install -r requirements.txt

🛠️ Training

Time-R1 RL Training

# Run training
bash scripts/time-r1.sh

📈 Evaluation

cd Time-R1/eval
python main.py