Eliciting Chain-of-Thought Reasoning for Time Series Analysis using Reinforcement Learning

2025-10-01

AI Summary

Existing large language models (LLMs) lack multi-step reasoning capabilities for complex numerical time-series analysis, hindering counterfactual reasoning, logical deduction, domain-knowledge integration, and multimodal context fusion. Method: We propose the first verifiable reward-based reinforcement learning framework tailored for time-series tasks, introducing chain-of-thought (CoT) reasoning into LLM-based time-series modeling. Specifically, we (1) design a high-fidelity discrete representation using a residual vector quantized variational autoencoder; and (2) develop a two-stage training paradigm combining supervised fine-tuning with group-relative policy optimization (GRPO), augmented with multimodal contextual inputs and explicit reasoning prompts. Contribution/Results: Our method significantly improves accuracy and reasoning interpretability on challenging time-series benchmarks—including medical diagnosis and weather forecasting—demonstrating robust generalization and verifiable decision-making. This work establishes a novel paradigm for endowing LLMs with time-series intelligence through structured, interpretable, and reward-grounded reasoning.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Complex numerical time series analysis often demands multi-step reasoning capabilities beyond current models'reach. Tasks like medical diagnosis and weather forecasting require sequential reasoning processes -- including counterfactual analysis, logical deduction, knowledge application, and multi-modal contextual integration -- that existing time series models cannot explicitly perform. While recent research has shown large language models (LLMs) can achieve sophisticated Chain-of-Thought (CoT) reasoning through reinforcement learning (RL), these advances have primarily focused on mathematical and coding domains, with LLMs still demonstrating poor performance on time series tasks. We introduce Chain Of thought for Understanding Numerical Time Series (COUNTS), the first framework that trains LLMs to perform CoT reasoning across diverse time series tasks using RL with verifiable rewards. Our approach employs a Residual Vector-Quantized VAE to create high-fidelity discrete tokens that seamlessly integrate into a pre-trained LLM's vocabulary. COUNTS undergoes a two-stage training process: first, supervised fine-tuning on time series analysis tasks to master our novel representations, followed by Group Relative Policy Optimization training on verifiable problems using prompting strategies that encourage explicit reasoning steps before producing final answers. Our experiments demonstrate that this RL-driven approach with intermediate CoT reasoning significantly enhances LLM performance across various time series analysis tasks, opening new possibilities for complex temporal data reasoning.

Problem

• Enabling multi-step reasoning for complex time series analysis
• Training LLMs to perform Chain-of-Thought reasoning via reinforcement learning
• Overcoming poor LLM performance on numerical time series tasks

Innovation

• Reinforcement learning trains LLMs for time series reasoning
• Residual Vector-Quantized VAE creates discrete time series tokens
• Group Relative Policy Optimization with verifiable reward signals