LangTime: A Language-Guided Unified Model for Time Series Forecasting with Proximal Policy Optimization

2025-03-11

AI Summary

This paper addresses three key challenges in applying large language models (LLMs) to time-series forecasting: weak cross-domain generalization, difficulty in cross-modal alignment, and severe error accumulation in autoregressive prediction. To tackle these, we propose a language-guided unified modeling framework. Our core contributions are: (1) Temporal Comprehension Prompting (TCP), enabling single-token temporal compression and domain-adaptive representation learning; and (2) TimePPO, a reinforcement learning fine-tuning algorithm that explicitly mitigates error propagation via a multi-dimensional reward function and repeated value estimation. The method integrates LLMs, temporal prompt engineering, Proximal Policy Optimization (PPO), cross-modal alignment, and cross-domain pretraining. Evaluated on multiple cross-domain and cross-modal benchmarks, our approach achieves state-of-the-art performance, significantly improving both stability and accuracy of autoregressive forecasting—reducing error accumulation by 37%.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Recent research has shown an increasing interest in utilizing pre-trained large language models (LLMs) for a variety of time series applications. However, there are three main challenges when using LLMs as foundational models for time series forecasting: (1) Cross-domain generalization. (2) Cross-modality alignment. (3) Error accumulation in autoregressive frameworks. To address these challenges, we proposed LangTime, a language-guided unified model for time series forecasting that incorporates cross-domain pre-training with reinforcement learning-based fine-tuning. Specifically, LangTime constructs Temporal Comprehension Prompts (TCPs), which include dataset-wise and channel-wise instructions, to facilitate domain adaptation and condense time series into a single token, enabling LLMs to understand better and align temporal data. To improve autoregressive forecasting, we introduce TimePPO, a reinforcement learning-based fine-tuning algorithm. TimePPO mitigates error accumulation by leveraging a multidimensional rewards function tailored for time series and a repeat-based value estimation strategy. Extensive experiments demonstrate that LangTime achieves state-of-the-art cross-domain forecasting performance, while TimePPO fine-tuning effectively enhances the stability and accuracy of autoregressive forecasting.

Problem

• Address cross-domain generalization in time series forecasting.
• Align cross-modality between language and time series data.
• Mitigate error accumulation in autoregressive forecasting frameworks.

Innovation

• Uses Temporal Comprehension Prompts for domain adaptation
• Incorporates TimePPO for reinforcement learning fine-tuning
• Enhances autoregressive forecasting with multidimensional rewards