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

• URL: http://arxiv.org/abs/2503.08271v1
• Date: Tue, 11 Mar 2025 10:40:39 GMT
• Title: LangTime: A Language-Guided Unified Model for Time Series Forecasting with Proximal Policy Optimization
• Authors: Wenzhe Niu, Zongxia Xie, Yanru Sun, Wei He, Man Xu, Chao Hao,
• Abstract summary: LangTime is a language-guided unified model for time series forecasting.<n>TimePPO is a reinforcement learning-based fine-tuning algorithm.
• Score: 3.1819993716919472
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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