LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

• URL: http://arxiv.org/abs/2308.08469v5
• Date: Thu, 18 Jan 2024 06:01:28 GMT
• Title: LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters
• Authors: Ching Chang, Wei-Yao Wang, Wen-Chih Peng, Tien-Fu Chen
• Abstract summary: We propose a framework for time-series forecasting with pre-trained Large Language Models (LLMs) LLM4TS consists of a two-stage fine-tuning strategy to align LLMs with the nuances of time-series data, and the textitforecasting fine-tuning stage for downstream time-series forecasting tasks. Our framework features a novel two-level aggregation method that integrates multi-scale temporal data within pre-trained LLMs, enhancing their ability to interpret time-specific information.
• Score: 12.887118862534331
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multivariate time-series forecasting is vital in various domains, e.g., economic planning and weather prediction. Deep train-from-scratch models have exhibited effective performance yet require large amounts of data, which limits real-world applicability. Recently, researchers have leveraged the representation learning transferability of pre-trained Large Language Models (LLMs) to handle limited non-linguistic datasets effectively. However, incorporating LLMs with time-series data presents challenges of limited adaptation due to different compositions between time-series and linguistic data, and the inability to process multi-scale temporal information. To tackle these challenges, we propose LLM4TS, a framework for time-series forecasting with pre-trained LLMs. LLM4TS consists of a two-stage fine-tuning strategy: the \textit{time-series alignment} stage to align LLMs with the nuances of time-series data, and the \textit{forecasting fine-tuning} stage for downstream time-series forecasting tasks. Furthermore, our framework features a novel two-level aggregation method that integrates multi-scale temporal data within pre-trained LLMs, enhancing their ability to interpret time-specific information. In experiments across 7 time-series forecasting datasets, LLM4TS is superior to existing state-of-the-art methods compared with trained-from-scratch models in full-shot scenarios, and also achieves an average improvement of 6.84% in MSE in few-shot scenarios. In addition, evaluations compared with different self-supervised learning approaches highlight LLM4TS's effectiveness with representation learning in forecasting tasks.

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