用大模型替代传统方法，高效选择时间序列预测模型
Efficient Model Selection for Time Series Forecasting via LLMs
- 利用大模型的推理能力，无需预先构建性能矩阵
- 在多个数据集上表现优于传统元学习方法
- 计算开销大幅降低，适合实际部署场景
模型选择是时间序列预测中的关键步骤，传统方法需在多个数据集上进行大量性能评估。元学习方法虽旨在自动化该过程，但通常依赖于预构建的性能矩阵，构建成本较高。本文提出利用大语言模型（LLMs）作为轻量级替代方案进行模型选择，通过LLaMA、GPT和Gemini的广泛实验表明，该方法无需显式性能矩阵即可实现比传统元学习技术和启发式基线更优的性能，同时显著降低计算开销。结果验证了大模型在高效时间序列模型选择中的潜力。
原文摘要 · Abstract (English)
Model selection is a critical step in time series forecasting, traditionally requiring extensive performance evaluations across various datasets. Meta-learning approaches aim to automate this process, but they typically depend on pre-constructed performance matrices, which are costly to build. In this work, we propose to leverage Large Language Models (LLMs) as a lightweight alternative for model selection. Our method eliminates the need for explicit performance matrices by utilizing the inherent knowledge and reasoning capabilities of LLMs. Through extensive experiments with LLaMA, GPT and Gemini, we demonstrate that our approach outperforms traditional meta-learning techniques and heuristic baselines, while significantly reducing computational overhead. These findings underscore the potential of LLMs in efficient model selection for time series forecasting.
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