用通用模型实现零样本时间序列预测，再微调提升精度。
Foundation Models and Fine-Tuning: Toward a New Generation of Models for Time Series Forecasting
- 构建大规模预训练模型，学习通用时序特征表示。
- 微调后在多个数据集上显著优于零样本基线。
- 适合希望省去调参、快速部署的工业场景使用者。
受自然语言处理中大语言模型突破的启发，基础模型成为零样本时间序列预测的有前景范式，可在预训练未见的数据集上实现准确预测。这些模型参数量从数百万到数亿不等，在海量多样时序数据上进行预训练，学习可泛化的表征，支持点预测与概率预测。该方法减少对特定数据集建模和手动调参的需求，为各类预测任务提供统一解决方案。本文综述了支撑此类模型的主要架构、预训练策略及优化方法，并研究了选定基础模型在预训练后的微调效果。实证结果表明，微调步骤能持续提升模型在具体数据集上的预测精度，优于零样本基准。
原文摘要 · Abstract (English)
Inspired by recent breakthroughs in large language models for natural language processing, foundation models have emerged as a promising paradigm for zero-shot time series forecasting, enabling accurate predictions on datasets never seen during pre-training. Ranging from tens to hundreds of millions of parameters, these models are pre-trained on vast and diverse collections of time series, learning generalizable representations that support both point and probabilistic forecasting. This approach alleviates the need for dataset-specific model design and manual tuning, offering a unified solution across forecasting problems. In this work, we review the main architectures, pre-training strategies, and optimization methods underpinning these models. We further investigate post-pre-training fine-tuning of selected foundation models to enhance their performance on specific datasets. Our empirical results demonstrate that this step consistently improves forecasting accuracy over the zero-shot baseline.
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