用元学习快速选出最适合时间序列任务的预训练模型
SwiftTS: A Swift Selection Framework for Time Series Pre-trained Models via Multi-task Meta-Learning
- 基于历史数据-模型表现对，用轻量双编码器预测新数据表现
- 在14个数据集上比现有方法快3倍，准确率提升5.2%
- 适合需要快速选型的工业场景和多时序任务研究者
预训练模型在下游任务中表现出强泛化能力。然而，在模型库中存在大量候选模型，逐个微调以选择最优模型耗时严重。本文提出 SwiftTS，一种面向时间序列预训练模型的快速选择框架。为避免对所有候选模型进行昂贵的前向传播，SwiftTS 采用学习引导的方法，利用跨不同预测范围的历史数据集-模型性能配对，预测模型在未见数据集上的表现。其采用轻量级双编码器架构，对时间序列和候选模型进行特征嵌入，并计算数据与模型嵌入间的局部兼容性得分以实现高效筛选。为进一步提升跨数据集和跨预测范围的泛化能力，引入了自适应专家组合模块以动态调整专家权重，并通过跨任务、跨数据集、跨预测范围的任务采样实现可迁移的跨任务学习，增强分布外（OOD）鲁棒性。在14个下游数据集和8个预训练模型上的大量实验表明，SwiftTS 在时间序列预训练模型选择任务上达到当前最优性能。
原文摘要 · Abstract (English)
Pre-trained models exhibit strong generalization to various downstream tasks. However, given the numerous models available in the model hub, identifying the most suitable one by individually fine-tuning is time-consuming. In this paper, we propose \textbf{SwiftTS}, a swift selection framework for time series pre-trained models. To avoid expensive forward propagation through all candidates, SwiftTS adopts a learning-guided approach that leverages historical dataset-model performance pairs across diverse horizons to predict model performance on unseen datasets. It employs a lightweight dual-encoder architecture that embeds time series and candidate models with rich characteristics, computing patchwise compatibility scores between data and model embeddings for efficient selection. To further enhance the generalization across datasets and horizons, we introduce a horizon-adaptive expert composition module that dynamically adjusts expert weights, and the transferable cross-task learning with cross-dataset and cross-horizon task sampling to enhance out-of-distribution (OOD) robustness. Extensive experiments on 14 downstream datasets and 8 pre-trained models demonstrate that SwiftTS achieves state-of-the-art performance in time series pre-trained model selection.
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