轻量级方法让时间序列模型推理时自动适应新数据
Accurate Parameter-Efficient Test-Time Adaptation for Time Series Forecasting
- 仅更新输入输出的小模块，不改主干网络
- 在多个数据集上各预测时长表现优于或持平基线
- 适合资源受限场景下的实时预测应用
现实世界的时间序列常具有非平稳特性，导致预训练预测模型性能下降。测试时自适应（TTA）通过在推理阶段调整模型来缓解此问题，但现有方法通常需更新完整模型，带来高内存与计算开销。本文提出PETSA，一种参数高效的方法，仅通过更新输入和输出端的小型校准模块实现测试时适配。PETSA采用低秩适配器与动态门控机制，无需重新训练即可调整表征。为在有限适配能力下保持精度，设计了一种融合三项的专用损失函数：（1）鲁棒项，（2）频域项以保留周期性，（3）块级结构项实现结构对齐。实验结果表明，PETSA在多个基准数据集上均取得竞争力或更优性能，且所需参数远少于基线方法。代码已开源。
原文摘要 · Abstract (English)
Real-world time series often exhibit a non-stationary nature, degrading the performance of pre-trained forecasting models. Test-Time Adaptation (TTA) addresses this by adjusting models during inference, but existing methods typically update the full model, increasing memory and compute costs. We propose PETSA, a parameter-efficient method that adapts forecasters at test time by only updating small calibration modules on the input and output. PETSA uses low-rank adapters and dynamic gating to adjust representations without retraining. To maintain accuracy despite limited adaptation capacity, we introduce a specialized loss combining three components: (1) a robust term, (2) a frequency-domain term to preserve periodicity, and (3) a patch-wise structural term for structural alignment. PETSA improves the adaptability of various forecasting backbones while requiring fewer parameters than baselines. Experimental results on benchmark datasets show that PETSA achieves competitive or better performance across all horizons. Our code is available at: https://github.com/BorealisAI/PETSA
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。