让时间序列大模型在线自适应，提升预测精度
Lightweight Online Adaption for Time Series Foundation Model Forecasts
- 设计轻量级机制，利用实时数据反馈动态调整模型
- 在多个标准数据集上均显著提升大模型预测性能
- 适合需要持续更新的实时预测场景，如金融、物流
基础模型（FMs）在时间序列预测中展现出巨大潜力。然而，由于在线学习计算成本高，部署后的FMs通常保持固定，无法根据新到达的数据及时调整预测，导致对当前数据特征的适应性不足。这引发了一个问题：能否通过高效利用在线反馈来提升FM的预测表现？本文提出ELF，一种轻量级在线自适应机制，用于响应实时反馈调整FM预测。ELF包含两部分：(a) ELF-Forecaster，用于学习当前数据分布；(b) ELF-Weighter，用于融合原始FM与ELF-Forecaster的预测结果。我们在多个主流时间序列数据集上，结合数个近期基础模型验证了ELF的性能。所有实验均表明，引入ELF后预测效果显著提升。本工作证明，高效利用在线反馈可有效增强基础模型的预测能力。
原文摘要 · Abstract (English)
Foundation models (FMs) have emerged as a promising approach for time series forecasting. While effective, FMs typically remain fixed during deployment due to the high computational costs of learning them online. Consequently, deployed FMs fail to adapt their forecasts to current data characteristics, despite the availability of online feedback from newly arriving data. This raises the question of whether FM performance can be enhanced by the efficient usage of this feedback. We propose ELF to answer this question. ELF is a lightweight mechanism for the online adaption of FM forecasts in response to online feedback. ELF consists of two parts: a) the ELF-Forecaster which is used to learn the current data distribution; and b) the ELF-Weighter which is used to combine the forecasts of the FM and the ELF-Forecaster. We evaluate the performance of ELF in conjunction with several recent FMs across a suite of standard time series datasets. In all of our experiments we find that using ELF improves performance. This work demonstrates how efficient usage of online feedback can be used to improve FM forecasts.
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。