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 AdapTS to answer this question. AdapTS is a lightweight mechanism for the online adaption of FM forecasts in response to online feedback. AdapTS consists of two parts: a) the AdapTS-Forecaster which is used to learn the current data distribution; and b) the AdapTS-Weighter which is used to combine the forecasts of the FM and the AdapTS-Forecaster. We evaluate the performance of AdapTS in conjunction with several recent FMs across a suite of standard time series datasets. In all of our experiments we find that using AdapTS improves performance. This work demonstrates how efficient usage of online feedback can be used to improve FM forecasts.

翻译：基础模型已成为时间序列预测的一种有前景的方法。尽管有效，但由于在线学习的高计算成本，基础模型在部署期间通常保持固定。因此，尽管有新到达数据提供的在线反馈可用，已部署的基础模型未能使其预测适应当前数据特征。这引发了一个问题：能否通过有效利用这种反馈来提升基础模型的性能？我们提出AdapTS来回答这个问题。AdapTS是一种轻量级机制，用于根据在线反馈对基础模型预测进行在线自适应。AdapTS包含两部分：a) AdapTS-Forecaster，用于学习当前数据分布；以及b) AdapTS-Weighter，用于结合基础模型与AdapTS-Forecaster的预测。我们在多个标准时间序列数据集上评估了AdapTS与若干近期基础模型结合的性能。在所有实验中，我们发现使用AdapTS均能提升性能。这项工作展示了如何通过有效利用在线反馈来改进基础模型预测。