Lightweight Online Adaption for Time Series Foundation Model Forecasts

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, comprising two parts: a) the ELF-Forecaster, which learns the current data distribution; and b) the ELF-Weighter, which combines forecasts from the FM and the ELF-Forecaster. We evaluate ELF with several recent FMs on standard time series datasets and find that it improves performance in all cases. This work shows that efficiently leveraging online feedback can enhance FM forecasts.