TFMAdapter: Lightweight Instance-Level Adaptation of Foundation Models for Forecasting with Covariates

Time Series Foundation Models (TSFMs) have recently achieved state-of-the-art performance in univariate forecasting on new time series simply by conditioning on a brief history of past values. Their success demonstrates that large-scale pretraining across diverse domains can acquire the inductive bias to generalize from temporal patterns in a brief history. However, most TSFMs are unable to leverage covariates-future-available exogenous variables critical for accurate forecasting in many applications-due to their domain-specific nature and the lack of associated inductive bias.