REQUA: RESIDUAL-GROUNDED QUERY ADAPTATION FOR COVARIATE-AWARE FORECASTING

Recent time-series foundation models (TSFMs) increasingly support native multivariate forecasting and have demonstrated substantial gains over univariate modeling on multivariate tasks. However, they always treat all covariates equally, which implies that adapting to downstream tasks, where the focus is on predicting a subset of target covariates, may require fundamentally altering the model’s internal structure. It is reasonable to expect that such adaptation cannot be achieved under the conventional fine-tuning paradigm on the pretrained models. Therefore, in this paper, we introduce ReQuA, a lightweight covariate-aware adaptation framework that keeps the TSFM and its original prediction pathway frozen. ReQuA constructs queries conditioned on the target history and forecast horizon to aggregate task-relevant auxiliary information. The backbone’s forecast-generating latent states attend to this representation to obtain a residual correction, whose magnitude is controlled by a single coefficient calibrated on held-out data. Experiments on eight widely used time-series datasets demonstrate that ReQuA delivers substantial and consistent improvements across frozen TSFM backbones with few additional parameters and little computational overhead, while global calibration effectively suppresses harmful corrections.