DAShrink: Disagreement-Adaptive Shrinkage for Multivariate Forecast Combination

Combining multivariate forecasters requires deciding how strongly to depart from equal weights. A learned router can exploit differences between experts, but a large weight change can also produce a large forecast change when their predictions disagree. We propose disagreement-adaptive shrinkage (DAShrink), which pulls contextual combination weights toward the uniform average. The shrinkage strength depends on the spread of history-normalized expert predictions. A scalar chosen on validation windows controls the overall departure from equal weighting and can recover the uniform average exactly. The resulting weights have a positive lower bound, and the forecast deviation from the uniform average is bounded by the range of expert predictions. We evaluate a model with three heterogeneous experts on ten multivariate benchmarks with forty dataset-horizon settings. Against eleven baselines, DAShrink achieves an overall average rank of 1.262 and ranks first in 34 settings for MSE and 34 for MAE. With trained parameters fixed, removing either combination control increases both errors in all eight ETT ablation settings.