Interpretable Drift Correction with Adaptive Transformation Selection

An interpretable drift adaptation mechanism for detection and correction is introduced, based on statistical tests and transparent transformations.  In contrast to prior work that applies a single universal mapping, the method adaptively selects transformations by drift type (location, scale, shape, or extreme), identified via Kolmogorov–Smirnov tests, Wasserstein distance, and distributional comparisons.  Each category is corrected with a suitable transformation such as mean-variance scaling, rank-based adjustment, or quantile mapping.  A novel Wasserstein–aware fallback rule ensures balanced corrections across metrics.  Applied to salary data across roles and years, the approach reduced Wasserstein distance by over 95% in location+scale drifts (e.g., from 22 416 to 1 118).  The method remains easy to interpret, auditable for regulatory checks, and effective for practical drift correction.