NOMADD fits a model on each past time period, compresses parameter changes with low-rank SVD, and extrapolates a damped linear trend to predict the model's future boundary, improving drift robustness across models from XGBoost to TabPFN.
Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V
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NOMADD: Numerical Optimization of Models Adapting to Data Drift
NOMADD fits a model on each past time period, compresses parameter changes with low-rank SVD, and extrapolates a damped linear trend to predict the model's future boundary, improving drift robustness across models from XGBoost to TabPFN.