A pipeline that augments small obsolescence datasets with synthetic data from Real NVP, TVAE, or CTGAN and then self-labels that data with a clustering step lifts Random Forest forecasting accuracy to 96-98%, beating a literature baseline by 5-7%.
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Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications
A pipeline that augments small obsolescence datasets with synthetic data from Real NVP, TVAE, or CTGAN and then self-labels that data with a clustering step lifts Random Forest forecasting accuracy to 96-98%, beating a literature baseline by 5-7%.