A. K. El-Kharbotly

Feature engineering for intermittent demand forecasting: zero-detection and forecast performance across GRU, LSTM, and TCN architectures

These findings emphasize the diagnostic value of Z% and the complementary but secondary role of NZ, providing empirical guidance on feature engineering, model behavior, and deep learning architecture selection for intermittent demand forecasting with direct implications for operational decision-making.