A. K. El-Kharbotly

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

Accurate forecasting of intermittent demand remains a persistent challenge for deep learning methods, partly due to the inadequacy of conventional error metrics in capturing zero-demand forecasting performance. This study tackles this gap by introducing Z% and NZ%, two decomposed accuracy metrics that distinctly quan...