The application of machine learning and deep learning on demand forecasting across time-critical industries: A systematic review

The applications of machine learning and deep learning in demand forecasting have attracted increasing attention, as they offer remarkable predictive capabilities that help automate forecasting processes and achieve higher accuracy. While numerous review studies have examined solutions within specific industries, there is a lack of comprehensive literature review investigating these solutions across different sectors. Therefore, this study overviews machine learning and deep learning applications in demand forecasting across time-critical industries, including power, tourism, water, transportation, and food. A two-tier classification framework is proposed to categorize demand forecasting studies by both application industry and methodological architecture. In addition, the most popular statistical metrics for evaluating demand forecasting are reviewed and summarized. This study reveals that while machine learning and deep learning are effective for demand forecasting, model selection highly depends on the target industry, data availability, and computational resources. Therefore, this study proposes a conceptual, generic framework that maps data characteristics to appropriate model architecture classes for demand forecasting and recommends adopting scale-independent evaluation metrics. The proposed framework offers a structured pipeline and practical guidance for practitioners and researchers to design forecasting systems across diverse industries, enabling consistent comparative analysis.