[Wang L.,Wnag X., Zhao Z. (2024). Mid-term electricity demand forecasting using improved multi-mode reconstruction and particle swarm-enhanced support vector regression. Energy, 304(132021).]Search in Google Scholar
[Dalal S., Lilhore U.K., Simaiya S., Radulescu M.,Belascu L. (2024). Improving efficiency and sustainability via supply chain optimization through CNNs and BiLSTM. Technological Forecasting and Social Change, 209(123841).]Search in Google Scholar
[Taillardat M., Fougères A.L., Naveau P., Fondeville R. (2023). Evaluating probabilistic forecasts of extremes using continuous ranked probability score distributions. International Journal of Forecasting, 39, 1448-1459.]Search in Google Scholar
[Abolghasemi M., Tarr G., Bergmeir C. (2024). Machine learning applications in hierarchical time series forecasting: Investigating the impact of promotions. International Journal of Forecasting, 40, 597-615.]Search in Google Scholar
[Ye L., Xie N., Boylan J.E., Shang Z. (2024). Forecasting seasonal demand for retail: A Fourier time-varying grey model. International Journal of Forecasting, 40, 1467-1485.]Search in Google Scholar
[Corsini R.R., Costa A., Fichera S., Framinan J.M. (2024). Digital twin model with machine learning and optimization for resilient production–distribution systems under disruptions. Computers & Industrial Engineering. 191(110145).]Search in Google Scholar
[Younespour M., Esmaelian M., Kianfar K. (2024). Optimizing the strategic and operational levels of demand-driven MRP using a hybrid GA-PSO algorithm. Computers & Industrial Engineering. 193(110306).]Search in Google Scholar
[Wellens A.P., Boute R.N., Udenio M. (2024). Simplifying tree-based methods for retail sales forecasting with explanatory variables. European Journal of Operational Research. 314, 523-539.]Search in Google Scholar
[Long X., Bui Q., Oktavian G., Schmidt D.F., Bergmeir C., Godahewa R., Lee S.P., Zhao K., Condylis P. (2025). Scalable probabilistic forecasting in retail with gradient boosted trees: A practitioner’s approach. International Journal of Production Economics. 279(109449)]Search in Google Scholar
[Wu Y., Meng X., Zhang J., He Y., Romo J.A., Dong Y., Lu D. (2024). Effective LSTMs with seasonal-trend decomposition and adaptive learning and niching-based backtracking search algorithm for time series forecasting. Expert Systems with Applications. 236(121202)]Search in Google Scholar
[Ahmed S., Chakrabortty R.K., Essam D.L., Ding W. (2024). A switching based forecasting approach for forecasting sales data in supply chains. Applied Soft Computing. 167(112419)]Search in Google Scholar
[Khedr A.M., S S.R. (2024). Enhancing supply chain management with deep learning and machine learning techniques: A review. Journal of Open Innovation: Technology, Market, and Complexity. 10(100379)]Search in Google Scholar
[Sukolkit N., Arunyanart S., Apichottanakul A. (2024). An open innovative inventory management based demand forecasting approach for the steel industry. Journal of Open Innovation: Technology, Market, and Complexity. 10(100407)]Search in Google Scholar
[Chae B., Sheu C., Park E.O. (2024). The value of data, machine learning, and deep learning in restaurant demand forecasting: Insights and lessons learned from a large restaurant chain. Decision Support Systems. 184(114291)]Search in Google Scholar
[Keswani M. (2024). A comparative analysis of metaheuristic algorithms in interval-valued sustainable economic production quantity inventory models using center-radius optimization. Decision Analytics Journal. 12(100508)]Search in Google Scholar
[Berrisch J., Ziel F. (2023). CRPS learning. Journal of Econometrics. 237(105221)]Search in Google Scholar
[Koochali, A., Schichtel, P., Dengel, A., & Ahmed, S. (2022). Random Noise vs. State-of-the-Art Probabilistic Forecasting Methods: A Case Study on CRPS-Sum Discrimination Ability. Applied Sciences, 12(10), 5104.]Search in Google Scholar
