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Optimizing Demand Forecasting: Classical Statistical Models vs. AI-Driven Approaches Cover

Optimizing Demand Forecasting: Classical Statistical Models vs. AI-Driven Approaches

Open Access
|Jul 2025

References

  1. 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).
  2. 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).
  3. 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.
  4. 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.
  5. 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.
  6. 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).
  7. 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).
  8. 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.
  9. 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)
  10. 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)
  11. 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)
  12. 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)
  13. 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)
  14. 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)
  15. 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)
  16. Berrisch J., Ziel F. (2023). CRPS learning. Journal of Econometrics. 237(105221)
  17. 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.
Language: English
Page range: 1309 - 1322
Published on: Jul 24, 2025
Published by: The Bucharest University of Economic Studies
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year

© 2025 Coralia Zotic, George Dinu, Mihai-Daniel Roman, published by The Bucharest University of Economic Studies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.