Publisher
Springer Nature Switzerland
Reference14 articles.
1. Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: a next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, pp. 2623–2631. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3292500.3330701
2. Böse, J.H., et al.: Probabilistic demand forecasting at scale. Proc. VLDB Endowment 10(12), 1694–1705 (2017)
3. Cerqueira, V., Torgo, L., Soares, C.: Machine learning vs statistical methods for time series forecasting: Size matters (2019). https://doi.org/10.48550/arxiv.1909.13316
4. Eisenach, C., Patel, Y., Madeka, D.: Mqtransformer: multi-horizon forecasts with context dependent and feedback-aware attention. arXiv:2009.14799 (2020)
5. Falatouri, T., Darbanian, F., Brandtner, P., Udokwu, C.: Predictive analytics for demand forecasting-a comparison of sarima and lstm in retail scm. Procedia Comput. Sci. 200, 993–1003 (2022)