Enhanced demand forecasting by combining analytical models and machine learning models-Reference-Cited by-同舟云学术

Enhanced demand forecasting by combining analytical models and machine learning models

Published:2024-08-30 Issue: Volume: Page:
ISSN:1476-6930
Container-title:Journal of Revenue and Pricing Management
language:en
Short-container-title:J Revenue Pricing Manag

Author:

Nanty Simon,Fiig Thomas,Zannier Ludovic,Defoin-Platel Michael

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1057/s41272-024-00490-w.pdf

Reference55 articles.

1. Bai, Lei, Lina Yao, Salil S. Kanhere, Zheng Yang, Jing Chu, and Xianzhi Wang. 2019. Passenger demand forecasting with multi-task convolutional recurrent neural networks. Advances in Knowledge Discovery and Data Mining: 23rd Pacific-Asia Conference, PAKDD 2019, Macau, China, April 14-17, 2019, Proceedings, Part II 23. 29–42.

2. Bengio, Yoshua, Réjean. Ducharme, and Pascal Vincent. 2000. A neural probabilistic language model. Advances in Neural Information Processing Systems 2000: 13.

3. Bikmukhametov, Timur, and Johannes Jäschke. 2020. Combining machine learning and process engineering physics towards enhanced accuracy and explainability of data-driven models. Computers & Chemical Engineering (elsevier) 138: 106834.

4. Bjerrum, Esben Jannik. 2017. SMILES enumeration as data augmentation for neural network modeling of molecules. https://arxiv.org/abs/1703.07076.

5. Blanco-Oliver, A., A. Irimia-Dieguez, M. Oliver-Alfonso, M. Vázquez-Cueto, M. Oliver-Alfonso, A. Spain. Vázquez-Cueto, et al. 2016. Hybrid model using logit and nonparametric methods for predicting micro-entity failure. Investment Management and Financial Innovations. https://doi.org/10.21511/imfi.13(3).2016.03.