Published Online:30 May 2024https://doi.org/10.1287/isre.2023.0130
- Cited by
- SSRN Electronic Journal, Vol. 25

Volume 36, Issue 1
March 2025
Pages iii-xi, 1-646, C2
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- Received:March 07, 2023
- Accepted:April 13, 2024
- Published Online:May 30, 2024
Copyright © 2024, INFORMS
Cite as
Yu Jeffrey Hu; , Jeroen Rombouts; , Ines Wilms (2024) Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms. Information Systems Research 36(1):552-571.
https://doi.org/10.1287/isre.2023.0130
Keywords
The authors thank the senior editor, associate editor, and referees for their constructive and detailed comments, which substantially improved the quality of the manuscript. The authors are grateful to Benjamin Wolter for expert advice and to Arnaud Dufays, Sarah Gelper, Harris Kyriakou, and Olivier Scaillet for comments and checks provided on earlier versions of the paper. The authors thank participants at the Symposium on Statistical Challenges in Electronic Commerce Research 2022; the Conference on Data Science, Statistics & Visualisation 2022; the International Symposium on Forecasting 2022; and the Workshop on Information Technologies and Systems 2022 for helpful discussions.
