摘要
Sharing economy platforms for accommodation sharing offer a more flexible supply-demand model compared to traditional hotels that own a fixed number of rooms. However, this flexibility can cause uncertainty and become a challenge if not managed dynamically. An important task for such platforms is forecasting future daily occupancy in a certain area, with sufficient lead time so that they can reach out to new hosts and secure more rooms in time for peak demand. We developed such a forecasting solution for AsiaYo, the largest Chinese language online accommodation sharing platform. We evaluate and compare various forecasting algorithms, including statistical and machine learning methods, using two years of data from AsiaYo on occupancy in different cities. The empirical results show that the occupancy is highly dependent on the weekday, city, and holidays. We show the strengths and weaknesses of different methods in terms of required accuracy level, computation time, and flexibility. © 2019 IEEE.