发表机构
Norwegian Computing Center; University of Oslo; Wheelhouse（挪威计算中心; 奥斯陆大学; 轮屋公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文基于Wheelhouse数据构建了全球500个市场2017-2022年的短期租赁日度入住率时间序列及预订轨迹曲线，为统计与机器学习模型提供独特基准。
AI 中文摘要
短期度假租赁，如Airbnb、Homeaway、Vrbo等平台所推广的，是旅游业中日益增长的一部分。本文利用美国公司Wheelhouse的数据，提供了一个独特的、关于短期租赁市场全球市场入住率的大型数据集。该数据集包含全球500个市场的数据。对于每个市场，提供了从2017年1月至2022年12月的日度入住率时间序列，允许研究短期租赁市场演变的本地和全球模式。此外，数据集还包括代表每个市场和住宿日期直至住宿日期前一年的预订轨迹曲线。这个大型数据集包含了时间序列和生存分析数据的独特组合，适合作为经典统计和机器学习模型的方法论基准。
英文摘要
Short-term vacation rentals, as promoted by platforms such as Airbnb, Homeaway, Vrbo, etc., are a growing component of the travel industry. This paper provides a unique, large dataset on global market occupancy for the short-term rental market using data from the American company Wheelhouse. The dataset consists of data for $500$ markets around the world. For each market, a daily occupancy time series from January $2017$ to December $2022$ is provided, allowing for studies of local and global patterns in the evolution of the short-term rental market. Additionally, the dataset includes curves representing the booking trajectory of each market and stay date up to one year prior to the stay date. This large dataset comprises a unique combination of time series and survival analysis data, and is suitable as a methodological benchmark for both classical statistical and machine learning models.
