With the rising wave of travelers and changing market landscape, understanding marketplace dynamics in commoditized accommodations in the hotel industry has never been more important. In this research, a machine learning approach is applied to build a framework that can forecast the unconstrained and constrained market demand (aggregated and segmented) by leveraging data from disparate sources. Several machine learning algorithms are explored to learn traveler’s booking patterns and the latent progression of the booking curve. This solution can be leveraged by independent hoteliers in their revenue management strategy by comparing their behavior to the market.

随着旅客浪潮的增加和市场格局的变化，了解酒店行业商品化住宿的市场动态变得前所未有的重要。在这项研究中，使用了一种机器学习方法来构建一个框架，该框架可以通过利用来自不同来源的数据来预测不受约束和受约束的市场需求（汇总和细分）。探索了几种机器学习算法来学习旅行者的预订模式和预订曲线的潜在进展。独立酒店经营者可以通过将他们的行为与市场进行比较，在其收入管理策略中利用该解决方案。