TY -的A2 Hassanien Abd E.I.baset AU -王,天洋PY - 2021 DA - 2021/03/18 TI -一个智能客流预测方法对定价策略和酒店操作SP - 5520223六世- 2021 AB -酒店业在旅游业的发展中扮演着关键角色。预测未来需求的酒店是酒店收益管理过程中的一个关键步骤。酒店客流预测中发挥着重要作用指导制定酒店价格和操作策略。一方面,酒店客流预测可以为酒店管理者提供决策支持,有效地避免酒店资源的浪费和损失的收入造成客户的损失。另一方面,它是保证优先占领商机的酒店企业,可以帮助酒店企业合理调整自己的经营策略,以更好地适应市场情况。此外,酒店客流预测有助于判断酒店业的整体运营情况和评估酒店项目的风险水平。酒店客流受到许多因素的影响,比如天气、环境、季节,假期,经济,和突发事件,具有复杂的非线性波动的特点。现有的需求预测方法包括线性方法和非线性方法。线性预测方法依赖于环境和时间序列的稳定性,所以他们不能完全模拟酒店客流的复杂非线性波动特征。 Traditional nonlinear prediction methods need to improve the prediction accuracy, and they are difficult to deal with the increasing data of hotel passenger flow. Based on the above analysis, this paper constructs a deep learning prediction model based on Long Short-Term Memory (LSTM) to predict the number of actual monthly arrival bookings. The number of actual monthly arrival bookings can reflect the actual monthly passenger flow of a hotel. The prediction model can effectively reduce the loss caused by cancellation or nonarrival of bookings due to various reasons and improve the hotel revenue. The experimental part of this paper is based on the booking demand dataset of a resort hotel in Portugal from July 1, 2015, to August 31, 2017. Artificial neural network (ANN) and support vector regression (SVR) are built as benchmark models to predict the number of actual monthly arrival bookings of this hotel. The experimental results show that, compared with the benchmark models, the LSTM model can effectively improve the prediction ability and provide necessary reference for the hotel's future pricing decision and operation mode arrangement. SN - 1076-2787 UR - https://doi.org/10.1155/2021/5520223 DO - 10.1155/2021/5520223 JF - Complexity PB - Hindawi KW - ER -