用LSTM预测全球5城酒店关键指标，精度与城市特性相关。
Deep Learning-Based Forecasting of Hotel KPIs: A Cross-City Analysis of Global Urban Markets
- 采用LSTM模型分析五城月度数据，结合时序分解提升预测能力。
- 曼彻斯特与孟买预测最准，迪拜和曼谷受季节影响波动大。
- 结果可为旅游决策者和城市规划提供跨城市参考框架。
本研究采用长短期记忆（LSTM）网络，对曼彻斯特、阿姆斯特丹、迪拜、曼谷和孟买五个主要城市的关键绩效指标（KPIs）——入住率（OCC）、平均房价（ADR）和每间可售房收入（RevPAR）进行预测。数据涵盖2018年至2025年的月度信息，其中80%用于训练，20%用于测试。通过先进的时序分解与机器学习技术，实现高精度预测与趋势识别。结果显示，曼彻斯特与孟买表现出最高预测准确率，反映其需求模式稳定；而迪拜与曼谷则因季节性及事件驱动呈现更高波动性。研究验证了LSTM在城市酒店业预测中的有效性，并提供了跨国比较的数智化决策支持框架，具备跨城市泛化潜力，适用于旅游行业与城市规划领域。
原文摘要 · Abstract (English)
This study employs Long Short-Term Memory (LSTM) networks to forecast key performance indicators (KPIs), Occupancy (OCC), Average Daily Rate (ADR), and Revenue per Available Room (RevPAR), across five major cities: Manchester, Amsterdam, Dubai, Bangkok, and Mumbai. The cities were selected for their diverse economic profiles and hospitality dynamics. Monthly data from 2018 to 2025 were used, with 80% for training and 20% for testing. Advanced time series decomposition and machine learning techniques enabled accurate forecasting and trend identification. Results show that Manchester and Mumbai exhibited the highest predictive accuracy, reflecting stable demand patterns, while Dubai and Bangkok demonstrated higher variability due to seasonal and event-driven influences. The findings validate the effectiveness of LSTM models for urban hospitality forecasting and provide a comparative framework for data-driven decision-making. The models generalisability across global cities highlights its potential utility for tourism stakeholders and urban planners.
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