Deep Learning-Based Forecasting of Hotel KPIs: A Cross-City Analysis of Global Urban Markets
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909675587371008 |
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| author | Atapattu, C. J. Cui, Xia Abeynayake, N. R |
| author_facet | Atapattu, C. J. Cui, Xia Abeynayake, N. R |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_03028 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Learning-Based Forecasting of Hotel KPIs: A Cross-City Analysis of Global Urban Markets Atapattu, C. J. Cui, Xia Abeynayake, N. R Machine Learning Artificial Intelligence 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. |
| title | Deep Learning-Based Forecasting of Hotel KPIs: A Cross-City Analysis of Global Urban Markets |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2507.03028 |