A carregar...
Publicação
Hotel Demand Forecasting using Foundation Models
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 1.07 MB | Adobe PDF |
Autores
Orientador(es)
Resumo(s)
The hospitality industry is increasingly reliant on accurate demand forecasting to support
revenue management and strategic planning. Traditional forecasting methods often struggle
to capture the nonlinear and seasonal patterns typical of hotel booking data. To address these
limitations, this study evaluates the performance of three types of models: statistical models
(e.g., ARIMA, Prophet), which rely on predefined mathematical structures; machine learning
models (e.g., Random Forest, XGBoost), which learn patterns from historical data; and
Foundation Models (e.g., TimeGPT), large-scale pretrained models applied in a zero-shot
forecasting setting. The objective is to evaluate how pretrained FMs perform in forecasting
hotel occupancy rates across different horizons (1-day, 7-day, and 28-day), using real
reservation data from 100 hotels. The CRISP-DM methodology and the Kedro framework were
employed to facilitate standardized ingestion, data cleaning, feature engineering, and
modeling. Forecasting targets were derived from the occupancy rate. Results show that
XGBoost consistently achieved the best balance between accuracy and computational
efficiency across all horizons. Random Forest offered solid performance but with higher
computational costs than XGBoost. TimeGPT, despite operating without local training or finetuning, performed competitively, especially in larger hotels, with more financial activity and
longer booking windows. Prophet performed well in smaller hotels with clear seasonal
patterns, while ARIMA was most effective in the simplest environments. TimeGPT had the
highest inference time, and while recent API updates allow the extraction of SHAP values for
partial interpretability, it remains a black-box model. No single model outperformed across all
contexts, confirming that model suitability depends on hotel characteristics. Limitations of this
work include the absence of variables such as events or weather; limited number of
Foundation Models tested due to computational constraints. Still, the study provides evidence
supporting the potential of Foundation Models,such as TimeGPT, in hotel demand forecasting
and offers a replicable methodology for model comparison in time series forecasting tasks
within the hospitality industry.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
Palavras-chave
Hotel Industry Hotel Demand Forecasting Artificial Intelligence Large Language Models Time Series Models SDG 8 - Decent work and economic growth