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This paper evaluates various forecasting models for daily hotel demand, including SARIMAX, neural networks, and GARCH models, using a dataset from a US hotel between 2015 and 2019. The results indicate that the SARIMAX model with external regressors generally outperforms other models, including ANN-MLP, in most forecast horizons, while GARCH models also show superior predictive accuracy. The findings provide valuable insights for revenue managers on the effectiveness of different forecasting techniques and

Author

taominhanh1406

Language

EN