Forecasting hotel demand for revenue management using machine learning regression methods
This paper compares the accuracy of a set of 22 methods for short-term hotel demand forecasting for lead times up to 14 days ahead. Machine learning models are compared with methods ranging from seasonal naive to exponential smoothing methods for double seasonality. The machine learning methods cons...
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| Format: | article |
| Language: | eng |
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2022
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| Online Access: | http://hdl.handle.net/10400.1/17572 |
| Country: | Portugal |
| Oai: | oai:sapientia.ualg.pt:10400.1/17572 |