A machine learning model for occupancy rates and demand forecasting in the hospitality industry

Occupancy rate forecasting is a very important step in the decision-making process of hotel planners and managers. Popular strategies as Revenue Management feature forecasting as a vital activity for dynamic pricing, and without accurate forecasting, errors in pricing will negatively impact hotel fi...

Full description

Autores:

Tipo de recurso:

Fecha de publicación:

2016

Institución:

Universidad Tecnológica de Bolívar

Repositorio:

Repositorio Institucional UTB

Idioma:

eng

OAI Identifier:

oai:repositorio.utb.edu.co:20.500.12585/8994

Acceso en línea:

https://hdl.handle.net/20.500.12585/8994

Palabra clave:

Forecasting
Hotel occupancy. Demand
Kernel Ridge Regression
Machine learning
Neural Networks
Ridge regression
Artificial intelligence
Costs
Decision making
Economics
Hotels
Learning systems
Neural networks
Regression analysis
Decision making process
Financial performance
Kernel ridge regressions
Machine learning models
Machine learning techniques
Mean absolute percentage error
Ridge regression
Specialized software
Forecasting

Rights

restrictedAccess

License

http://creativecommons.org/licenses/by-nc-nd/4.0/