A Study on the Development of a Hotel Economic Trend Predictive Model Using Machine Learning Models
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초록
(Purpose) Based on a total of 21 hotel economic trend data collected through the Korea Statistical Information Service (KOSIS) and the Korea Hotel Industry Association, this study uses a machine learning model to determine the prediction model estimation and the importance of variables to propose a differentiation strategy for hotels to preemptively respond to rapidly changing economic fluctuations. (Design/methodology/approach) Based on previous research, a machine learning model was used to grasp the prediction accuracy and importance of variables in the hotel economic trend model. Data collection was based on a total of 12 years of data from 2011 to 2022, and the data cycle was set on a monthly and quarterly basis. On the other hand, the orange data mining 3.32 program, a machine learning program, was used to grasp the prediction accuracy of hotel business trends. (Findings) The prediction accuracy was the highest in the linear form at 93.3%, followed by the neural network at 89.7%, linear at 83.3%, and support vector machine at 75.6%. In other words, it is judged that it is necessary to actively use adaboost and neural network models to predict hotel business trends and future time series models. (Research implications or Originality) In order to predict hotel economic trends, in addition to existing macroeconomic indicators, room profit rate indicators, which directly affect hotel revenue, should be considered. Implications of this study provide academic and managerial suggestions and limitations of the study and directions for future research are suggested.
Abstract
(Purpose) Based on a total of 21 hotel economic trend data collected through the Korea Statistical Information Service (KOSIS) and the Korea Hotel Industry Association, this study uses a machine learning model to determine the prediction model estimation and the importance of variables to propose a differentiation strategy for hotels to preemptively respond to rapidly changing economic fluctuations. (Design/methodology/approach) Based on previous research, a machine learning model was used to grasp the prediction accuracy and importance of variables in the hotel economic trend model. Data collection was based on a total of 12 years of data from 2011 to 2022, and the data cycle was set on a monthly and quarterly basis. On the other hand, the orange data mining 3.32 program, a machine learning program, was used to grasp the prediction accuracy of hotel business trends. (Findings) The prediction accuracy was the highest in the linear form at 93.3%, followed by the neural network at 89.7%, linear at 83.3%, and support vector machine at 75.6%. In other words, it is judged that it is necessary to actively use adaboost and neural network models to predict hotel business trends and future time series models. (Research implications or Originality) In order to predict hotel economic trends, in addition to existing macroeconomic indicators, room profit rate indicators, which directly affect hotel revenue, should be considered. Implications of this study provide academic and managerial suggestions and limitations of the study and directions for future research are suggested.
참고문헌 (46)
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