A Forecasting with Multilayer Perceptron Algorithm the Occupancy Rate of Accommodation Establishments in Turkey

Keywords: Artificial Neural Networks, Hotel Occupancy Rate, Multilayer Perceptron


Artificial neural networks (ANN), which is one of the applications of artificial intelligence, is the information processing technology that analyzes the existing data by mimicking the working structure of the human brain and creates new information with different learning algorithms. In recent years, ANN have become popular in scientific and business fields. In the hotel industry, researchers have recently focused on the classification of tourist segments of neural networks and predicting visitor behavior. However, it was requested to include ANN in the hotel occupancy rate prediction. In this paper, forecasted occupancy. rate of the hotel in Turkey by using ANN's a class that Multilayer Perceptron (MLP) algorithm. This study were used “Accommodation Statistics” of monthly data between 2000-2018 years obtained from the Ministry of Culture and Tourism. 3 input values and 1 output value were used in the training of MLP developed for hotel occupancy rate estimation. As a result of the study, a prediction that occupancy rate close to the actual occupancy rate was obtained. And it has been found to have low error rates. The success rate of the algorithm has been seen that be per cent 91.85%. In future studies, it is seen that occupancy rates of hotels operating within a certain region or province limits, tourist spending in hotels, number of overnight stays and average lengths of stay can be estimated by using more variables with ANN.


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How to Cite
M. Yasak, A. Golcuk, and M. Dalmizrak, “A Forecasting with Multilayer Perceptron Algorithm the Occupancy Rate of Accommodation Establishments in Turkey”, IJISAE, vol. 8, no. 2, pp. 66-70, Jun. 2020.
Research Article