Forecasting with multilayer perceptron algorithm the occupancy rate of accommodation establishments in Turkey

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 forecast. In this paper, forecasted occupancy rate of the hotel in Turkey by using ANN a class that Multilayer Perceptron (MLP) algorithm. Accommodation Statistics” of monthly data between 2000-2018 years obtained from the Republic of Turkey Ministry of Culture and Tourism was used in this study. 3 input values and 1 output value were used in the training of MLP developed for hotel occupancy rate forecast. As a result of the study, a forecast that occupancy rate close to the actual occupancy rate was obtained. İt has been found to have low error rates. The success rate of the algorithm is 91.85 %.