Demand forecasting model using hotel clustering findings for hospitality industry

Kaya K. , Yılmaz Y. , Yaslan Y. , Öğüdücü Ş. , Cingi F.

INFORMATION PROCESSING & MANAGEMENT, vol.59, no.1, 2022 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 59 Issue: 1
  • Publication Date: 2022
  • Doi Number: 10.1016/j.ipm.2021.102816
  • Keywords: Demand forecasting, Attention LSTM, Deep learning, Hotel clustering, Feature embedding, TOURISM DEMAND, PREDICTION, LSTM


Tourism has become a growing industry day by day with the developing economic conditions and the increasing communication and social interaction ability of the people. Forecasting tourism demand is not only important for tourism operators to maximize their revenues but also important for the formation of economic plans of the countries on a global scale. Based on the predictions countries are able to regulate the sectors that benefit economically from tourism locally. Therefore, it is crucial to accurately predict the demand in many weeks advance. In this study, we propose a new demand forecasting model for the hospitality industry that forecasts weekly hotel demand four weeks in advance through Attention-Long Short Term Memory (Attention-LSTM). Unlike most of the existing methods, the proposed method utilizes the time series demand data together with additional features obtained from K-Means Clustering findings such as Top 10 Hotel Features or Hotel Embeddings obtained using Neural Networks (NN). While creating our model, the clustering part was influenced by the fact that travelers choose their accommodation according to certain criteria, and the hotels meeting similar criteria may have similar demands. Therefore, before the clustering part, we also applied methods that would enable us to represent the features of the hotels more properly and we observed that 10-D Embedded Hotel Data representation with NN Embeddings came to the fore. In order to observe the performance of the proposed hotel demand forecasting model we used a real-world dataset provided by a tourism agency in Turkey and the results show that the proposed model achieves less mean absolute error and mean absolute percentage error (at worst % 3 and at most % 29 improvements) compared to the currently used machine learning and deep learning models.