Development of Fuzzy Time Series Model for Hotel Occupancy Forecasting
Year: 2019
Type: article
Abstract: Receiving appropriate forecast accuracy is important in many countries’ economic activities, and developing effective and precise time series model is critical issue in tourism demand forecasting. In this paper, fuzzy rule-based system model for hotel occupancy forecasting is developed by analyzing 40 months’ time series data and applying fuzzy c-means clustering algorithm. Based on the values of root mean square error and mean absolute percentage error which are metrics for measuring forecast accuracy, it is defined that the model with 7 clusters and 4 inputs is the optimal forecasting model for hotel occupancy.
Source: Sustainability
DOI: 10.3390/su11030793
Authors: Rashad Aliyev, Sara Salehi, Rafig Aliyev
Institutions: Eastern Mediterranean University, University of Warwick
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,722
OpenAlex ID: W2914924619