Development of Fuzzy Time Series Model for Hotel Occupancy Forecasting

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...

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Bibliographic Details
Published in:Sustainability 2019-02, Vol.11 (3), p.793
Main Authors: Aliyev, Rashad, Salehi, Sara, Aliyev, Rafig
Format: Article
Language:English
Subjects:
Accuracy
Back propagation
Clustering
Economic forecasting
Error analysis
Forecasting
Fuzzy logic
Fuzzy sets
Genetic algorithms
Linguistics
Model accuracy
Neural networks
Occupancy
Probability distribution
Sustainability
Time series
Tourism
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Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su11030793