Development of Fuzzy Time Series Model for Hotel Occupancy Forecasting

Rashad Aliyev,Rafig Aliyev,Sara Salehi

doi:10.3390/su11030793

Rashad Aliyev, Rafig Aliyev + Show 1 more

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https://doi.org/10.3390/su11030793

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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.

Highlights

Tourism is mentioned as one of the most significant economic fields over the last two decades: its ranking in world trade is second only to oil, which sets it apart from other economic fields
By combining linear autoregressive integrated moving average (ARIMA) model and nonlinear artificial neural network model, an effective hybrid methodology for time series forecasting is proposed in [1], and the experimental results show that the combined approach takes the advantages of both models and significantly improves the forecasting performance
This paper aims to develop fuzzy time series model for forecasting an occupancy of one of the hotels of North Cyprus

Summary

Introduction

Tourism is mentioned as one of the most significant economic fields over the last two decades: its ranking in world trade is second only to oil, which sets it apart from other economic fields. Nowadays, making a reasonable decision, especially under uncertain circumstances, is necessary in the field of tourism. Promotion in the tourism sector would be simpler if it were possible to forecast changes in number of tourists by examining current and past tourism demands. Due to the competitive and complicated environment in the tourism sector, it is required to observe and to enhance the previous standard of performances. In the prediction of tourism demand, the importance of accuracy is undeniable; selecting a suitable model to fit a problem is as necessary as accuracy of the results. The different tourism forecasting models have been suggested by researchers, and each model has superiorities as well as drawbacks

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