Original scientific paper

https://doi.org/10.22598/at/2017.29.2.157

A HYBRID INTELLIGENT MODEL FOR TOURISM DEMAND FORECASTING

Anurag Kulshrestha ; Indian Institute of Management Indore, Mumbai Campus, Mumbai, India
Abhishek Kulshrestha ; Shri Ramswaroop Memorial University, Lucknow, India
Shikha Suman ; Indian Institute of Information Technology, Allahabad, India

Abstract

The ever increasing demand of the tourism sector worldwide has led to an increase in tourism demand forecasting methodologies. New techniques yield much reliable predictions of tourist arrivals for better economic planning. The study aims to forecast and compare the performance of two non-linear artificial intelligence approaches in predicting the number of tourist arrivals to Singapore. The Singapore inbound monthly tourism data were utilized to generate one, two, four and six month ahead forecasts with non-linear autoregressive (NAR) neural networks and neuro-fuzzy systems. The predictive accuracy of NAR neural networks and neuro-fuzzy systems were compared with various performance metrics. The study revealed that neuro-fuzzy systems outperformed NAR networks in all forecasting horizons and for all countries. The proposed neuro-fuzzy methodology helps in improving the forecasting performance of artificial intelligence based techniques. The study contributes to hospitality literature and could be utilized by managers to effectively plan and implement tourism related policy measures.

Keywords

tourism demand; forecasting; non linear autoregressive neural network; adaptive neuro-fuzzy inference system

Hrčak ID:

192565

URI

https://hrcak.srce.hr/192565

Publication date:

1.12.2017.

Article data in other languages: croatian

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