International Congress on Eurasian Economies
26-28 June 2024 – Bishkek, KYRGYZSTAN
Paper properties
Paper ID : 2888
Status : Paper accepted
Language : English
Topic : Sectoral Analyses
Presenter: Assoc. Prof. Dr. Tuğba Sarı
Session : 6A Microeconomics
Artificial Neural Networks Enhanced Demand Forecasting in Tourism Industry
Artificial Neural Networks Enhanced Demand Forecasting in Tourism Industry
- Assoc. Prof. Dr. Tuğba Sarı (Konya Food and Agriculture University, Türkiye)
- Asst. Prof. Dr. Seyil Najimudinova (Kyrgyz-Turkish Manas University, Kyrgyzstan)
- Prof. Dr. Cengiz Yılmaz (Konya Food and Agriculture University, Türkiye)
Abstract
Tourism is a dynamic business industry that plays an important role in economic growth, infrastructure development and cultural interaction. Tourism makes a significant contribution on both a global and national scale, generating income, creating jobs, and thus stimulating economic growth. However, the tourism industry can be exposed to various risks, ranging from natural disasters to pandemics, political events to economic uncertainties. Therefore, accurate forecasting of tourism demand is of great importance for both the sustainability of the industry and national economies. Accordingly, this study aims to accurately forecast the number of tourist arrivals to Kyrgyzstan and Türkiye. In the study, the data of tourists entering Kyrgyzstan and Türkiye between 2003 and 2022 are addressed. The number of tourist arrivals to both countries is obtained from the official websites of the statistical agencies of the respective countries and subjected to a series of analyses. The tourist data are first analyzed with ARIMA, Holt exponential smoothing and Gray forecasting methods, and then these methods are enhanced with artificial neural networks to improve the forecasting results. The results of the analysis performed with various methods were compared and the most accurate forecasting methods were determined. Thus, the prediction of the number of tourists arriving in Kyrgyzstan and Türkiye in the coming years was made using the most accurate methods determined in the previous step. The findings of the study are expected to help tourism industry managers and policy makers in planning of labor force, financial investments and other resources.
JEL codes: C45, E17, Z31
