Kara Gülay B.
ERCIYES UNIVERSITY JOURNAL OF FACULTY OF ECONOMICS AND ADMINISTRATIVE SCIENCES, sa.69, ss.1-10, 2024 (TRDizin)
Özet
This study aims to identify the optimal forecasting model for predicting Turkey's tourism income, a crucial factor for economic planning and development. This study employs different forecasting techniques, including the seasonal Autoregressive Integrated Moving Average (SARIMA), additive and multiplicative Holt-Winters methods, Exponential Smoothing State Space (ETS), Artificial Neural Networks (ANNs) and seasonal-trend decomposition procedure based on the loess (STL)-ANN hybrid model and evaluates their performance. The methodology involves analyzing monthly tourism income data from January 2012 to December 2023, incorporating additional economic indicators such as the economic confidence index, number of visitors, consumer price index, industrial production index, and USD exchange rate for ANN models. The findings reveal that ANNs, particularly the model incorporating tourism income alongside other economic indicators, outperform traditional models with the lowest Mean Absolute Scaled Error (MASE) and Root Mean Squared Scaled Error (RMSSE). Specifically, the ANN model with additional predictors demonstrates the highest forecasting accuracy. These results suggest that advanced machine learning techniques provide superior predictive capabilities compared to traditional linear models. The study underscores the importance of integrating complex models to achieve more accurate forecasting, offering valuable insights for policymakers and practitioners in the tourism sector.