TURİZM GELİRLERİNİN MODELLENMESİ VE TAHMİN EDİLMESİ: TÜRKİYE ÖRNEĞİ

Yıl 2024, Sayı: 69, 251 - 257, 30.12.2024

Begüm Kara Gülay

https://doi.org/10.18070/erciyesiibd.1581119

Öz

Bu çalışmanın amacı, ekonomik planlama ve kalkınma için önemli bir faktör olan Türkiye'nin turizm gelirini tahmin etmek için optimum tahmin modelini belirlemektir. Mevsimsel Otoregresif Entegre Hareketli Ortalama (SARIMA), Holt-Winters yöntemleri (eklemeli ve çarpımsal), Durum Uzay Modelleri (ETS), Yapay Sinir Ağları (YSA) ve mevsimsel eğilim ayrıştırma (STL) -ANN hibrit modeli dahil olmak üzere tahmin tekniklerini kullanmak ve bunların performansını değerlendirmektedir. Metodoloji, Ocak 2012'den Aralık 2023'e kadar aylık turizm geliri verilerinin analiz edilmesini ve YSA modeli için ziyaretçi sayıları, ekonomik güven endeksi, tüketici fiyat endeksi, endüstriyel üretim endeksi ve ABD doları döviz kuru gibi ek ekonomik göstergelerin dahil edilmesini içermektedir. Bulgular, özellikle turizm gelirini diğer ekonomik göstergelerle birlikte içeren model olan YSA'ların, en düşük Ortalama Mutlak Ölçekli Hata ve Kök Ortalama Karesel Ölçekli Hata ile geleneksel modellerden daha iyi performans gösterdiğini ortaya koymaktadır. Özellikle, ek öngörücülere sahip YSA modeli en yüksek tahmin doğruluğunu göstermiştir. Bu sonuçlar, gelişmiş makine öğrenme tekniklerinin geleneksel doğrusal modellere kıyasla üstün tahmin yetenekleri sağladığını göstermektedir. Çalışma, daha doğru tahminler için karmaşık modellerin entegre edilmesinin önemini vurgulayarak, turizm sektöründeki politika yapıcılar ve uygulayıcılar için değerli sonuçlar sunmaktadır.

Anahtar Kelimeler

Turizm Geliri, Öngörümleme, Yapay Sinir Ağları, Zaman Serisi Analizi

MODELING AND FORECASTING OF TOURISM INCOME: THE CASE OF TURKEY

Öz

This study aims to identify the optimal forecasting model for predicting Türkiye'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), the additive and multiplicative Holt-Winters methods, the 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, which serve as input for ANN models. The findings reveal that ANNs, particularly the model that incorporates 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.

Anahtar Kelimeler

Tourism Income, Forecasting, Artificial Neural Networks, Time Series Analysis

Kaynakça

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