The tourism industry, characterized by its rapid and sustained growth, has progressively gained significance within global economies. The influx of international visitors has witnessed a remarkable escalation, transitioning from a mere 25.2 million in 1950 to 439 million in 1990, ultimately surging to an astonishing 1.4 billion in 2019 [1]. This exponential rise in foreign tourist arrivals bears substantial implications for economic landscapes, elevating the prominence of tourism as a pivotal contributor to national and international prosperity. Meeting the needs of these tourists requires not only expanding infrastructure, such as airports, transportation systems, and communication networks, but also establishing new businesses in tourism regions, including shopping centers, pharmacies, and car rental services. The consequential impact of these developments extends across various sectors, triggering a multiplier effect on economies and employment rates [2]. The World Travel and Tourism Council’s report highlights that prior to the COVID-19 pandemic, the tourism sector accounted for a substantial 10.4% of global GDP and constituted 10.6% of total employment [3]. However, the pandemic inflicted severe setbacks on the sector, resulting in an estimated loss of USD 4.5 trillion and a staggering 62 million job losses [4]. It is worth noting that incidents such as wars, policy issues, financial crises, and natural disasters exert significant influences on the tourism sector.

Against this backdrop, accurate and comprehensive forecasting of tourism demand assumes paramount importance in guiding countries’ strategic planning and decision-making processes [5,6]. Accurate forecasts facilitate decision-making for tourism and hospitality businesses, resource management, marketing and pricing strategies, and infrastructure development, among other critical areas [7,8,9,10]. For businesses, precise forecasting helps to mitigate unnecessary costs, such as unsold event tickets and unconsumed food, while aiding in resource allocation by analyzing priorities and potential risks [11,12]. Governments, on the other hand, can leverage forecasts to inform policies and precautions aimed at enhancing the tourism sector and optimizing income. Disregarding the significance of forecasting may lead to redundant investments, resource waste, and ineffective policy management.

Nonetheless, forecasting tourism data accurately and reliably poses considerable challenges due to the intricate interplay of multiple factors that influence tourism demand and income. These factors encompass a wide range of economic factors such as conflicts and wars between nations [14,15,16], and global factors exemplified by the profound impact of the COVID-19 pandemic [17,18,19].

Accurate tourism demand and income forecasts are critical for both the industry and the national economy. Since tourism forecasts are affected by several factors, especially the uncertainty of market conditions, the methods are diversified among studies. Hence, there is no unique forecasting method because of differences among countries, locations, and time intervals.

In the literature, while some studies use univariate time-series models [20,21,22] and econometric approaches [22,23,24], others use artificial intelligence-based models [25,26,27] and other methods.

Among the studies employing time-series methods, Chen et al. [28] analyze the forecasting of inbound tourism demand in Hong Kong using a multi-series structural time-series model and highlight its superior accuracy compared to ARIMA and exponential smoothing (ES) methods. Apergis et al. [29] examine the performance of four alternative univariate seasonal time-series forecasting models using data from 20 Croatian countries and conclude that the SARIMA model with Fourier transformation yields better results than the SARIMA, AR, and fractionally integrated ARMA models. Chu [20] assesses the reliability of forecasting by considering nine tourist destinations in the Asia-Pacific region, finding that the performance of the ARMA-based models utilized in the study is satisfactory.

Unlike univariate time-series models, econometric methods offer the advantage of comprehending the causal relationship between the dependent variable and explanatory variables [7]. Huang and Hao [30] present a novel two-step procedure involving a double-boosting algorithm and a support vector regression-based deep belief network (DBN) approach for tourism demand forecasting. The study demonstrates that this method significantly outperforms other models. Grey prediction models are used in some studies to characterize unknown systems with limited samples. Li et al. [31] examine international tourism demand using a linear almost ideal demand system (LAIDS) and conclude that the dynamic error correction LAIDS model provides better results than its static counterpart. De Mello and Fortuna [24] focus on the dynamic almost ideal demand system (DAIDS) for modeling tourism demand and highlight the advantageous and robust results of the dynamic model compared to static AIDS and other restricted models in terms of reconciling data and theory.

In recent years, the advancement of information technology has led to an increased use of artificial = intelligence-based models in tourism forecasting studies. Claveria et al. [32] compare the performance of a multi-layer perceptron, a radial basis function, and an Elman network as artificial neural network techniques for tourist demand forecasting and find that the multi-layer perceptron and radial basis function models yield better results than the Elman network. Artificial-intelligence-based models receive more attention due to their ability to explain non-linear relationships and patterns among time series [33]. Nguyen et al. [34] examine the tourism demand forecasting of Vietnam using an artificial neural network (ANN) and suggest that policymakers can implement this method for its accurate forecasting. Pai and Hong [25] assert that an improved neural network model for forecasting arrivals outperforms the ARIMA approaches.

Furthermore, some studies incorporate neural networks with adaptive neuro-fuzzy inference systems to overcome a large number of input variables for multivariate forecasting [35], while others prefer using ensemble deep-learning approaches to address challenges such as the curse of dimensionality and high model complexity [36]. He et al. [37] propose a new multiscale mode learning-based model for forecasting tourist arrivals by introducing mode decomposition models and the convolutional neural network model. Salamanis et al. [38] analyze tourism demand forecasting using the long short-term memory network, which allows for the incorporation of data from exogenous variables. The forecasting tourism demand study of Lin et al. [39] uses ARIMA, ANN, and multivariate adaptive regression splines (MARS) modelling approaches. The findings obtained indicate the outperformance of ARIMA. Zhang et al. [40] focus the issue by using seasonal and trend decomposition using a loess and duo attention deep learning model (STL-DADLM). They assert that the results of the proposed method are effective in terms of increasing accuracy for forecasting. Hassani et al. [41] analyze the results of singular spectrum analysis (SSA), ARIMA, exponential smoothing (ETS), and neural networks (NN), and the performance comparison shows the higher performance of SSA among other models. With the aim of obtaining a better performance, Cuhadar [42] compares exponential smoothing, Box–Jenkins, and an ANN model for Turkey’s tourism revenues forecasting and concludes that the ANN model outperforms the other models.

Apart from these methods, the grey model, particularly GM(1,1), is one of the most commonly used approaches for tourism demand forecasting. However, GM(1,1) faces stability issues in certain cases, despite its advantage of handling uncertain systems with limited samples and poor information. To address this, various optimization methods have been proposed, such as background value optimization [43,44,45], parameter optimization [46,47], accumulated generating operator optimization [48,49], and initial condition optimization [50,51,52]. Some studies adopt hybrid approaches that combine multiple methods to achieve more accurate results [53]. Ma [54] analyzes tourism demand forecasting using the grey model and BP neural network theory, while Hu [55] combines neural network-based interval grey prediction models and asserts their superiority over other interval models. Another study by Hu [56] employs fractional grey prediction models with Fourier series for tourism demand forecasting, highlighting their enhanced performance compared to other models.

Limited research exists in the literature focusing on tourism demand forecasting studies specifically for Turkey, despite Turkey being one of the countries with significant international shares, as per data from the World Trade Organization [13]. Among the notable studies conducted for Turkey, Soysal and Omurgonulsen [57] aimed to identify the most suitable model for tourism demand forecasting in Turkey based on the 2000–2007 period. Their comparative analysis revealed that Winter’s method outperformed moving average and exponential smoothing methods, providing better forecasting results. Onder and Hasgul [58] utilized Box–Jenkins models and artificial neural network models to forecast the number of foreign visitors between 2008 and 2010. The study suggested that while the artificial neural network model had a higher error value, it could serve as an alternative for forecasting. However, Winter’s method demonstrated a lower error value.

In another study, Cuhadar [59] focused on modeling and forecasting inbound tourism demand using data from the 1987–2012 period. The analysis employed feed-forward-back-propagation (MLP), radial basis function (RBF), and the time delay artificial neural network model (TDNN). The findings indicated that MLP yielded the most accurate results for demand forecasting. Karahan [60] evaluated the performance of artificial neural network methods by considering six independent variables and the 2010–2013 period. The results supported the higher predictability of using this method in the tourism sector.

Overall, it is evident that the number of tourism demand forecasting studies specific to Turkey is limited in the literature, despite Turkey’s significant international presence in the tourism market. The existing studies highlight the effectiveness of various methods, including Winter’s method and artificial neural network models, in forecasting tourism demand for Turkey. However, there is a need for further research and exploration in this area to enhance the understanding and accuracy of tourism demand forecasting in Turkey.