## 3 Materials and Methods  
In general, forecasts represent informed assumptions of future activity. They depend on detailed analysis and precise assessment of historical traffic demand trends, projection of economic growth and other relevant factors that might impact the local air transport market growth. A detailed and accurate analysis ensures more precise forecasting, particularly for short-term periods. Forecasts developed for medium or long-term periods provide airport decision makers with important guidance, for example, the requirements of new or expanded infrastructure, etc.  
Insights into future air traffic activities (forecasts) serve as the basis for effective terminal design decisions, i.e., they are useful in determining the necessity for new or expanded terminal facilities.  
The traffic demand forecast should include passenger traffic as well as aircraft operations. Forecasts of passenger activity and aircraft operations are usually developed on an annual and peak basis to ensure that future facilities are planned to meet demand levels even at peak times.  
Examples of forecasting methods being used can be found in various aviation sectors such as air navigation services, airline operations, and airport operations. Available examples of forecasting methods used in various aviation sectors were examined in [43; 44; 45; 46; 47; 48; 49], and a detailed mathematical elaboration of each forecasting method is available in [50; 51].  
Forecasting methods used in airport operations were examined and tested to determine the optimal forecasting method for this research. The most common forecasting methods used in airport operations are the Naive Forecasting Method, Simple Average Method, Simple Moving Average Method, and Exponential Smoothing Methods (including Simple Exponential Smoothing Method, Holt's Exponential Smoothing Method, and Holt-Winters Exponential Smoothing Method). These methods were considered for selection. Other forecasting methods (ARIMA modeling, machine learning (ML) forecasting models, deep learning (DL) forecasting models, artificial neuron network (ANN) forecasting models, etc.) were not considered due to some key shortcomings, such as limited ability to capture nonlinear patterns, problems with complex seasonal trends, necessity for large amounts of high-quality data, potential loss of valuable information due to necessary data transformations, necessity for significant computer processing power and data training time, etc.  
Statistical data on the passenger traffic at a sample airport (Franjo Tudman Airport) in the period from January 2006 to December 2021 were used as a dataset for all forecasting examples, applying various forecasting methods. Python forecasting tools and MS Excel tools were utilized for analysis and comparison of the results. The most suitable forecasting method was selected and applied in this research. The forecasting method applied in this research was selected according to its applicability to the case at hand, i.e., Pula Airport, which records show seasonality (airport is the busiest during the tourist season).  
Franjo Tudman Airport was taken for the analysis of different forecasting methods as an example of one airport in Croatia. Croatia is known as a tourist destination, so the demand for air traffic increases significantly during the summer months. In Croatia, all airports, from the aspect of traffic demand, have similar characteristics--strong seasonality, dominance of international traffic, dominance of passenger traffic over cargo traffic, dependence on charter and seasonal lines, etc. Considering that Franjo Tudman Airport is the largest air transport hub in the country and a key point for international and domestic flights, it records the largest number of passengers and flights and has significant seasonality; it was chosen as an example for the analysis and selection of an appropriate (optimal) method of traffic demand forecasting at airports. This method was applied to forecast traffic demand and airline business model shares at Pula Airport.