Forecasting tourism demand is a crucial component of tourism marketing because it enables destinations, governments, and private enterprises to anticipate future visitor flows, allocate resources efficiently, design promotional strategies, and reduce risks associated with seasonality and uncertainty. Tourism demand forecasting involves estimating the number of tourists who will travel to a destination within a specific time frame and the associated expenditure patterns. Various quantitative and qualitative methods are used to forecast tourism demand, each with its own strengths, limitations, and suitability depending on data availability and forecasting objectives. One of the most widely used methods in tourism demand forecasting is the time series method. Time series analysis relies on historical data to identify patterns such as trends, seasonality, cyclical movements, and irregular fluctuations. By examining past tourist arrivals, overnight stays, or tourism receipts, analysts can project future demand assuming that past patterns will continue. Techniques such as moving averages, exponential smoothing, and decomposition models are commonly applied in this approach. Seasonal adjustment is particularly important in tourism because demand often fluctuates significantly across months or seasons. Time series methods are relatively simple to apply and useful for short-term forecasting, but they may not adequately capture sudden structural changes, such as economic crises, pandemics, or political instability. Another important quantitative method is econometric modeling. Econometric models establish relationships between tourism demand and its determinants, such as income levels, exchange rates, travel costs, relative prices, marketing expenditure, and political stability. Regression analysis is commonly used to estimate how changes in these variables affect tourist arrivals. For example, an increase in disposable income in a source market may lead to higher outbound travel