Method of Smoothing Seasonal Fluctuations of Tourist Activity Indicators in The Fergana Valley

Abstract

Seasonality persists as an enduring problem for the tourism industry because visitor flows in certain locations tend to vary dramatically throughout the year. Tourism enterprises throughout the Fergana Valley area of Uzbekistan face low tourist engagements between peak times that results in resource underuse and income instability. Actively existing seasonality management initiatives do not have sufficient analytical tools that specifically meet regional requirements. Research about seasonal changes in Fergana Valley tourist activity remains minimal because statistical models lack sufficient evidence in this area. Time-series analysis through moving averages and analytical smoothing functions will be used to model and identify seasonal patterns in tourist activity according to this research's main goal. The main goal involves extracting trend and seasonal patterns together with random disturbances which enables creation of evidence-based strategies to minimize seasonal variations. Research data supports July as the summit of annual tourism but most months create an expanded period of little tourism activity. The application of addition and multiplication patterns and index computations demonstrated that seasonal patterns changed their forms and intensity measurements periodically. Novelty: The paper provides an innovative methodological approach that links the well established tools of classical moving averages with the advanced analytical smoothing techniques to develop accurate trend models to be applied for seasonal forecasting in tourism. This research offers tourism businesses and policy makers operational frameworks for constructing substitute marketing plans together with strategies to boost sustainable tourism products throughout the entire year which leads to tourism economy stability in the region.