A Mathematical and Statistical Framework for Analyzing Retail Import Patterns and Distance Sales in the European Union

This study examines statistical approaches for analyzing retail import dynamics and distance sales within the European Union (EU) and the European Economic Area (EEA), respectively. Using Eurostat data from 2014 to 2023, we first explore intra‐EU imports, which dominate retail imports across member states, and employ time series clustering to identify common import patterns. Linear models (LM) are then proposed at the group level to capture effects, revealing heterogeneous growth rates across clusters—most notably Germany and France. For quarterly retail distance sales (2013–2025), we analyze seasonal and trend components using regression models and Holt–Winters exponential smoothing. The Holt–Winters approach generally provides superior predictive performance and more reliable residual behavior. Finally, we discuss future directions, emphasizing the role of artificial intelligence, machine learning, and real‐time monitoring in strengthening retail forecasting and supply chain resilience within Europe.