Predictive analysis of trade flows: A lever for optimizing financial performance and adapting international marketing strategies

This article presents a comprehensive analysis and forecast of Tunisia’s export flows in the mechanical and electrical sectors, focusing on improving predictive accuracy through time series modeling. The study began with the collection and preprocessing of historical export data, ensuring data quality and appropriate formatting for time series analysis. After exploring data to understand trends and seasonality, two classical forecasting models, ARIMA and Holt-Winters, were implemented. Due to the absence of actual export data for 2025, a model evaluation was carried out on the 2024 data, allowing an out-of-sample validation to objectively assess predictive performance. Metrics such as mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R²) were calculated to compare the precision of the models. The ARIMA model demonstrated superior performance with lower MAE and RMSE values and a better R² score, suggesting it is better suited for capturing the patterns in the export data. The article also discusses the challenges encountered, including data frequency issues and model parameter tuning. These findings provide actionable insights for stakeholders aiming to optimize export strategies and adapt to market fluctuations. The methodology and results lay the groundwork for future work integrating machine learning techniques to further enhance forecasting capabilities.