Statistical parameter estimation and probabilistic forecasting of cargo turnover at seaports grouped by sea basin using predictive models

The article presents the results of an analysis of cargo turnover in five sea basins of the Russian Federation for 2021–2024. The analysis of port and sea basin operations, carried out on the basis of available statistical data, makes it possible to identify trends in their future development, which depend on both coastal shipping and foreign economic relations. Forecasting cargo turnover using existing methods enables sea basins and individual ports to optimise cargo-handling operations, plan organisational and technical measures for the operation and maintenance of lifting and handling equipment, coordinate cargo flow directions, and determine the amount of cargo to be stored in warehouses. Managing these indicators makes it possible to plan the utilisation of warehouse areas at seaports, prepare vessel consignments in a timely manner, and avoid vessel downtime during cargo operations. The analysis and forecasting of various types of economic activity are carried out using expert assessment and various mathematical methods, which are probabilistic in nature. This study applies the Holt-Winters method, which provided the most accurate forecast based on the results of the analysis. Following the cargo turnover forecast for 2025 obtained using the Holt-Winters method, the predicted values were compared with the actual data. To verify the degree of agreement between the forecast and the actual values, an additional calculation was performed using a built-in Microsoft Excel function. The application of these forecasting methods makes it possible to analyse cargo turnover in sea basins in order to mitigate potential conflicts during cargo transshipment, support prompt decision-making, and ensure the rational use of port infrastructure within each sea basin. The comparison of the methods considered shows that the cargo turnover values obtained using these methods are close to the actual statistical data.

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