Fig 1.
Proposed Methodology.
Fig 2.
Distribution of export values visualized with and without log scale.
Left: original scale; Right: log scale.
Fig 3.
Exploring relationships between export value and key features.
The figure shows how export value relates to important variables in the dataset.
Fig 4.
Average export value trends across feature levels, illustrating how export values vary with key features in the dataset.
Fig 5.
Feature importance ranking using the Random Forest Regressor for predicting export values.
The plot shows which features contributed most to the model’s predictions.
Fig 6.
Graph structure and connectivity analysis of the country trade network.
Fig 7.
Proposed Graph Neural Network (GNN) architecture.
Fig 8.
Learning curves of the proposed model: (a) training and validation loss (MSE), (b) validation RMSE, and (c) validation MAE over 250 epochs.
Fig 9.
Training behavior of the proposed GNN model for trade balance forecasting: (left) validation R2 score across epochs, achieving a stable value of approximately 0.91, and (right) step-wise learning rate decay used during training.
Table 1.
Performance comparison of the proposed model against baseline methods.
Fig 10.
Residual distribution analysis of the proposed GNN model for trade balance forecasting.
The top row shows predicted versus actual trade balance for the training, validation, and test sets. The middle row presents residual plots, while the bottom row illustrates the residual error distributions along with MAE values for each dataset.
Table 2.
Paired t-test results comparing the proposed GNN model with baseline methods.
Table 3.
Wilcoxon signed-rank test results comparing the proposed GNN model with baseline methods.
Table 4.
Sensitivity analysis results of the proposed GNN model across baseline, geographical, and temporal settings.
Table 5.
Top 20 features ranked by mean absolute SHAP values.
Fig 11.
SHAP summary plot showing the global feature importance and contribution patterns for trade balance prediction using the proposed GNN model.