Table 4 Analysis of model prediction failures and proposed improvements.
Failure case | Identified cause | Proposed improvement |
|---|---|---|
High-magnitude errors | Underrepresented system states in training data | Data augmentation, rebalancing, and adaptive learning techniques |
Misclassification in boundary cases | Model struggles with transition states | Feature engineering to highlight boundary conditions |
Temporal drift in predictions | Accumulation of minor errors in sequential forecasting | Improved recurrent architectures (e.g., Transformer-based models) |
Sensitivity to sensor noise | Presence of faulty or noisy input data | Robust filtering techniques and anomaly detection |
Overfitting to common patterns | Model biases towards dominant trends in training data | Regularization techniques and hybrid statistical-domain models |
Uncertainty in rare event detection | Insufficient training examples for rare critical events | Uncertainty quantification and confidence interval estimation |
