KALFormer: Knowledge-augmented attention learning for long-term time series forecasting with transformer
Fig 5
Accuracy and loss comparison in ablation experiments.
The figure reports trend-based accuracy (%) and loss (mean ± Std) for each model variant. KALFormer exhibits the highest accuracy (95.19%) and the lowest loss (0.1631), highlighting its predictive precision and training stability compared with other configurations.