While error measures are important, they aren't the only factor in optimizing forecasting methods. Solely relying on metrics like MAE or MSE can sometimes overshadow other crucial aspects, such as understanding underlying patterns and trends. It's beneficial to combine these quantitative evaluations with qualitative insights and domain knowledge to ensure a more comprehensive and adaptable forecasting approach. This balance helps in capturing a fuller picture and making the models more resilient to unexpected changes.