<p>This paper explores the impact of various loss functions on the accuracy and robustness of advanced models in long-term time series forecasting, aiming to identify the most effective loss function for benchmarking purposes. The study focuses on four loss functions: Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Huber loss. Experiments are conducted using a fixed input horizon paired with four standard output horizons across six benchmark datasets, ensuring a comprehensive evaluation of model performance. To rigorously assess robustness, these experiments are performed using multiple random seeds, allowing for a thorough examination of model responses under different initial conditions. Recognizing that MSE has dominated as the default loss function in many latest architectures, this research seeks to investigate whether alternative loss functions can enhance model efficacy, particularly in terms of accuracy and stability. The choice of loss function is crucial, as it directly affects gradient updates and optimization behavior, thereby influencing model convergence and generalization. Through this investigation, valuable information is expected to emerge on the optimal selection of loss functions for long-term forecasting tasks, contributing to a deeper understanding of their role in enhancing model performance.</p>