(untitled) (Comprehensive Journal of Science, 2025)
This work discusses using Neural Ordinary Differential Equations (ODEs) for forecasting temperature values. We discuss the impact of learning rate reduction on performance in the model, and display impressive gains in training loss and test loss after the learning rate was reduced from 0.075 to 0.001. This study highlights the importance of learning rate tuning for better model generalization and more accurate predictions. We also provide a detailed description of the data preprocessing steps, the model architecture, and the results of the experiments, including comparisons of the performance before and after the adjustment.