In order to optimize the balance between stationary and non-stationary information in time series forecasting, several strategies can be implemented. One approach is to fine-tune the parameter that controls this balance, such as the alpha value mentioned in the context. Conducting a thorough grid search or optimization process to determine the optimal value for this parameter based on specific dataset characteristics and modeling requirements can help achieve a better equilibrium between these two types of information. Additionally, incorporating adaptive mechanisms that dynamically adjust this balance during training based on model performance metrics or data patterns could enhance the flexibility and adaptability of the model.