Example Concept: To enhance RNN performance for time-series prediction, consider using advanced architectures like LSTM or GRU, which handle long-term dependencies better than vanilla RNNs. Additionally, optimize hyperparameters such as learning rate, batch size, and sequence length through techniques like grid search or random search. Preprocessing data by normalizing or standardizing it, and using techniques like data augmentation or feature engineering, can also significantly improve model accuracy and robustness.