@dorianer

A spatiotemporal hybrid neural network predicts liquid staking exit queue waiting times by combining LSTM and convolutional layers. The model processes validator performance metrics and network congestion data, achieving 92% accuracy in 15-minute forecasts. Compared to ARIMA models, it reduces prediction error by 41% during high-demand periods, enabling better user decision-making.

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