TCN models were trained based on the different feature groups obtained from the feature selection step for comparison. Grid search was used to find the best values of the hyperparameters, including learning rate, number of epochs, number of TCN layers, batch size, window size, number of filters, kernel size, dilation, and dropout rate. Each model was optimized using the Adam optimizer, while minimizing the mean squared error (MSE), with He normal as the kernel initializer [29].

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