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Table 4 Hyperparameters for the best models for predicting magnitude of change Acc. F1 Score - The sum of the individual F1 Scores of each label/class

From: Bitcoin price change and trend prediction through twitter sentiment and data volume

  LSTM CNN BiLSTM
# Layers 3 2 2
Layer Size 256 128 256
Batch Size 50 80 80
Dataset 1 day lag 3 day lag 1 day lag
Lagged Features 7 3 7
Train-Test Split 85:15 85:15 85:15
Loss Function Categorical Crossentropy Categorical Crossentropy Categorical Crossentropy
Early Stopping Parameter Validation Loss Validation Loss Validation Loss
Early Stopping Patience 20 20 20
Maximum Accuracy 58.21% 57.35% 59.09%
Mean Accuracy 46.76% 51.47% 46.67%
Acc. F1 Score 12.33% 14.21% 12.88%