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Table 2 Hyperparameters for the best models for predicting price direction DBMLA - Difference between Maximum and Lowest Accuracies

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

  LSTM CNN BiLSTM
# Layers 1 3 2
Layer Size 32 32 64
Batch Size 80 50 80
Dataset 1 day lag 1 day lag 1 day lag
Lagged Features 7 7 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 67.16% 64.18% 64.18%
Mean Accuracy 59.10% 58.51% 60.90%
DBMLA 14.93% 11.94% 7.46%