lm16

# Calculate the predicted returns using the LSTM model
y_train_pred_returns <- model %>% predict(x_train)
y_test_pred_returns <- model %>% predict(x_test)
# Set up the layout of the plots
par(mfrow = c(1,2))
options(repr.plot.width=15, repr.plot.height=8)
# Plot the training and predicted values
plot(y_train, type = "l", col = "green",main="Apple daily Log Returns", xlab = "Day", ylab = "Returns",lwd =3)
lines(y_train_pred_returns, col = "red")
legend(x = "topleft", legend = c("Train", "Train Predictions"), col = c("green", "red"), lwd = 3)
grid()
# Plot the loss of training data
plot(history$metrics$loss, type = "l", xlab = "Epochs",main="Training Loss", ylab = "Loss", col = "blue", lwd =3)
grid()