Machine Learning, Deep Learning, Trading, Finance, Stock Market.
- epsilon) loss = K.square(K.log(y_true) - K.log(y_pred)) return K.mean(loss, axis=-1) model.compile(optimizer=opt, loss=mse_log) Result looks like: GOOGL volatility forecasting with log-MSE loss Metrics are MSE 2.52380132336e-05, MAE 0.0037 and MAPE 37%. Not that much, but already better! Some