How to calculate prediction error in a LSTM keras

Question

I have an LSTM which I have constructed and run in keras using python. I use this model to predict $n$ points into the future for a time series forecasting problem.

When I use a method such as ARIMA to make the forecast I am able to generate prediction errors for my predictions as the model is fit by minimising the MLE using AIC for example.

Is there a way that is currently supported in keras for me to generate prediction errors for my regression predictions? If there isn't, is there a way that I can calculate it myself?