Construction of confidence interval for a univariate stock price signal predicted through Long Short Term Memory Network

Any forecasting or an estimation technique used for the prediction of a signal always leads to some level of random variations in the values of the prediction at each time point. A statistical way to address such variation is by providing a confidence band of that predicted values. All statistical signal processing techniques address the issue while making their prediction. Machine learning approaches do not have any well-studied framework to address this critical problem. One of the reasons is such construction of confidence interval requires appropriate modeling of signal noise or asymptotic properties of the estimated parameters.