Predicting future values in a multivariate time forecasting LSTM model

Question

I am confused on how to predict future results with a time series multivariate LSTM model.

I am trying to build a model for a stock market prediction and I have the following data features

Date DailyHighPrice DailyLowPrice Volume ClosePrice

If I train my model on 5 years of data up until today and I want to predict tomorrows ClosePrice, essentially I will need to predict all the data features for tomorrow. This is where I am confused.... Because if all the data features are dependent on one another how do i predict for one day in the future when all the data features for tomorrow are still unknown? Does anyone have any example code on how to deal with this issue?

Answer 1

The solution I decided to go with here is a TimeseriesGenerator from the keras library.

https://machinelearningmastery.com/how-to-use-the-timeseriesgenerator-for-time-series-forecasting-in-keras/