Forecasting time series outside the training/test set
Let's say you trained a forecasting model using the following base data:

time       | index_1 | index_2 | label
01/01/2018 | 80      | 70      | 1
01/02/2018 | 60      | 30      | 0
01/03/2018 | 75      | 90      | 1


You used time, index_1 and index_2 to predict label. Then you would simply need a dataset like this to predict 01/04/2018:

time       | index_1 | index_2
01/04/2018 | 60      | 75      


Using your model on this data set should predict the label-value.

Now in a time series this can be more complicated, let's say what you actually want to predict is the label-value of time X from the indicie values of time X-4 months. In this case your data to build the model should look like this:

time       | index_1_lag_4_months | index_2_lag_4_months | label
01/04/2018 | 80                   | 70                   | 1
01/05/2018 | 60                   | 30                   | 0
01/06/2018 | 75                   | 90                   | 1


This model would predict the label-value for 01/04/2018 based on the indice values of 01/01/2018. To actually get a prediction we again give a data set like this:

time       | index_1 | index_2
01/04/2018 | 60      | 75      


Only the output would not be the label-value for 01/04/2018 but instead for 01/08/2018.