An initial network is produced and trained using every row from the First to the Last. Another network is produced and trained using the rows from the Start and then with all the following rows up to the maximum. A copy of the row being forecast and a new row are added to the end of the grid. The new row has the forecasted values. This allows the forecasted values to be compared with the actual values. More than one forecast can be produced at every step. The process continues until the maximum steps is reached. When the process stops the grid is returned to its original state without removing the added rows. A secondary copy of the grid can be saved along with the secondary network.