How it Works
How SwingNN forecasts the future.
The future can never be forecasted with guaranteed accuracy but by using the SwingNN methods it is possible to achieve good results.
The SwingNN grid is produced by importing txt, csv, xls, bmp or binary files. The grid can also be produced manually using the editing facilities. Numeric, text and image columns can be used together in the same grid.
Only future numeric values can be forecasted. Text and image inputs will influence the forecasting of the numeric values.
A neural network is created from the grid data. Grid input columns are connected to network input nodes, grid output columns are connected to network output nodes and grid serial columns are connected to both network input and output nodes. At least one serial column is needed for forecasting.
The neural network is trained using the grid example rows. The risk level is set to zero.
After the neural network is trained, the serial input values are forced to swing beyond their limits. The serial output values are forecasted by the neural network. A new neural network is created and trained using the new inputs and forecasted outputs. The new neural network is compared with the original neural network. The inputs are adjusted, another new neural network is created and the risk level is incremented by one. The process continues until a new neural network agrees with the original one about the forecasts or the risk level is too high. The forecasts are added to the grid for you to use.