Your data is imported into a grid and used to train a neural network. The input values are forced to swing beyond their limits. The output values are forecasted by the neural network. A new neural network is created using the new inputs and forecasted outputs. The neural networks are compared. The inputs are adjusted and another new neural network is created. The process continues until a new neural network agrees with the original about the forecasts. The forecasts are added to the grid for you to use.
What's new in current version
Hidden Pattern forecasting added.
Performance improved.
Requirements
3 MBytes HD
Please contact SwingNN publisher, Neural Planner Software Ltd if you have questions or issues regarding this product.
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