About SwingNN
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.
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.
Previous Versions
Here you can find the changelog of SwingNN since it was posted on our website on 2015-04-27 03:00:00.
The latest version is 3.0 and it was updated on 2024-03-27 16:28:39. See below the changes in each version.
SwingNN version 3.0
Updated At: 2010-06-11
Changes: Bug fixes and performance improvements
SwingNN version 3.0
Updated At: 2009-01-04
Changes: Pattern detection and forecasting simplified.
Disclaimer
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