Selecting Neural Network Type

Selecting the correct neural network type

This is the image you will see when you start the wizard.

Walk-forward neural networks have no curve fitting issues unlike the standard training neural networks. This is because they retrain on each new bar and make a prediction before they retrain again on the next bar and make another prediction. However,  there is a downside in that they are slower because they are constantly training and predicting on each new bar. Also what they can learn from the data you feed them is limited by the number of periods in the look back. They are particularly useful for predicting rapidly changing markets.

Standard neural networks can train on massive amounts of data and thus learn complex relationships in the data and generalise what they learn. But unlike the walk-forward neural networks they can suffer from curve fitting and need to be trained first before they can be used and, may need retraining once in a while when more data is available. This type of neural network is by far the most common and versatile. If you are trying to create say a data smoother by predicting data that has been smoothed, this type of neural network would be the best choice. Also, if you are creating a system by predicting an optimal trading system based on say zigzag this type of neural network would also be the best choice.

Generally you should be selecting 'Standard Training Neural Network'