Why does ANNI require 675 entries to train on?
When working with Neural Nets it is important that you have sufficient entries for the Neural Nets to learn off of. The data that is learned is called the solution domain or in simple words, where the Neural Nets will perform best in. If a system is forced to predict outside of this range, extrapolation occurs and the accuracy of the result are severely restricted (since it’s predicting in an area it doesn’t have experience with). This problem is compounded when insufficient data is provided to the system since the system now becomes highly biased towards a narrow solution domain. Since ANNI also uses various indicators for inputs, up to 200 additional entries are required to buffer the calculation of these indicators (for a total of 675 entries). Therefore in order to protect a user from accidentally creating a system that is highly biased that can also be forced to extrapolate its predictions, a minimum amount of entries is required to train ANNI’s prediction system and Basic Trade S
