Online Tutorial - Current Solution's Details
When selected, shows the current solutionís statistics.
|Input Period (days):||The number of days or bars the current solution
utilizes as a single input.
|Complexity Level:||The complexity level of the current solution.
This is an important value because it holds significance in the
generalization capability of the solution. The lower the level, the more
generalized the solutionís outputs are. The higher the level, the more
precise the solutionís outputs are. The difficulty is finding the right
balance between these two. Too much precision sacrifices generalization,
which tends to make the solutionís predicted outputs unreliable. And to much
generalization will make the solutionís predicted outputs inaccurate. The
genetic algorithms used to optimize this value, attempts to achieve the
perfect balance between these two states.
|Value Difference:||The actual difference in percentage between
actual close values and all
predicted evaluation values.
|Correlation %:||The correlation percentage ( a statistical
measure of similarity ) of all predicted evaluation values vs. actual
|Survived Generations:||When a solution has passed through one
generation without being eliminated it has survived a generation. As the
system converges, good performing solutions will have survived more
generations than poorer performing solutions. This shows the number of
survived generations for the given solution. This value will be 0 for
solutions that have not been trained yet and are brand new for the
|Comparison to Average:||This shows how well this solution
compares to the populations average.
|Error Graph:||The lower graph tracks three values:
Professional Only Features
The professional version allows viewing of the internal "Walk Forward" test
period by right clicking while on the graph and selecting the "Walk Forward"
option. This is a special test that truly shows if the solution has achieved success
and is heavily used in Genetic Optimization of the system. It uses the same
algorithm in the prediction period where the outputs are wrapped back into the
inputs in order to walk-forward (so to speak) into future predicted values. This
is how ANNI can produce up to 90 days of future predictions. By using this same
algorithm in the evaluation period without the use of actual values, it provides
an incredibly rigorous test to determine how well the solution can predict several
bars forward by comparing itís results to the actual values. This walk forward period is half of the evaluation period, which
can easily be over 30+ bars. For a solution to successfully see the inherent trend
over a 30+ bar period without relying on any actual values is astounding.
Professional users have the option of viewing this walk-forward period in the
output graph for detailed analysis of the best solutions. When active, an
additional red line will appear marking the beginning of the internal
Caution: For optimum training speed, do not keep this option selected. It is a CPU intensive computation and should be used for quick evaluation only.
What Are Neural Nets