Training Samples
Number of samples used for training. This box is informative only as the number of training samples depends on the parameters chosen to transform the time series such as Embedding Dimension, Delay Time, Prediction Mode, and the number of Testing Predictions.

Records in Time Series
This box is informative only and shows the number of records in the current time series.

Number of Chromosomes
Size of the population of models created each generation for breeding new ones.

Head Size
Size of the head of each gene. This parameter determines the upper limit for the size of the programs encoded in the gene.

Number of Genes
Number of genes per chromosome or, in other words, the number of complex terms in the evolving models.

Linking Function
Function used to link the different terms encoded in the genes of a model. You can choose either addition, subtraction, multiplication, or division to link the sub-ETs of your models.

Embedding Dimension
The embedding dimension used to transform the time series for training. It corresponds to the number of independent variables after the transformation of the time series.

Delay Time
The value of the delay time t, which determines how data are processed, that is, continuously if t = 1 or at t intervals.

Prediction Mode
The two kinds of recursive predictions of APS. The first one – Prediction – is used for making predictions about the future. The second – Testing – is used for evaluating the predictive power of the evolved models on past known events. In both cases, predictions are made recursively, by evaluating the forecast at t+1, then using it to forecast t+2, and so on.

Testing Predictions
This box becomes active when Testing is selected in the Prediction Mode combo box. Here you must enter the number of samples you want to use for the recursive test.

Enable Complexity Increase
Check this box if you wish to allow APS to automatically increase the number of genes in the evolving models.

Generations without Change
The period of time you think acceptable for evolution to occur without improvement in best fitness, after which a mass extinction is created or a neutral gene (an extra term) is automatically added to all evolving models.

Number of Tries
The number of consecutive evolutionary epochs (defined by the parameter Generations Without Change) you will allow before a neutral gene is introduced in all evolving models.

Max. Complexity
The maximum number of terms (genes) you think reasonable for modeling your data. Beyond this threshold no other terms are introduced automatically by the Complexity Increase Engine and APS stops evolving new models.