Discussing the article: "The Group Method of Data Handling: Implementing the Combinatorial Algorithm in MQL5"
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Check out the new article: The Group Method of Data Handling: Implementing the Combinatorial Algorithm in MQL5.
In this article we continue our exploration of the Group Method of Data Handling family of algorithms, with the implementation of the Combinatorial Algorithm along with its refined incarnation, the Combinatorial Selective Algorithm in MQL5.
The combinatorial algorithm of the GMDH, often referred to as COMBI, is the basic form of GMDH and serves as a foundation for more complex algorithms within the family. Just like Multilayered Iterative Algorithm (MIA), it operates on an input data sample represented as a matrix containing observations over a set of variables. The data sample is divided into two parts: a training sample and a test sample. The training subsample is used to estimate the coefficients of the polynomial, while the test subsample is used to select the structure of the optimal model based on the minimal value of the selected criterion. In this article we will describe the computation of the COMBI algorithm. As well as present its implementation in MQL5 by extending the "GmdhModel" class described in the previous article. Later on we will also discuss the closely related Combinatorial Selective aglorithm and its MQL5 implementation.
Author: Francis Dube