Group Method of Data Handling (GMDH) in Forecasting Electric Power Consumption

The group method of data handling is considered one of the most effective methods for solving the problem of supervised learning, while there are a few effective software libraries aimed at automating this method. The aim of the paper is to increase the efficiency of supervised learning problem solving by implementing a software library aimed at automating the group data handling method. The article analyses in detail the general approach, which is the basis of the group data handling method, as well as specific algorithms for implementing the method: COMBI, MULTI, MIA, RIA. The creation of an effective software library aimed at implementing the GMDH algorithms is considered. Experiments have been carried out for electric power consumption time series forecasting problem, showing the advantages of the developed library.