Hybrid GMDH deep learning networks – analysis, optimization and applications in forecasting at financial sphere

Abstract

In this paper, the new class of deep learning (DL) neural networks is considered and investigated — so-called hybrid DL networks based on self-organization method Group Method of Data Handling (GDMH). The application of GMDH enables not only to train neural weights, but also to construct the network structure as well. Different elementary neurons with two inputs may be used as nodes of this structure. So the advantage of such a structure is the small number of tuning parameters. In this paper, the optimization of parameters and the structure of hybrid neo-fuzzy networks was performed. The application of hybrid Dl networks for forecasting market indices was considered with various forecasting intervals: one day, one week, and one month. The experimental investigations of hybrid GMDH neo-fuzzy networks were carried out and comparison of its efficiency with FNN ANFIS in the forecasting problem was performed which enabled to estimate their efficiency and advantages.

Keywords

• hybrid deep learning networks
• self-organization
• parameters and structure optimization
• forecasting