1. Introduction

One of the most important problems in the sphere of economy and finance is the problem of forecasting economic and financial processes. The distinguishing properties of these processes are the following:


In this case the application of traditional methods of statistical analysis (e.g., regression analysis) is impossible, and it's necessary to apply methods based on computational intelligence (CI). To this class belongs group method of data

handling (GMDH) developed by Ivakhnenko [1, 2] and extended by his colleagues. GMDH method belongs to self-organizing methods and allows to discover hidden laws in the appropriate object area. The advantage of GMDH algorithms is the capability of constructing optimal models.

But classic GMDH has the following shortcomings:


Therefore in the last 10 years, the new variant of GMDH—fuzzy GMDH—was developed and extended which may work with fuzzy input data and is free of classical GMDH drawbacks [3–5].

Fuzzy GMDH is also based on the same principles as classical GMDH but construct fuzzy models.

The main goals of this paper are to investigate different modifications of FGMDH, analyze their properties, and investigate its efficiency as compared with classical GMDH in forecasting problems.
