Investigation of computational intelligence methods in forecasting at financial markets

Abstract

The work considers intelligent methods for solving the problem of short- and middle-term forecasting in the financial sphere. LSTM DL networks, GMDH, and hybrid GMDH-neo-fuzzy networks were studied. Neo-fuzzy neurons were chosen as nodes of the hybrid network, which allows to reduce computational costs. The optimal network parameters were found. The synthesis of the optimal structure of hybrid networks was performed. Experimental studies of LSTM, GMDH, and hybrid GMDH-neo-fuzzy networks with optimal parameters for short- and middle-term forecasting have been conducted. The accuracy of the obtained experimental predictions is compared. The forecasting intervals for which the application of the researched artificial intelligence methods is the most expedient have been determined.

Keywords

• optimization
• gmdh
• hybrid gmdh-neo-fuzzy network
• lstm
• short- and middle-term forecasting