Investigation of the effectiveness of artificial neural networks of different generations in the task of forecasting in the financial sphere

This paper discusses ANNs of different generations. The efficiency of using computational intelligence in the task of short- and medium-term forecasting in the financial sphere is investigated. For the investigation, a fully connected feed-forward network (Back Propagation), a recurrent network (LSTM), a hybrid deep learning network based on self-organization (GMDH neo fuzzy), and a hybrid system of computational intelligence based on bagging and group method of data handling (HSCI bagging) were chosen. The experimental parameters chosen are the prediction interval, the number of inputs, the percentage of validation data in the training set, and the number of fuzzifiers (for GMDH neo-fuzzy). Experiments were conducted, and the best results for different prediction intervals were compared. The optimal parameters of the networks and the feasibility of their use in the task of forecasting at different intervals are determined.