Application of High-Speed Algorithms for Training Neural Networks for Forecasting Financial Markets

Abstract

The article discusses the use of artificial neural networks in financial markets, due to the fact that this direction is very promising. Neural networks are well suited for tasks in which there are a large number of influencing factors, both known and unknown. The existing methods of neural networks training are considered, their pros and cons are noted. The use of neural networks in forecasting financial time series in real time is faced with the problem of the duration of the learning process of the neural network and the selection of significant inputs of the neural network. This problem can be solved by using the high-speed method of teaching the perceptron. The high-speed method of training allows for a much smaller number of iterations to train a multilayer perceptron on a given set of examples compared to the method of back propagation of the error. The high-speed method of training a multilayer perceptron allows us to assess whether it is possible to reach a given learning error of the neural network or not. This method of training is proposed to be used as an evaluation in the case of critical tasks before applying the method of back propagation of the error, in the case of non-critical tasks, the results of the neural network trained by this method can be used directly.