Neural Network Architecture Development for Time Series Forecasting

The paper reveals the results of studies on the development of neural network architecture for time series forecasting. Three types of neural networks were investigated: radial network, convolutional network, and multilayer perceptron. The main objective of this study is to identify, based on a comparative analysis, the architecture of a neural network, which is most suitable for the specifics of forecasting data of a financial nature. In the course of work, from the more than 1000 configurations of neural networks, the most successful ones were selected, with the smallest standard error. The C# programming language and the Visual Studio development environment were chosen in the capacity of the implementation tools. In the work, the back propagation of error algorithm was used as a method for training the neural network. During the training of the network, a dynamic change in the network configuration was carried out. The time series of the financial market were used as the data supplied to the neural network. The work was carried out on the data of exchange rates for 2000-2018. As a result of the study, it was determined that the use of a multilayer perceptron provides higher accuracy. The results of the work can be applied to forecast the values of securities, currency pairs and other financial instruments.