Improving the accuracy of neural network exchange rate forecasting using evolutionary modeling methods
DOI:
https://doi.org/10.20535/SRIT.2308-8893.2024.3.01Keywords:
exchange rate, genetic algorithm, evolutionary modeling, neural network, optimization, forecasting, accuracy, time seriesAbstract
A set of models of feedforward neural networks is created to obtain operational forecasts of the time series of the hryvnia/dollar exchange rate. It is shown that using an evolutionary algorithm for the total search of basic characteristics and a genetic algorithm for searching the values of the matrix of neural network weight coefficients allows optimizing the configuration and selecting the best neural network models according to various criteria of their training and testing quality. Based on the verification of forecasting results, it is established that the use of neural network models selected by the evolutionary modelling method increases the accuracy of forecasting the hryvnia/dollar exchange rate compared to neural network models created without the use of a genetic algorithm. The accuracy of the forecasting results is confirmed by the method of inverse verification using data from different retrospective periods of the time series using the criterion of the average absolute percentage error of the forecast.
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