Comparison of the accuracy of forecasts bsed on neural networks before and after the outbreak of the COVID-19 pandemic on the example of selected exchange rates

Abstract

This article examines the impact of the COVID-19 pandemic on the accuracy of forecasts for three currency pairs before and after its outbreak based on neural networks (ELM, MLP and LSTM) in terms of three factors: the forecast horizon, hyper parameterisation and network type.