Investigation of recurrent networks LSTM in the problem of Covid-19 forecasting

in this paper problem of Covid-19 forecasting was considered and investigated. Review of different models and methods of pandemic forecasting are presented. For middle term forecasting indicators of Covid-19 the application of LSTM networks is suggested. The experimental investigations were carried out during which the optimal parameters LSTM network were found: sliding window size, forecasting interval and network architecture. The efficiency of LSTM in Covid-19 forecasting was estimated. © 2021 IEEE.

Investigation of hybrid neo-fuzzy neural networks in the problem of pandemic forecasting

The problem of covid-19 forecasting was considered and investigated. Review of different models and methods of pandemic forecasting are presented. For short-term forecasting indicators of covid-19 the application new class neural networks – hybrid neo-fuzzy networks based on GMDH is suggested. The application of GMDH enables to construct the structure of hybrid network and accelerate the speed of learning neural weights. The experimental investigations were carried out during which the optimal parameters of hybrid network were found sliding window size, forecasting interval and network architecture. The efficiency of hybrid neo-fuzzy network in the pandemic forecasting problem was estimated and compared with Back Propagation neural network. © 2021 Copyright for this paper by its authors.