Abstract:
The object of the study is a sample of prices of cryptocurrency "Litecoin" for each
day from 2017 to 2021.
The subject of research is the methods of data mining based on recurrent
neural networks and the method of GMDH. Python was chosen as the
programming language.
Litecoin cryptocurrency forecasting models are built on the basis of the latest
modifications of recurrent neural networks LSTM and GRU, such as bidirectional
LSTM and GRU, extended LSTM, dependent (dependent) Bi-RNN, these models
with the addition of attention mechanism attention-based mechanism), machine
learning methods, and the method of group argumentation (GMDH). A software
product for predicting the value of cryptocurrencies has been developed. Training of
models of recurrent neural networks is executed.
Experiments were conducted to select the parameters of these models. Using a
set of criteria, estimates of the quality of the constructed models and the obtained
forecasts are obtained. The results obtained by GMDH and models of recurrent
neural networks are compared.
When performing the work, two methods were established that give the best results that
are quite close to the real ones. The direction of development of work is in expansion of
functionality, reduction of errors of forecasting of the price and time of training of model.
