Crypto Currency Price Forecast: Neural Network Perspectives (In Press)
a National University of Ostroh Academy, Ostroh, Ukraine

The study examines the problem of modeling and forecasting the price dynamics of crypto currencies. We use machine learning techniques to forecast the price of crypto currencies. The FB Prophet time series model and the LSTM recurrent neural network were selected to implement the study. Using the example of data from Binance (the most popular exchange in Ukraine) for the period from 06.07.2020 to 01.04.2023, prices for Bitcoin, Ethereum, Ripple, and Dogecoin were modeled and forecasted. The recurrent neural network of long-term memory showed significantly better results in forecasting according to the RMSE, MAE, and MAPE criteria, compared to the Naïve model, the traditional ARIMA model, and the FB Prophet results.

Full Text
Cite as: Kleban, Y., Stasiuk, T. (2022). Crypto Currency Price Forecast: Neural Network Perspectives (In Press). Visnyk of the National Bank of Ukraine, 254, .
Citation Format

Rights and Permissions
This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material.
Submit Your Paper