Crypto Currency Price Forecast: Neural Network Perspectives (In Press)

Kleban, Yuriy; Stasiuk, Tetiana

doi:10.26531/vnbu2022.254.03

Yuriy Kleban a

Tetiana Stasiuk a

a National University of Ostroh Academy, Ostroh, Ukraine

Abstract

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.

DOI

https://doi.org/10.26531/vnbu2022.254.03

JEL Codes

C45, C53, G11

Keywords

forecasting neural networks crypto currency time series

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Cite as: Kleban, Y., Stasiuk, T. (2022). Crypto Currency Price Forecast: Neural Network Perspectives (In Press). Visnyk of the National Bank of Ukraine, 254, . https://doi.org/10.26531/vnbu2022.254.03

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