Bitcoin Price Forecasting using Time-series Architectures

The cryptocurrency market has shown an imperfect market property due to volatility and erratic behavior. Cryptocurrencies“ prices largely fluctuate because of the 24/7 trading policy that weakens the forecasting power of thetraditional models. However, research works reveal that cryptocurrency prices cannot be truly random in nature. In thispaper, traditional methods of time-series forecasting such as the ARIMA and SARIMA were presented and compared with the recent deep learning models such as the GRU, RNN, LSTM, and Facebook Prophet. Historical data for Bitcoin was acquired consisting of 2770 samples from 2018 to 2022. The study yielded the lowest MSE and RMSE of 2159166.25 and 1469.41 respectively for the GRU model. FB Prophet yielded the highest RMSE and MSE among all the experimented models.