Neural networks for bitcoin price forecasting

Abstract

This paper concerns the problem of daily Bitcoin price prediction, aiming to find the best predictive model among the linear and nonlinear forecasting models. Finding the most accurate forecasting model would help investors take important decisions about taking the next step when investing. We compare the forecasting performance of linear and nonlinear forecasting models using daily Bitcoin price data for the period between 31 December 2017 until 24 November 2021. We discuss various forecasting approaches, including an Autoregressive Integrated Moving Average (ARIMA) model, a Nonlinear Autoregressive Neural Network (NARNN) model, a TBATS model and Exponential Smoothing on the data collected from 31 December 2017 to 24 November 2021 and compared their accuracy using the data collected from 01 June 2021 to 09 June 2021, choosing the model with the lowest Mean Absolute Percentage Error (MAPE) value. The chosen model has been used for daily Bitcoin price forecasting for the next 30 days without any additional intervention. The forecasting model can be applied to other cryptocurrencies available on the global cryptocurrency market cap.