Using ARIMA model to analyse and predict bitcoin price

Abstract

In this paper, Autoregressive Integrated Moving Average (ARIMA) model is used for analysing and forecasting the adjusted closing price of bitcoin. The whole dataset used is daily bitcoin closing price dating from Jan 2017 to Sep 2022. However, for testing the performance of the ARIMA model, the dataset is divided into two parts: the ARIMA model is built on training set and later using the test set to check the accuracy of the prediction. Two models, namely ARIMA (5, 2, 1) and ARIMA (0, 2, 2) are selected and comparations between them are made. ARIMA (5, 2, 1) is chosen with stepwise selection and approximated information criteria while ARIMA (0, 2, 2) is without stepwise selection and the information criteria is not approximated. Both pass the residual test and ARIMA (0, 2, 2) is slightly better according to AIC, AICc and BIC. Later, the predicting accuracy of the two models for different forecasting periods (5-day, 10-day, and long-term forecasting) are compared. It is not surprising that ARIMA performs better while making short term prediction. The 5-day and 10-day forecast works well while the long-term forecast is of limited practical value.