Comparative Analysis of ARIMA and Prophet Algorithms in Bitcoin Price Forecasting

Abstract

The purpose of this research is to compare ARIMA and Prophet algorithms and find the best algorithm for forecasting bitcoin prices. The dataset is two years historical bitcoin data between February 2019 and 2021. The data is segmented into daily, weekly, and monthly period category. Both algorithms are built into a univariate model that only receive 2 features for training the model. Several ARIMA models is developed for each dataset interval. After that, the parameter of each model will be cross-referenced to each other to obtain the best parameter combination. Meanwhile, Prophet model will be developed using automatic and manual tuning. Then again parameter value of each model will be cross-referenced to each other to obtain the best parameter combination. Evaluation of the training model is done by calculating the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and Rooted Mean Squared Error (RMSE). The results showed that the best model for the daily and weekly data category was the Prophet algorithm, while for the monthly data category was the ARIMA algorithm.