Forecasting Bitcoin prices using artificial intelligence: Combination of ML, SARIMA, and Facebook Prophet models

Abstract

In recent years, investors, corporations, and enterprises have shown great interest in the Bitcoin network; thus, promoting its products and services is crucial. This study utilizes an empirical analysis for financial time series and machine learning to perform prediction of bitcoin price and Garman-Klass (GK) volatility using Long Short-Term Memory (LSTM), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Facebook prophet models. The performance findings show that the LTSM boost has a noticeable improvement compared to SARIMA and Facebook Prophet in terms of MSE (Mean Squared Error) and MAE (Mean Average Error). Unlike Long Short-Term Memory (LSTM), a component of Deep Learning (DL), the finding explains why the bitcoin and its volatility forecasting difficulty has been partially met by traditional time series forecasting (SARIMA) and auto-machine-learning technique (Fb-Prophet). Furthermore, the finding confirmed that Bitcoin values are extremely seasonally volatile and random and are frequently influenced by external variables (or news) such as cryptocurrency laws, investments, or social media rumors. Additionally, results show a robust optimistic trend, and the days when most people commute are Monday and Saturday and an annual seasonality. The trend of the price and volatility of bitcoin using SARIMA and FB-Prophet is more predictable. The Fb-Prophet cannot easily fit within the Russian-Ukrainian conflict period, and in some COVID-19 periods, its performance will suffer during the turbulent era. Moreover, Garman-Klass (GK) forecasting seems more effective than the squared returns price measure, which has implications for investors and fund managers. The research presents innovative insights pertaining to forthcoming cryptocurrency regulations, stock market dynamics, and global resource allocation.