Abstract
This paper illustrates the working process of predicting the Bitcoin price applying ARIMA, SARIMA and linear regression. Since more and more machine learning models were developed and tested in the financial field, these three models are selected to examine their reliabilities. In this study, three methodologies have been used for the Bitcoin predictions under the data set of Bitcoin historical prices. With the help of python notebook, order (1, 1, 1) and seasonal order (0, 1, 1, 12) were applied to the predictions in ARIMA and SARIMA respectively. In terms of linear regression, this paper used two independent variables including historical data and trading volume to predict the Bitcoin prices. It was discovered that the predictive graph for these three methodologies can match the actual value well, and linear regression performs the best. Considering the rapid development of machine learning methods, adopting alternative methods deserve in-depth investigations.
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