As technology leads toward a new era of tools, there are also some sudden changes in
business and marketing. Cryptocurrency is a new and emerging investment and exchanging
tool for business and marketing. This work aims to develop a time series model that
efficiently forecasts cryptocurrency values. To achieve this end, we use the classical time
series model Autoregressive integrated moving average (ARIMA), also known as the BoxJenkins methodology. This work demonstrates that by using ARIMA models, the future
behavior of the series can be efficiently guessed. The work suggests some ARIMA models by
utilizing the Box-Jenkins methodology that can efficiently guess the future behavior of the
cryptocurrency, and these models are selected based on forecast accuracy. Namely root mean
square error (RMSE), mean absolute percentage error (MAPE), and the Akaike information
criteria (AIC). The results show that different models are selected to model and forecast the
four cryptocurrencies. These results will provide an initial guess to the investors and
consumers to know the behavior of the cryptocurrencies in the upcoming days.