Price Prediction Using ARIMA Model of Monthly Closing Price of Bitcoin Apriliyanus Rakhmadi Pratama 1* 1 IAIN Sultan Amai Gorontalo, Gorontalo, Indonesia * Corresponding Author: Apriliyanus.pratama@iaingorontalo.ac.id Article Info Abstract Article History: Received: July 12, 2022 Revised: October 29, 2022 Accepted: October 29, 2022 Available Online: October 31, 2022 The rising of bitcoin’s user as a digital currency and investments causing an instability and an uncertainty in price movement and increasing the risk of trading, therefore in this study we try to forecast the future value of bitcoin price using ARIMA Models. 2 candidate models are selected by the lowest value of AIC and using the performance indicators ME, RSME, MAE, MPE, and MAPE conclude ARIMA (1,1,0) are the best ARIMA model, then the next 5 months future price forecasted using the best model. While ARIMA (1,1,0) is the best model, the model failed to follow price movement as shown in the forecasted price. Key Words: ARIMA Bitcoin Forecasting Univariate 1. INTRODUCTION Bitcoin is a phenomenal digital currency that shaking economic foundation since its nature are decentralized and powered by its users with no central authority or middleman. Bitcoin’s transactions are recorded and monitored by its users using cryptograph network technology or called blockchain. Created in 2008 and used for the first time in 2009 as it launched as open software [1]. Nowadays, bitcoin has gathered many interests, not just people, communities, or companies but countries [2]. Like a double edge sword, bitcoin’s price rising and falling as its user increasing follows the market flow of supply and demand causing a instability and uncertainty. Despite its volatility, bitcoin is still in early phase and one promising digital currency in the future. Therefore, in this study, we try to apply time series analysis method to forecast the future price of bitcoin and trying to follow its price movement. 2. METHOD 2.1 Nonstationary Nonstationary is a condition where time series data had no zero mean, constant variance over time, and constant autocorrelation structure over time. Performing stationary test on time series data formally conducted by unit roots test’s Dickey-Fuller [3] with uses the null hypothesis H0: data had unit root / time series data nonstationary against alternative hypothesis H1: data had no unit root / time series data is stationary. Ideally, reject H0 as p-value less than significance level alpha = 0.05 and conclude time series data is stationary. Handling nonstationary data is carried out through differencing process (1 − )  with  as differencing order while transforming time series data helps to reduce non constant variances, most commonly transformation method is Box-Cox Transformation [4]. Box-Cox transformation depend on the parameter  and are defined as   ={ log (  )   = 0 (   − 1)  ℎ where   is original observations and   is transformed observations. Detail estimation value of parameter  shown in Table 1. JSDS: JOURNAL OF STATISTICS AND DATA SCIENCE VOLUME 1, No 2, October 2022 e-ISSN: 2828-9986 https://ejournal.unib.ac.id/index.php/jsds/index