In recent years, Bitcoin has garnered significant attention from a diverse range of individuals, including academic researchers and trade investors. Bitcoin is widely recognized as the first and preeminent cryptocurrency in existence. Since its inception in 2009, the trading system of Bitcoin has gained significant popularity across diverse demographics due to its decentralized nature and the notable price fluctuations associated with the cryptocurrency. This article introduces a viable model for accurately forecasting the market price of Bitcoin through the utilization of several statistical analysis. The study was conducted using a data set spanning five and a half years of bitcoin data, specifically from 2015 to 2020. The analysis employed a time series analysis technique known as the autoregressive integrated moving average (ARIMA) model. Moreover, it is also subjected to comparison with an established machine learning technique known as the linear regression (LR) model. The extensive prediction results demonstrate that the ARIMA model, when compared to the LR model, exhibits greater performance in determining short-term volatility in the weighted costs of bitcoin.