Stock prices are a really challenging and obscure task that requires tremendous efforts while the nature of the stock market is arbitrary and uncertain. Stock estimation is such an important topic in business, economics, and finance that researchers have been engaged to explain how to construct effective forecasting models. In the stock market, there is no control over the performance of an investment, so anything can occur in the short term, a pill that is difficult to swallow so researchers predict stock prices by adopting scientific methods which are valuable for investors to earn and grow their profits. In time series forecasting research, the Autoregressive Integrated Moving Average (ARIMA) models have been examined. This article explains how to use the ARIMA model to create a comprehensive stock price prediction model. The stock price of Johnson & Johnson (JNJ) is combined with published stock data from S & P (500), and a predictive model is constructed. The results demonstrate that the ARIMA model can address traditional stock price forecasting approaches and has a lot of potential for JNJ in terms of short-term forecasting. As a result of its tremendous volatility. The ARIMA model, on the other hand, is not ideal for non-stationary or weakly stationary data, such as the S & P 500 index.