Time series analysis is an essential field in data analysis, particularly within forecasting and prediction domains. Researching and building time series models play a crucial role in understanding and predicting the temporal dynamics of various phenomena. In mathematics, time series data is defined as data points indexed in chronological order and have a consistent time interval between consecutive observations. This can include data such as daily stock prices, annual national income, quarterly company revenue, and more. The advantage of time series data is that it can capture the state of a variable over time. In contrast, the world is constantly changing, and phenomena rarely remain static they typically exhibit variations over time. Therefore, time series data has highly practical applications and is used in various fields, including statistics, econometrics, financial mathematics, weather forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, telecommunications, and signal processing. ARIMA, which stands for Auto Regressive Integrated Moving Average, is a widely used time series forecasting method in data science. It is a popular model for analyzing and predicting time-dependent data points. ARIMA combines autoregression, differencing, and moving averages to capture different aspects of time series data. In this paper, we study ARIMA, which is a significant model for analyzing and predicting time series data.