The ARIMA (AutoRegressive Integrated Moving Average) model, a powerful time series forecasting method, consists of three main components. The AutoRegressive (AR) element captures the relationship between an observation and its lagged counterparts, denoted by “p”, which represents the number of lagged observations considered. A higher p-value indicates a more complex structure involving long-term dependencies. The integral (I) component includes differentiation to achieve stationarity, which is crucial for time series analysis. The “d” represents the order of difference, which indicates how many times it is used. The moving average (MA) component accounts for the correlations between the observations and the residual errors of the moving average model, where “q” represents the order of the residuals. Expressed as ARIMA (p, d, q), the model can be applied to finance, environmental research, and any time-dependent data analysis. The modeling process includes data exploration, stationarity checking, parameter selection, model training, validation and testing, and finally forecasting. ARIMA models are invaluable tools for analysts and data scientists, providing a systematic framework for effective time series forecasting and analysis.