The AutoRegressive Integrated Moving Average (ARIMA) model, a potent time series forecasting method, comprises three key components. The AutoRegressive (AR) element captures the relationship between an observation and its lagged counterparts, denoted by ‘p’ signifying the number of lagged observations considered. A higher ‘p’ value indicates a more intricate structure, capturing longer-term dependencies. The Integrated (I) component involves differencing to achieve stationarity, crucial for time series analysis. ‘d’ represents the order of differencing, indicating how many times it is applied. The Moving Average (MA) component considers relationships between observations and residual errors from a moving average model, with ‘q’ representing the order of lagged residuals. Expressed as ARIMA(p, d, q), the model finds application in finance, environmental research, and any time-dependent data analysis. The modeling process involves data exploration, stationarity checks, parameter selection, model training, validation and testing, and ultimately forecasting. ARIMA models are indispensable tools for analysts and data scientists, offering a systematic framework for effective time series forecasting and analysis.