ARIMA, or AutoRegressive Integrated Moving Average, is a powerful time series forecasting model widely used in various fields. It consists of three main components: autoregressive (AR), integrated (I), and moving average (MA). The autoregressive component involves predicting future values based on past values, while the integrated component focuses on differencing to make the time series stationary. The moving average component considers the average of past prediction errors. The model is denoted as ARIMA(p, d, q), where ‘p’ is the autoregressive order, ‘d’ is the differencing order, and ‘q’ is the moving average order. By identifying these parameters based on the characteristics of the time series data, ARIMA helps forecast future values, particularly when there is a noticeable trends.