ARIMA, which stands for AutoRegressive Integrated Moving Average, is a popular statistical method used for time series forecasting. It combines three components: autoregression (AR), differencing (I), and moving average (MA). This model helps in understanding and predicting future points in a series based on its past values and the errors of previous predictions. The AR part uses past values to predict future ones, while the MA part uses past forecast errors. The I component involves differencing the data to make it stationary, meaning it has a constant mean and variance over time. This makes ARIMA effective for various applications, including economic and environmental forecasting.