ARIMA stands for AutoRegressive Integrated Moving Average, a popular statistical method used for time series forecasting. It combines three components: autoregression, which uses past values to predict future ones; integration, which involves differencing the data to make it stationary; and moving average, which uses past forecast errors to improve predictions. ARIMA models are particularly useful for analyzing data that shows trends or seasonality. They require careful selection of parameters, often denoted as (p, d, q), where p is the number of autoregressive terms, d is the number of differences needed for stationarity, and q is the number of lagged forecast errors.