The Autoregressive Integrated Moving Average (ARIMA) is 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 models the relationship between an observation and a residual error from a moving average model. ARIMA is denoted as ARIMA(p, d, q), where p represents the number of autoregressive terms, d is the number of differences needed for stationarity, and q is the number of moving average terms. This model is widely used in various fields, including economics and finance, to analyze and forecast trends in data over time.