The Autoregressive Integrated Moving Average, or 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 smooths out short-term fluctuations by averaging past forecast errors. ARIMA models are particularly useful for analyzing data that shows trends or seasonality. By identifying patterns in historical data, ARIMA can help forecast future values, making it valuable in various fields such as economics, finance, and environmental science.