The ARIMA Model, which stands for AutoRegressive Integrated Moving Average, is a popular statistical method used for forecasting time series data. It combines three components: the autoregressive (AR) part, which uses past values to predict future ones; the integrated (I) part, which involves differencing the data to make it stationary; and the moving average (MA) part, which uses past forecast errors to improve predictions. ARIMA models are particularly useful when data shows trends or seasonality. Analysts often use the Box-Jenkins methodology to identify the best parameters for the model, ensuring accurate forecasts for various applications, such as economics and finance.