In time series forecasting, what is the primary role of the ARIMA model ?

Explanation: The ARIMA model (AutoRegressive Integrated Moving Average) is one of the most robust techniques for forecasting time series data. It combines three components: autoregressive (AR), which uses past values to predict future ones; integrated (I), which accounts for differencing to stabilize the series; and moving average (MA), which models the error terms. ARIMA works well for non-seasonal data and requires pre-processing such as stationarity checks. It is widely used in finance, sales forecasting, and inventory management. Option A: Exponential smoothing techniques, not ARIMA, focus on smoothing data for short-term forecasting. Option B: ARIMA handles more than linear trends; it also accounts for autoregressive and moving average aspects. Option D: Decomposition is a preparatory step for analysis, not ARIMA’s primary role. Option E: Seasonal indices are relevant for seasonal models like SARIMA, not ARIMA.