What does the ARIMA model utilize for forecasting?

The ARIMA model, which stands for AutoRegressive Integrated Moving Average, is a popular statistical method used for time series forecasting. It incorporates both autoregressive (AR) terms and moving average (MA) terms to capture different aspects of the data patterns.

The autoregressive component utilizes the relationship between an observation and a number of lagged observations (previous time points). This means it regresses the current observation on its past values. The moving average component, on the other hand, models the relationship between an observation and a residual error from a moving average model applied to lagged observations.

By combining these two elements—regressive terms that account for past values and moving average terms that account for the dependency on past forecast errors—the ARIMA model is able to create a comprehensive approach for forecasting future values based on historical data. This dual incorporation allows for greater flexibility and accuracy in modeling and predicting complex time series data.

The other options do not capture the full functionality of the ARIMA model. For instance, focusing solely on moving average terms neglects the critical autoregressive aspect. Similarly, using only linear combinations of individual periods overlooks the importance of past values and errors in forecasting. Lastly, restricting the model to only error values would ignore the contribution of previous