ARIMA (Autoregressive Integrated Moving Average) is a time series forecasting method that combines autoregression (AR) and moving average (MA) models with differencing. It is used in finance, economics, and other fields to predict future trends or patterns in time series data.
TD_ARIMAFORECAST is used to forecast a user-defined number of periods based on models fitted from the TD_ARIMAESTIMATE function.
The following procedure is an example of how to use TD_ARIMAFORECAST:
- Run TD_ARIMAESTIMATE function with FIT_PERCENTAGE less than 100 to get the coefficients for the ARIMA model.
- Run TD_ARIMAVALIDATE function to validate the model produced during the TD_ARIMAESTIMATE estimation phase.
- Run the TD_ARIMAFORECAST function on the ART table produced by TD_ARIMAVALIDATE to forecast the future periods beyond the last observed period.