TD_ARIMAVALIDATE Function | Teradata Vantage - TD_ARIMAVALIDATE - Teradata Vantage

The TD_ARIMAVALIDATE function performs in-sample forecasting for both seasonal and non-seasonal auto-regressive (AR), moving-average (MA), and ARIMA models. It also supports the extended Box-Jenkins seasonal ARIMA model formula.

ARIMA validation is the process of evaluating the accuracy and reliability of an ARIMA model for time series forecasting. The TD_ARIMAVALIDATE function provides metrics and residuals, such as mean absolute error (MAE), and mean squared error (MSE), that can be used for model selection and validation.

To perform ARIMA validation using TD_ARIMAVALIDATE, first train an ARIMA model on a training dataset using the appropriate parameters for the model, such as the order of differencing or autoregressive order. Once the model is trained, TD_ARIMAVALIDATE performs within-sample forecasting on the training dataset.

The amount of data processed by TD_ARIMAVALIDATE depends on the FIT_PERCENTAGE parameter used during the TD_ARIMAESTIMATE operation. For example, if FIT_PERCENTAGE was set to 80 during estimation, than that means that 80% of the series dataset was used for estimation processing, and 20% remains for the validation operation.

After evaluating the metrics, use the residuals produced by TD_ARIMAVALIDATE to perform additional model validation tests. Residuals are the differences between the predicted values and the actual values in the training dataset. Analyzing the residuals can identify any patterns or trends that the model may have missed, such as seasonality or trends, and determine if the model is correctly specified.

• TD_ARIMAVALIDATE Syntax
• TD_ARIMAVALIDATE Syntax Elements
• TD_ARIMAVALIDATE Input
• TD_ARIMAVALIDATE Output
• TD_ARIMAVALIDATE Examples