### Global Performance Indicators

You check the quality of your predictive model performance with the **Expected MAPE**. The expected MAPE is the evaluation of the 'error' made when using the predictive model to estimate the future values of the signal, whatever the horizon.

- For each actual observed value, the predictive model calculates as many forecasted values as requested by the analyst. This is called the
**horizon of forecasts**. - Each of those forecasted values is compared to the corresponding actual ones. Then, for each possible horizon, a per-horizon MAPE can be calculated, which is the mean of the absolute differences between actual and forecasted values, expressed as a percentage of actual values.
- The expected MAPE is the mean of all per-horizon MAPE values that have been calculated.
- An expected MAPE of zero indicates a perfect predictive model. The lower the Horizon-Wide MAPE, the better your predictive model performance.
- An expected MAPE of 12% indicates that the error made when using a forecasted value will be approximately 12%.

### Forecasts vs Actual

The forecast vs. actual, graph as shown above, displays curves for the predicted values (forecast) and actual values (target) for the time series data source, quickly showing the accuracy of the predictive model.

The predictions are displayed at the end of the graph. For each forecasted value, the predictive model shows an estimation of the minimum and maximum error.

- The area between this upper and lower limit of the possible errors in the predictive forecasts produced by your predictive model is called the
**confidence interval**and is only displayed for predictive forecasts. **Outliers**are values marked with a red circle on the graph.- The
**forecasting error indicator**is the absolute difference between the actual and predicted values. This is also called the**residue**. The residue abnormal threshold is set to 3 times the standard deviation of the residue values on an estimation (or validation) data source. The forecasted value and error limit values are listed in the table for each predictive forecast, as shown below.

### Forecasts

The forecast table displays the following information to help you analyze the performace of your time series predictive model:

**Forecast:**Predicted values for the predictive model over a set of known data called the validation partition.**Error min and max:**Minimum and maximum deviation measures of the values around the predictive forecasts. If you choose to segment your time series forecasting predictive model using an entity, you will have this information for each segment.

### Outliers

The output table displays details of the outliers. An actual signal value is qualified as an outlier once its corresponding forecasting error is considered to be abnormal relative to the forecasting error mean, observed on the estimation dataset.

The forecasting error indicator is the absolute difference between the actual and predicted values and is also called the residue. The residue abnormal threshold is set to 3 times the standard deviation of the residue values on a training dataset.

**Anomalies** are signal values that are outside the zone of possible error for the predictive forecast, which is defined by its upper and lower limit. The signal is compared to all the predictive forecasts.