Debriefing Time Series Predictive Model Results

A predictive model produces performance indicators and reports as a result of a successful training. Here is a short summary of the different components that you can use to debrief your results so you can verify the accuracy of your predictive model.

• Is the main performance indicator high enough to consider my predictive model robust and accurate? Check the quality of your model performance over the Horizon-Wide MAPE. It evaluates the "error" made when using the predictive model to estimate future values of the signal, where zero indicates a perfect model. The lower the Horizon-Wide MAPE, the better your predictive model performance. For more information, refer to Horizon-Wide MAPE.

• What forecasts are provided by the predictive model? Have a close look at the signal and forecasts. The Signal Analysis tab shows trends, cycles, and fluctuations in the signal, each with a description. Check if there are outliers in the forecasts and detect anomalies on the signal. For more information, refer to The Predictive Forecasts, The Signal Outliers and The Signal Anomalies

• How accurate is my predictive model? Use the Signal vs. Forecast graph to visualize the predicted values (forecast) and actual values (signal) for the data source. You can then quickly see how accurate your predictive model is, what are the outliers, the zone of possible errors. For more information, refer to The Forecast vs. Actual Graph and The Signal Outliers.

What's next?

If you are satisfied with the results of your predictive model, use it. For more information, see Saving Predictive Forecasts Generated by a Time Series Predictive Model into a Dataset or Saving Predictive Forecasts Back into Your Planning Model.

If you are not satisfied, try to improve your predictive model by changing the settings, or if necessary, changing the data source.