Analyzing the Results of Your Time Series Predictive Model
Once you've trained your Time Series predictive model, you can analyze its performance to make sure it's as accurate as possible.
Use the dropdown list to access and analyze the reports to get information on your predictive model composition and evaluate your predictive model performance.
Click the area for more information.
Is the main performance indicator high enough to consider my predictive model robust and accurate?
Check the quality of your predictive model performance over the Horizon-Wide MAPE.
The Horizon-Wide MAPE is the evaluation of the "error" made when using the predictive model to estimate the future values of the signal. A Horizon-Wide MAPE of zero indicates a perfect predictive model. The lower the Horizon-Wide MAPE, the better your predictive model performance.
For more information, refer to Horizon-Wide MAPE.
What are the predicted values provided by the predictive model?
Analyze the predicted values for the predictive model over a set of known data from the training data source.
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 (predictive forecast) and actual values (signal) for the training 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 are the past data that most influences the signal?
Identify whether the signal is influenced by the recent past or far past in the case of an autoregressive component.
The lags are numbered with negative integers representing their distance in the past from the forecast. Lag -1 is the point in the past just before the forecast. Lag -5 is five points in the past. The higher the absolute value, the further the point is in the past.
For more information, refer to The Lagged Predictors Contribution.
What's next?
- Your are satisfied with your predictive model's performance. Then you can use it: Saving Predictive Forecasts Generated by a Time Series Predictive Model into a Dataset or Saving Predictive Forecasts Back into Your Planning Model.
- You would like to see if you can improve your predictive model's performance:
- Duplicate your current predictive model and experiment with updated settings. You can then compare the two versions and find the best one. See Duplicating a Predictive Model.
- Update the settings of your predictive model and retrain it.
See Define Settings and Train a Time Series Predictive Model.CautionYou will erase the previous version!
- Delete your predictive model. See Deleting a Predictive Model.