Concept | What if? analysis for time series

Definition

What if? analysis is a technique used to evaluate how variations in input parameters impact the model output. In the context of time series forecasting, it involves modifying one or more variables that influence the temporal process and assessing how forecast trajectories respond to these changes.

In classical ML, What if? analysis is static and immediate. In time series, What if? analysis is dynamic and temporal: changes affect not only the current value but also propagate into future periods due to the inherent dependencies of sequential data.

In Dataiku, this feature is available after training a model for a time series forecasting issue. When you change values, you can directly see the changes being displayed for the forecasted values once you compute them.

If you are predicting a multiple time series case, you can:

• Switch between time series identifiers.

• Modify values across timesteps on the fly. They’ll be highlighted to mark them as changed for easy tracking.

Finally, if you save a set of modifications, you can browse through them and observe the effect of different potential futures.

What’s next?

To learn more on time series in Dataiku, see Tutorial | Time series forecasting (visual ML).