Explainability details are included in the Amazon Ensemble, Anaplan Prophet, and MVLR algorithms.

  • Explainability details pinpoint the relative impact of the forecast inputs (such as related data) on prediction results. Take, for example, an electric bike company:
      • You can load a PlanIQ forecast model with information on historical oil prices, e-bike prices, and promotion data.
      • You can show whether ‌increased oil prices or promotions have a greater sales impact.
  • Explainability details are derived from independent feature variables within algorithms. These details help to make predictions about the future. ‌Common feature examples are:
      •  Marketing campaigns and promotions
      •  Inventory levels
      •  Crude oil prices
  • Not all features impact prediction results in the same way. The feature contribution provides an idea about the degree of influence a single feature has on a prediction result. 

The chart below displays the impact of features on forecast results.

This chart shows that Historical trends have an impact of 10.1%, 3 month seasonality has an impact of 9.9%, and that
12 month seasonality has an impact of 51.2%.

You can see that 12-month seasonality has a much greater impact than historical trend or 3-month seasonality, which have similar impacts.