MVLR is one of the algorithms that provides details on explainability.

MVLR is used for feature-based forecasts. You can use MVLR when: 

  • There are multiple features, such as promotions or inventory levels  
  • Features may be related to the SKU (promotions or inventory levels)
  • Features may be unrelated to the SKU (oil prices or gross domestic product)

The MVLR algorithm uses feature data to generate a prediction. Features are important when they influence prediction results.

MVLR feature input sources

  1. Historical data 
  2. Related data
  3. Synthetic data:
      • These features are created by PlanIQ automatically. They can be based on either historical or related data.  Examples are:
          • Exponential trends 
          • Linear trends
          • Seasonality
          • Lagged values
            (for example, a gross margin prediction, based on a fuel price change three weeks prior)

At the "create a related data module" stage, for the MVLR algorithm only, you can include up to 28 related time series (RTS) columns.


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