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rajeshm1
Calcite | Level 5

Hi SAS Experts,

 

I am performing Hierarchical Forecasting where I have set the reconciliation level at somewhere mid of the Hierarchy. So, while dis-aggregation for top down approach, which method should be better to use: DIFFERENCE or PROPORTION?

 

Thanks in advance! Looking forward to your response.

 

-Rajesh

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YueLi
SAS Employee

Analytically, it depends on your data, depends on what makes sense in your application.

Technically, there is no limitation on when to use DIFFERENCE, but you can only use PROPORTION when all the forecasts at a given ID value share the same sign, e.g. all non-negative.

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YueLi
SAS Employee

Analytically, it depends on your data, depends on what makes sense in your application.

Technically, there is no limitation on when to use DIFFERENCE, but you can only use PROPORTION when all the forecasts at a given ID value share the same sign, e.g. all non-negative.

mitrov
SAS Employee

Hi Rajesh,

 

The reconciliation level is essentially a reference level over which all other levels are adjusted to match. Ideally you want to find a level that optimizes your lowest level forecasts. However, typically, when you select a different level some series will improve some other will get worse. There is no single recipe to select the reconciliation level. It all depends on your data, like Yue said. It's a bit of an art mixed with business knowledge. Typically you want to select a level for which time series models would fit the data. For example, you do not want many intermittent series. Often the lowest levels are too intermittent and "noisy" to be modeled with time series models.

  

I suggest you export your data to VA (Visual Analytics) and explore it hierarchically to get a feeling of how each level looks like. Unfortunately, currently VF does not allow the explorations of different level of a hierarchy. It is planned for future releases. 

 

Similarly, the choice between proportion and difference should be driven by performance of the forecasts at the level of your interest. In practice, most people use proportions because it is more intuitive and easier to explain. Note that you can use proportions even with mixed-sign data.

 

Michele

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