Choose to exclude any values of zeros from your forecast, or use them as a true value in historical and related data sets.
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Users can change certain values in the original time series data based on automated logic. The change does not affect the original data, just how PlanIQ interprets the data.
This functionality helps to distinguish between actual 0 numeric values and cases when numeric value is missing. In both cases, the value in the module is 0.
For forecasts, it's critical to understand if the value is 0 or missing. For example:
An item that's 0 as there was no demand on a specific day, is different from an item that's 0 because the store was closed, and there were no sales.
Add an exclude value column to each column of numeric data in your historical and related data sets so PlanIQ can differentiate between those cases.
You can add an exclude value column to any time series data in historical and related data modules.
To exclude values from input:
- Open a historical or related data module that has 0 values.
- Add a new numeric line item and add ___exclude_value to the end of the title.
For example if you have a line item called Units sold, the zero line item would be Units sold___exclude_value:
- For any zero values that you want to include, enter a 1 in the corresponding ___exclude_value field. Values marked with 1 can be either zeros or other problematic values. Users can change these values automatically.
- For any zero values that you want to exclude, keep 0 in the corresponding ___exclude_value field.
PlanIQ excludes these zeros, and treats these as missing values.