Automatic Forecast predictions
It's possible to have your Forecast predictions in the future to be done automatically based on your historical data.
The Forecast algorithms
There are currently three different methods you can use to generate Forecasts, two are performed by Quinyx, and the third is an option to connect a third party prediction service to Quinyx, allowing the forecast to be generated elsewhere and then presented in Quinyx.
Last Year Same Day
This algorithm automatically copies the actual data for your previous years forecast as a forecast for the current year-to-date ahead. You have the option to add a modifier as an increase or decrease in percentage on the previous years data.
Quinyx AI Demand Forecasting
The goal of the AI Demand Forecasting module is to create forecasts on a 15, 30, or 60-minute or daily level based on historical data. These forecasts can be used to create a headcount as a basis for scheduling. Headcount can be calculated either through Quinyx Labour Standards or Static/Dynamic Rule solution or through customer-specific calculations outside of Quinyx.
This method allows a third-party solution to push forecasted data into Quinyx through the API using POST /predicted-data:
With this operation, you can upload predictions from external tools used for predicting sales, transactions, etc., that optimal staffing rules will use to define staffing needs:
- externalForecastVariableId - ID for a variable as set up for units in Account settings→Forecast→Variables→External ID.
- externalForecastConfigurationId -
- externalUnitId - External ID for a unit as set up for units in Classic → Settings → Tables → integration keys
- externalSectionId (optional) - External ID for a section as set up for units in Classic → Settings → Tables → integration keys.
- runIdentifier - A value to be set if external system sends multiple predictions for the same date range.
- runTimestamp - A value to be set to define when the predicted data was generated. In the future, Quinyx will use this value to compare different predictions if they exist.
- Data - the amount of the external prediction.
- Timestamp - the time of where the amount should be placed in UTC format 2020-04-09T20:45:00+01:00.