Define the Settings of Time Series Predictive Models for Planning

There are some settings to specify before you train your time series predictive model using a planning model as data source.

To define how you want your predictive model to be trained, use the Settings panel as described in the tables below.

For more information about what is currently supported in Smart Predict, see the section Restrictions Using Planning Model as Data Source for Smart Predict in Restrictions.

General

Settings	Action	Additional Information
Description	Enter a description that explains what your predictive model is trying to do.	For example, you might want to `Forecast product sales by city`.
Times Series Data Source	Browse and select the planning model you want to use as a data source.	Smart Predict supports only standalone planning models, (both new model types and classic account models). Note SAP Business Planning and Consolidation (SAP BPC) planning models are not supported whether these are live or acquired.
Version	Browse and select the planning model version you want to use as data source.	The input version must be a public version, not in edit mode, or a private version. You must have a least read access to it. There are also some specificities when currency conversion is enabled. See Use Currencies with Predictive Planning.

Predictive Goal

Settings	Action	Additional Information
Target	Select the numeric value containing the data to be forecasted. Note If your target is related to a currency, you also have the option to select Default Currency or Local Currency. The option you choose determines the currency used to forecast and report on your target. If your planning model is a new model type structured with an account dimension and one or multiple measures, to define your Target, you must select a value for both, using the Measure and Account selectors. If your planning model is a new model type or a classic account model structured with measures only, you're only prompted to select a value for Measure.	Smart Predict doesn't support calculated measures when using a planning model, even if an inverse formula is provided. For more information on inverse formulas, you can refer to the chapter Inverse Formulas. For more information about using a planning model as a data source, see the section Restrictions Using Planning Model as Data Source for Smart Predict in Restrictions. For more information about currency support, see the chapter Use Currencies with Predictive Planning. To learn more about the different model types, you can refer to the chapter called Get Started with the New Model Type.
Date	The date dimension in the predictive model.
Time Granularity	By default, this refers to the level of date granularity available in the planning model data source.	The forecast points created by Smart Predict respect the Time Granularity of the planning model. For instance, if the planning Time Granularity is month and 3 forecast periods are requested, the forecasts will be created for 3 consecutive months.
Number Of Forecast periods	Select the number of predictive forecasts you would like to get.	For more information, see How Many Forecasts can be Requested?
Entity	Select up to five dimensions or attributes for which you want to get distinct forecasts. This field is optional.	An Entity corresponds to defining each entity that you want to get forecasts for. The predictive model captures specific behaviors for each entity and generates distinct predictive forecasts. For more information, see Get Distinct Predictive Forecasts per Entities For your Planning Model Note There are specific restrictions on Entities. For more information, see the section Restrictions Using Planning Model as Data Source for Smart Predict in Restrictions.
Entity Filters	You can select the relevant values in dimensions or attributes that are defined as part of entity. It is possible to select values and specific members in the hierarchy (parent-child, level based, or flat) for a dimension defined as an entity. This helps you to focus on the predictive forecasting scope. (This field is optional.) Restrictions: It limits predictive forecasts to relevant combinations of entities so that only predictive models that have an interest for the user are generated. It reduces computation time through the reduced number of entities.	The Entity Filters help in filtering the entities you want to get the forecasts for. Note There are specific restrictions on Entity Filters. For more information, see the section Restrictions Using Planning Model as Data Source for Smart Predict in Restrictions.

Predictive Model Training

Settings	Action	Additional Information
Train Using	Select whether you want to train your predictive model using all observations or a window of observation. If you choose to use a window of observations you'll need to specify the size of the window you want to use. Note If the range of predictive forecasts overlaps existing data in the private version, data will be overriden.	It can be useful to define the range of observations that will be used to train the predictive model. You may want to ignore very old observations or inappropriate observation to avoid that your predictive model learns based on obsolete/inappropriate behavior.
Until	Select whether you want to train your predictive model until the last observation or another date of your choice.	If you select a custom observation date, make sure it stays within the time range defined in the data source planning model.
Convert Negative Forecast Values to Zero	Switch the toggle on if you want to get positive forecasts only.	This turns negative predictive forecasts to zero. This can be useful when predictive forecasts only make sense as positive predictive forecasts. For example, if you need to forecast the number of sales for one of your main product by major cities for a region. It makes no sense to get negative values. Either you sell a number of products or you sell none of them. Negative values will be turned to 0.

Select the Train & Forecast button. Thanks to the generated reports, you can analyze the predictive model performance, and decide if you need to further refine your predictive model, or if you can use the predictive forecasts with confidence. For more information, see Analyzing the Results of Your Time Series Predictive Model.

To know more about why you would want to use influencers in context of time series models on top of planning models, please refer the link below that explains How Adding Influencers to Your Planning Model Can Potentially Increase the Accuracy of Your Predictive Model?.