Building a Time Series Model Using SAP Analytics Cloud Smart Predict

The training data set contains past observations (the history) used to generate the predictive model, along with the data and the times when the observations were recorded.

• In this data set, the values of the signal variable are known.
• The data set might also contain some influencer variables.
• The past and future values of the influencer variables must be known (at least for the expected forecast horizon).

By analyzing the training dataset, Smart Predict generates a time series model.

The training or application input data set must not contain more than 1,000 columns.

While applying the predictive model to an application dataset, Smart Predict generates additional columns, and the application process can be blocked if the dataset exceeds the 1,000-column limit.

As with classification and regression models, you must select your data source and edit your column details. However, this lesson covers specific settings for building and training a time series model.

Target: The Target variable is the signal that you want to predict the values for or explain.

Date: The Date variable is mandatory for time series models.

Regardless of the date granularity selected in the time series predictive scenarios with a dataset as the data source, every date format must include years, months, and days. Therefore, even if a quarterly or monthly forecast is required, the dataset's date format still needs to include days.

For example, for the YYYY-MM-DD date format, the time series predictive scenarios can be created where the date granularity can be:

• Year expressed as YYYY-01-01, where YYYY is variable (moving year).
• Quarter or Month expressed as YYYY-MM-01, where YYYY-MM is variable (moving month).
• Weekly data in the date format YYYY-MM-DD, taking, for instance, the first day of the week as the characters DD (moving week).
• Day (calendar dates) expressed as YYYY-MM-DD where YYYY-MM-DD is variable (moving day).

Number of forecast periods: The number of forecasts to generate. If the input data set contains future values for influencers, the number of forecasts must be less than or equal to the number of future values in the data set. If there are future values for the next six months, the number of forecasts requested cannot exceed six.

The number of forecasts delivered with confidence intervals is determined as follows:

• If the training data set size is equal to or fewer than 12 periods, it is treated as a small data set case. By default, the number of forecasts with confidence intervals is set to 1.
• In other cases, the number of forecasts with confidence intervals is set to 1/5 of the training data set size.
• If the training data set contains 1,000 rows of data, Smart Predict can provide up to 200 forecasts with confidence intervals. If more than 200 forecasts are required, the accuracy of forecasts beyond the 200th cannot be evaluated.

Entity: Entity is an optional variable that is used to split up the predictive model into segments, with each one producing its own predictive model, with distinct predictions for each segment.

For example, it might be more relevant to have KPIs on both stores and products. If this type of prediction is useful, click the box and select the columns to use for segmentation.

Limitations for time series models: If the predictive model is configured for some forecast periods and/or entities beyond the recommended maximum limits, it is likely to create performance issues that can impact other users on the same SAP Analytics Cloud tenant.

• The maximum number of entities is 1000.
• The maximum number of forecast periods (independent of the number of entities) is 500.

Train using: Select which observations are used when training the data set for the time series model. You have two options:

1. All Observations: Train the predictive model using all observations available in the data set. Choose the date of the last observation or define the last date.
2. Window of Observations: Specify a restricted period of observations. Select the number of days, weeks, months, or years to be included in the observation window. Choose the date of the last observation or define the last date (this date must be available in the data set).

Until: You have two options:

1. Last Observation: Let the application use the last training reference date as a basis.
2. User-defined Date: Select a specific date (that is available in the data set).

Exclude As Influencer: The past and future values of the influencer variables must be known (at least for the expected forecast horizon). Select the influencer variables to exclude when training the time series forecast model.

Convert Negative Forecast Values to Zero: Turn negative forecasts to zero. This is useful when negative values are not relevant for the business scenario, for example, the number of births. Negative values are forced to take a zero value, which influences error computation and the selection of the best predictive model.