When a planner leverages SAP Analytics Cloud Predictive Planning to create predictive forecasts, he can gauge accuracy using the measure called
Expected Mean Absolute Percentage Error (Expected MAPE) to evaluate the accuracy of the predictive model. This measure is not exported with the predictive forecasts into a private version of the planning model.
The goal of this blog is to explain how a planner compares the actuals with predictive forecasts and measures in the story the accuracy of these predictive forecasts.
You can refer to the blog
Time Series Forecasting in SAP Analytics Cloud Smart Predict in Detail to know how predictive forecasts are computed from historical data. Expected MAPE is explained in the blog
Understand accuracy measure of time series forecasting models (note that before wave 2021.19 it was named HW-MAPE). In this blog, I will explain how you can generate the Mean Absolute Percentage Error (MAPE) inside a story. It is of course possible to use other accuracy measures as well. For example, this
blog explains how to expose the Mean Absolute Error (MAE).
How the MAPE is calculated
The formula of the MAPE is:
Where:
- forecast is a predicted value
- signal is an actual value
- h is the horizon, or the number of forecasts requested by the user in the settings of the predictive scenario. It refers to the period in the future you want to predict.
This formula shows the difference between predicted values and actual values. The formula is a sum of terms divided by the number of these terms. It is an average of the difference between predictive forecasts and actual values. These terms are called the
Absolute Percentage Error (APE).
MAPE is the average of these APEs. The smaller this difference is, the closer the predictive forecasts will be from the actual values.
Let’s see how to compute the MAPE inside a story based on a planning model.
Prepare the Story
In
GitHub there is file
Car Sales Italy.xlsx. It contains the monthly car sales amount in Italy from 2015 to 2020. The predictive goal is to plan sales amount for the next twelve months. The first step is to create a planning model from this excel file.
Thus, go to your SAC tenant and open the Modeler. Select the excel file to import it into the Modeler. Do not forget to check the option
Enable Planning as shown below.
Fig 1: Set model as a planning model
Then click the
Create Model button and give it a name. Once done, the planning model will look like this:
Fig 2: Planning model
Let’s now create a story. In the title bar of the window, there is a contextual shortcut to create a story as shown below.
Fig 3: Create a Canvas Story
Choose the type
Canvas and insert a
Table. With this shortcut, a new story is created, based on the planning model we just created. Configure the story as shown in the figure below with the DATE dimension in the rows section and the account SALES in the columns section.
Fig 4: Creation of the Story
From the toolbar, select the
Version Management and click the icon on the right side of the
Actuals version.
Fig 5: Create a private version
In the
Version Management window, create a blank private version. This private version will receive the predictive forecasts.
Fig 6: Setting of the private blank version
Now, the story looks like this:
Fig 7: Story before the predictive forecasts
Name your story and save it.
Get Predictive Forecasts in the Story
Let’s visualize the MAPE directly into the story. For that, it is necessary to compare the predictive forecasts with the actual values. If we forecast 2021, this comparison is not possible as the historical dataset is available only until the end of 2020 and does not have actual values in 2021.
The solution can be found in the settings of the Predictive Scenario. Thus, let’s create a time series Predictive Scenario. Its data source is the planning model about car sales in Italy. The settings to predict the next twelve months are shown below:
Fig 8: Settings of the Predictive Scenario
Now, to be able to compute the MAPE inside the story, the Predictive Scenario is set to train the predictive model until the end of 2019. Thus, the twelve predicted months correspond to the year 2020. This makes it possible to compare the predictive forecasts to the actuals.
Fig 9: Train until 2019
Click the
Train & Forecast button to generate the predictive model.
You will get explanations - here SAC Predictive Planning detects a piecewise trend and a recurring monthly pattern every year.
Fig 10: Breakdown of the time series historical data
You also get the monthly predictive forecasts for the year 2020.
Fig 11: Predictive Forecasts for 2020
This figure shows an
Expected MAPE of 6.33%. A low Expected MAPE corresponds to an accurate predictive model. In this case, we can consider that a value of 6.33% is a good accuracy value. I let you read this
blog to have a detailed explanation of the Expected MAPE. It is important to understand that this accuracy measure is calculated based on internal calculations which are not visible to the end-user. The measure included inside the story is the MAPE obtained for the year 2020 by a comparison between actual values and predictive forecasts of 2020.
To do this, the next step consists in saving the predictive forecasts into a private version of the story.
Fig 12: Save Predictive Forecasts
Once applied, the predictive forecasts for 2020 are visible in the story.
Fig 13: Story with the predictive forecasts for 2020
Compute the MAPE
Multiple steps are needed to compute the MAPE.
To avoid changing the
Actuals version, the easiest way is to filter this version. The filter is set on
Category as shown below.
Fig 14: Filter the Category to keep only the private version
But the car sales measure
SALES (actuals) is in the
Actuals version while the predictive forecasts are in the private version. You need to have
SALES also in the private version. It is easy to add a calculation in the
Account dimension as shown below.
To do this, click on the three dots beside
Account and select
Add Calculation…
Fig 15: Add actuals into the private version
In the
Calculation Editor, select a
Restricted Measure whose name is
Actuals (SALES). The values are copied from the measure
SALES of the category
Actual. It is important to check
Enable Constant Selection on the dimension
Category. This makes the new variable available to all versions.
Fig 16: Definition of variable Actuals (SALES)
The story will now look like this:
Fig 17: The story is ready to include the MAPE.
The next step is to compute the Absolute Percentage Error (APE) for each month. The formula to get the APE is:
APE_{m} = ABS((forecast_{m} – actual_{m}) / actual_{m})
Where
m stands for a month.
To do this, click once again on the three dots beside
Account and select
Add Calculation…. This time, select the Calculated Measure option in the drop down and name it as
Absolute Percentage Error with the formula shown in the figure below:
Fig 18: Definition of the variable to compute the Absolute Percentage Error
Click the
OK button and the story is updated right away. As the APE is a percentage, the best is to format this variable as a percentage. To do this, click the variable and then on the icon to edit formatting options.
Fig 19: Select Edit Formatting options
Set the
Formatting window as shown below:
Fig 20: Formatting window
Click
OK and the APE is formatted as a percentage.
Fig 21: Story with the Absolute Percentage Error
The last step consists of creating a new variable for the
MAPE (the average of all the Absolute Percentage Errors). The formula is:
MAPE = AVERAGE(APE_{m}) for m = 1 to 12
Thus, as before, create a new calculation named
MAPE. But this time, use an aggregation based on an average of all Absolute Percentage Errors. This aggregation is based on the dimension
DATE. The
Calculation Editor should look like this:
Fig 22: Definition of the variable to compute the MAPE
Change the formatting option as a percentage as done for the APE. The result appears directly in the story. The MAPE for 2020 is 8.56% which is good.
Fig 23: MAPE for the year 2020
Note: In addition, there are intermediate MAPEs for each month and quarter.
Conclusion
You can now extend your story with a MAPE measure. You can also create your own quality measure if that fits your use case best
Hope these explanations help you to use all the potential of SAC and help you also to meet your expectations. If you appreciated reading this blog, please like, comment, and share. You can also comment if you have follow-up questions. Thank you.
Resources:
SAP Predictive Planning playlist
Time Series Forecasting in SAP Analytics Cloud Smart Predict in Detail
Understand accuracy measure of time series forecasting models
Building a Time-Series Forecasting Performance Evaluation Metric In SAP Analytics Cloud