Forecasting with Predictive Analytics

Time series forecasting is available in SAP Analytics Cloud in the following areas:

• Smart Predict
• Time series line chart
• Story grid or table

• Expense and cost planning:
  • What will be my expenses by location and category?
  • Should we transfer budget between areas?
• Revenue and sales planning:
  • What is the revenue forecast per business unit?
  • How will sales evolve for specific products in the future?
• Headcount planning:
  • How many employees will leave due to attrition or retirement next year?
  • What are the profiles of employees that leave?

Predictive forecasting takes different values into account and also looks at trends, cycles, and fluctuations in your data. This data-driven approach optimizes your planning process since it's based on facts, not feelings.

SAP Analytics Cloud predictive forecasting helps you understand your key business influencers, with interactive visuals giving you a clear view of your data. This unbiased view leads to more accuracy in planning tasks.

To use predictive forecasting in SAP Analytics Cloud, try to use a dataset with as much historical data as possible. The system will use this dataset to look for trends, and the more history you have, the easier it is to notice trends.

SAP Analytics Cloud predictive forecasting allows you to understand past data trends to predict future metrics. The automatic predictive algorithm classifies existing information, identifies outliers, and surfaces relationships within your data to help you identify and understand your business’ key influencers.

The main functions of the automatic predictive calculations are:

• Identify key influencers.
• Explore visualized data that is automatically generated based on your query.
• Predict future results based on historical data.

Configuring the predictive scenario is straightforward with Smart Predict. You don't need to be a machine learning expert or a data scientist.

• Select the planning model to be augmented.
• Select the version of the planning model to learn from. Smart Predict should learn from actual data.
• Select the measure to be predicted. In this scenario, you want to predict the gross revenue measure.
• Select the number of forecast periods to be predicted. The granularity of the planning is month, and if you would like to get predictions for 12 months, request 12 forecasts.
• Select the entities. For example, you can share the predictions and insights with stakeholders responsible for different products in different countries/regions.

You can further refine your predictive forecasts by using the Influencers setting that allows you to select which influencer variables to include before training your predictive planning model.

With Smart Predict, you can go one step further and generate forecasts per entity to get accurate business-oriented insights, not only raw forecasts. You can directly use your planning model as the data source, no need to extract data in a dataset.

Smart Predict uses the data available in your planning model to create and train a predictive model. You can then analyze predictive forecast accuracies across the combined dimension values and understand signal breakdown in details. Once you're satisfied with the accuracy of your predictive model, you can generate the predictive forecasts: they're saving back directly in the private version of your planning model. It’s then easy for you to augment your story with actual and predictive forecasts.

When you create a predictive model, you initially specify a training data source, a target or signal variable, and then define additional training settings. Training is a process that uses SAP automated machine learning algorithms to find relationships or patterns of behavior in the data source. You can apply the result to a new data source to predict with a probability what could be the value of the target or signal for each element of the data source.

When using a planning model, the input version must be a public version, not in edit mode, or a private version. You have at least read access to it.

A predictive model produces performance indicators and reports as a result of a successful training.

Here's a short summary of the different components that you can use to debrief your results so you can verify the accuracy of your predictive model:

• Horizon-Wide MAPE Is the main performance indicator high enough to consider my predictive model robust and accurate? Check the quality of your model performance over the Horizon-Wide MAPE. It evaluates the "error" made when using the predictive model to estimate future values of the signal, where zero indicates a perfect model. The lower the Horizon-Wide MAPE, the better your predictive model performance. For more information, refer to Horizon-Wide MAPE
• Signal Analysis What forecasts are provided by the predictive model? Have a close look at the signal and forecasts. Signals show trends, cycles, and fluctuations in the signal, each with a description. Check if there are outliers in the forecasts and detect anomalies on the signal. For more information, refer to The Predictive Forecasts, The Signal Outliers and The Signal Anomalies
• Signal vs. Forecast How accurate is my predictive model? Use the Signal vs. Forecast graph to visualize the predicted values (forecast) and actual values (signal) for the data source. You can then quickly see how accurate your predictive model is, what are the outliers, the zone of possible errors. For more information, refer to The Forecast vs. Actual Graph and The Signal Outliers

If you're not satisfied with the model once you’ve analyzed the results, you can improve your predictive model by changing the settings, or if necessary, changing the data source.

Check the quality of your predictive model performance over the Horizon-Wide MAPE. The Horizon-Wide MAPE is the evaluation of the "error" made when using the predictive model to estimate the future values of the signal. A Horizon-Wide MAPE of zero indicates a perfect predictive model. The lower the Horizon-Wide MAPE, the better your predictive model performance.

Analyze the predicted values for the predictive model over a set of known data from the training data source. Check if there are outliers in the forecasts and detect anomalies on the signal.

Use the Signal versus Forecast graph to visualize the predicted values (predictive forecast) and actual values (signal) for the training data source. You can then quickly see how accurate your predictive model is, what are the outliers, the zone of possible errors.

For more information regarding Smart Predict, you can go to Analyzing the Results of Your Time Series Predictive Model.

After running Smart Predict and confirming that the results are reasonable, you can save the results to the planning model in a private version.

You can write Smart Predict results to private planning versions only. The goal is to enforce a validations workflow and avoid unvalidated forecasts to be written to public versions.

The confidence interval (dotted lines) helps to understand the level of confidence we have in the forecast. If the interval is narrow, we have a high confidence in the forecasts.

After running Smart Predict and confirming that the results are reasonable, you can save the data to the planning model in a private version.

After analyzing the results in a story, the values can then be published to a public version.

For more information regarding on predictive modeling, go to the Applying AI-powered Visualizations and Augmented Analytics to Business Data in SAP Analytics Cloud learning journey, where we explore augmented analytics in more detail, including time series predictive models.