How to create forecasts with SAP Analytics Cloud for Planning
SAP Analytics Cloud (SAC) allows you to create time-series forecasts within a story. An algorithm uses historic data to predict future values. The result of the forecast can then be used in the planning procedure. In this post, I will be talking about the forecast function in detail.
Create a forecast
You can easily create a forecast in the tale. To do so, click on the Predictive forecast in the Tools menu.
When executing the forecast, you can specify the time interval for the forecast and the past periods whose data you want to use as a basis. You can also discover the granularity of the forecast by specifying whether the forecast is to be created on a monthly, quarterly, half-yearly, or yearly basis.
In the next step, you have the option to choose the algorithm to be used. You can choose between ‘automated forecast’, ‘linear regression’ and ‘three-way exponential smoothing’. You can also specify whether only positive worths need to be considered.
Then a preview of the forecast is shown, which includes the previous actual values along with the forecast values, which are displayed both as a layout and in the form of a table. The upper confidence period is additionally shown.
The solid line represents the past values and the dashed line represents the values. The confidence interval is shaded.
You can move the mouse over individual details to display the details. You can compare the predictive and real values.
A confidence period is as shown for future values for which there were no values. The confidence interval shows the range in which the future values are likely to lie.
The forecast data can then be moved to the plan.
When working with forecasts you need to accept some restrictions. For example, the underlying model may only include a one-time dimension. Weekly forecasts can not be delivered because a month is one of the most granular levels that can be used. Finally, forecasts can not be created for calculated key figures, solutions, and composite data sets.
To summarise, we can say that the incorporated forecast feature provides great help in the planning process. In this way, future values can be acquired that are based on statistical-mathematical techniques rather than pure previous year’s values.
A negative aspect is the minimal choice of algorithms. You can only select between 3 algorithms for time collection forecast and can not use your models. The available algorithms are sim[le models that are not capable of properly replicating complicated dependencies. Therefore, the generated forecasts should always be treated with caution and should only be treated as a rough guide.
Additionally, it is not possible to manage the forecast parameters. Thus the system stands for a black box and it is not feasible to understand how the respective result is achieved.
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