This document gives the step by step procedure to use R-Single Exponential Smoothing Algorithm in SAP Predictive Analysis.
R-Single Exponential Smoothing:
The R-Single Exponential Smoothing algorithm enables you to smooth the source data by reducing noise and performing prediction for the time series data by using R library functions. Time series data is a sequence of observations over a period of time.
Let us walk through a simple example to work on the R-Single Exponential Smoothing
Step 1: The below Excel file have the OrderID and Sales Amount
When you use a time series model for analysis, the model does not consider data from the selected data source. Instead, it forecasts by considering data that was used while generating a model.
Step 2: Open the Predictive Analysis Tool.
Step 3: Go to FILE-> NEW-> Select the Data Source (here in this case select Excel)
Switch to “Predict” tab to set up the forecasting.
You can create a model by saving the state of a trained algorithm.
- In the application toolbar, choose the Designer button to switch to the designer perspective.
- Expand Time Series sub type.
- Drag the R-Single Exponential Smoothing algorithm onto the analysis editor.
In the analysis editor, right-click the algorithm component and choose Properties
In the properties view, perform the following steps:
- Select Forecast as output mode, as you want to forecast the data.
- Select the Sales Amount column as the dependent column. The algorithm forecasts the data based on the Airline Passenger column.
- In the Period field, select Month (12).
- Enter 2011 as the start year.
- Enter 1 as a start period. As the period is Month (12), 1 implies first month of the year (January).
- Enter 12 for the number of periods to predict.
- Retain the default values for the advanced properties.
Then Visualize your results using “Visualize” Tab
Hope it helps to use R-Single Exponential Smoothing Algorithm in SAP Predictive Analysis.