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)
Step 4: Browse the file and select the columns and click on Acquire as shown in the below screen.
Step 5: Switch to “Predict” tab to set up the forecasting.
Step 6: 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.
Step 7: 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.
- Select Yes to switch to visualization perspective.
- Switch to the designer perspective.
To view the results click the Results and view them in Grid mode
Switch to “Charts” tab.
Then Visualize your results using “Visualize” Tab
Algorithm Summary provides summary information for the algorithm execution. It contains information on the columns used in the algorithm. It also contains measures like least squares, f-statistics, confidence level, and various other parameters based on the type of the algorithm, which determines the efficiency Analyzing Data of the algorithm.
This information helps you to understand whether the algorithm is the best choice for the given dataset.
Hope it helps,