Simple Exponential Smoothing
To use Simple Exponential Smoothing, go to Forecasting > Modeling, and select Simple Exponential Smoothing. Use the drag-and-drop method or double-click to use the algorithm on the canvas. Click the algorithm to view and select different properties for analysis.
Properties of Simple Exponential Smoothing
The available properties are shown in the figure below.
The table below describes the different fields present in the properties of Simple Exponential Smoothing.
Field | Description | Remark |
Run | It allows you to run the node. | – |
Explore | It allows you to explore the successfully executed node. | – |
It displays the following options in the list view.
| -- | |
Task Name | It is the name of the task selected on the workbook canvas. |
|
Time ID Variable | It allows you to select the interval type variable for which you need to process the dependent or target variable's values. |
|
Target Variable | It allows you to select the experimental or predictor variable(s). |
|
Group By | It allows you to select the variable for which you want to group the data. |
|
Initialization Method | It determines how the initial forecast is calculated before the smoothing process begins. |
|
Interval | It allows to select the interval for the accumulation of data in the accumulation test. |
|
Number of Periods for Forecasting | It allows you to select a specific number of periods you want to forecast based on the results. |
|
Smoothing Level | It determines the weight given to the most recent observation when generating forecasts. |
|
Example of Simple Exponential Smoothing
Consider a "Unit_Production" dataset with 30 records. It contains columns for Date, Number of Production, and the Name of Supervisor. A snippet of the input data is shown in the figure given below.
We apply the Train Test Split on the input data and then connect Simple Exponential Smoothing to the Train Test Split algorithm.
The selected values for Simple Exponential Smoothing are given below.
Property | Value |
Time ID Variable | Date |
Target Variable | No of Production |
Group By | Name of Supervisor |
Initialization Method | estimated |
Interval | Day |
Number of Periods for Forecasting | 5 |
Smoothing Level | 1 |
Further, the Result page displays
- Forecasting Chart with actual values and forecasted values
- Trained Model Parameters
- Accuracy parameters
Similarly, you can change the Supervisor Name from the Select Group field and obtain the corresponding plots.
Related Articles
Holt Exponential Smoothing
Holt Exponential Smoothing is located under Forecasting > Modeling > Holt Exponential Smoothing. Double-click or drag-and-drop the algorithm to use in the workbook canvas. Click the algorithm to view and select the different properties for analysis. ...Holts-Winters Exponential Smoothing
Holts-Winter Exponential Smoothing is located under Forecasting > Modeling > Holts-Winter Exponential Smoothing. Double-click or drag-and-drop the algorithm to use in the workbook canvas. Click the algorithm to view and select the different ...Components in Forecasting
Forecasting deals with the analysis and detection of trends in the time-series data. The components of forecasting are, Data Exploration: Data Exploration is used to explore the time-series data. It helps in identifying the underlying parameters ...Data Joiner
Data Joiner is located under Model Studio ( ) in Data Preparation, in the task pane on the left. Use the drag-and-drop method to use the algorithm in the canvas. Click the algorithm to view and select different properties for analysis. Refer to ...Data Joiner
Data Joiner is located under Model Studio ( ) in Data Preparation, in the task pane on the left. Use the drag-and-drop method to use the algorithm in the canvas. Click the algorithm to view and select different properties for analysis. Refer to ...