--- title: "Use SIMPLE_EXPONENTIAL_SMOOTHING for Exponentially Smoothed Values" slug: "simple-exponential-smoothing-function" description: "Learn to use SIMPLE_EXPONENTIAL_SMOOTHING to compute exponentially smoothed values across a ranking Dimension in Pigment." updated: 2025-05-28T10:26:30Z published: 2025-08-22T12:00:18Z --- > ## Documentation Index > Fetch the complete documentation index at: https://kb.pigment.com/llms.txt > Use this file to discover all available pages before exploring further. # SIMPLE_EXPONENTIAL_SMOOTHING function ## Description Computes an exponentially smoothed version of the input Block over a ranking Dimension. ## Syntax `SIMPLE_EXPONENTIAL_SMOOTHING(Input Block [, Ranking Dimension [, alpha]})` ## Arguments | Argument | Type | Dimensions | Description | | --- | --- | --- | --- | | *Input Block* (required) | Number | Any Dimensions | This is the data source which will be smoothed. The Metric must be defined at least on the `Ranking Dimension` Dimension. | | *Ranking Dimension* (optional) | Dimension | Not applicable | This is a Dimension applied to the time series taken in the `Input Block`. This is optional if it’s a datetime Dimension from the calendar. If this is not the case, then this is mandatory. It’s also mandatory if the Metric is defined on several time Dimensions. | | *alpha* (optional) | Number | no Dimension | Data smoothing factor, with a value between 0 and 1. The default value is 0.5 . | ## Returns | Type | Dimensions | | --- | --- | | Number | Dimensions of *Input Block* | - Before the first non-blank value of the Input Block, the function returns blank. - Between the first and the last non-blank value of the Input Block, the function returns the following values: - for the first non blank value, the function return the same, this is shown in the *s0* value in the equation below. - for other values the function computes a weighted average between the current value and the previous smoothed value. Here is an equivalent with mathematical notations (*s* being the exponentially smoothed version of the *x* serie): ![](https://cdn.document360.io/e47cfe35-dc28-40c7-a083-6cf003073d8e/Images/Documentation/399515ed-13de-4242-a5f4-3ffd86fce6b3.png) - After the last non-blank value of the Input Block, the function returns a constant value equal to the last smoothed value computed. > Blank observations (in the input Block) between the first non-blank value and the last non-blank values are considered as 0. ## Examples | Formula | Description | | --- | --- | | `SIMPLE_EXPONENTIAL_SMOOTHING(Actuals)` | Returned values are explained in the Returns section above. | | `SIMPLE_EXPONENTIAL_SMOOTHING(Actuals, Month, 0.2)` | Examples with different values of alpha: ![](https://cdn.document360.io/e47cfe35-dc28-40c7-a083-6cf003073d8e/Images/Documentation/1f22ef42-f67f-47b5-b28f-1f6a78be6015.png) ## Using Exponential Smoothing as Forecasting Function A common use case for using the SIMPLE_EXPONENTIAL_SMOOTHING function is to prepare a forecast. It’s a good method when your observation series shows no specific trend****and no specific seasonality. In that case the last smoothed value of the series is a good estimation of the next forecasted value. To do so you just need to offset the result by 1 period with this syntax: `SIMPLE_EXPONENTIAL_SMOOTHING(Observations)[SELECT: Month -1]` ![](https://cdn.document360.io/e47cfe35-dc28-40c7-a083-6cf003073d8e/Images/Documentation/36aea74e-8abf-4321-9a8d-cfd04526c1be.png) Using Exponential Smoothing as Forecasting Function ## See also Excel: no equivalent Related articles: None References: [Wikipedia Exponential Smoothing](https://en.wikipedia.org/wiki/Exponential_smoothing)