# SIMPLE_EXPONENTIAL_SMOOTHING function

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## 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):

• 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:

## 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]`