## 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 |
---|---|---|---|

(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. |

(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. |

(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.

- for the first non blank value, the function return the same, this is shown in the

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]`

## See also

Excel: no equivalent

Related articles: None

References: Wikipedia Exponential Smoothing