DOUBLE_EXPONENTIAL_SMOOTHING function

  • 18 April 2024
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Description

Computes a double exponentially smoothed version of the input Block over the ranking Dimension.

 

Syntax

DOUBLE_EXPONENTIAL_SMOOTHING(Input Block [, Ranking Dimension [, alpha, beta])

 

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 NA 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 0.25 .

beta

(optional)

Number no Dimension Trend smoothing factor, with a value between 0 and 1. The default value is 0.1 . 

 

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 value of the F series computed with the following process: 

 

  • After the last non-blank value of the input Block, the function returns the linear function result using the last slope and intercept 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
DOUBLE_EXPONENTIAL_SMOOTHING(Actuals) Returned values are explained in the Returns section above. 
DOUBLE_EXPONENTIAL_SMOOTHING(Actuals, Month, 0.2, 0.2)

 

Example:

 

 

Using Exponential Smoothing as Forecasting Function

A common use case for using the DOUBLE_EXPONENTIAL_SMOOTHING function is to prepare a forecast. It’s a good method when your observation series shows a 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: 

DOUBLE_EXPONENTIAL_SMOOTHING(Observations)[SELECT: Month -1]

 

See also

Excel: no equivalent

Related articles: SIMPLE_EXPONENTIAL_SMOOTHING

References: Wikipedia Exponential Smoothing


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