# Documentation/Calc Functions/FORECAST.ETS.SEASONALITY

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## Function name:

FORECAST.ETS.SEASONALITY

## Category:

Statistical Analysis

## Summary:

Calculates the number of samples in period as calculated by Calc in case of FORECAST.ETS functions when argument period_length equals 1.

## Syntax:

FORECAST.ETS.SEASONALITY (values, timeline, [data_completion], [aggregation])

## Returns:

Returns an integer value giving the number of samples.

## Arguments:

values is a numeric array or range. values are the historical values, for which you want to forecast the next points.

timeline is a real number or dates or time array or a reference to the range to cells containing them. The timeline (x-value) range for historical values. The timeline doesn't have to be sorted, the function will sort it for calculations. The timeline values must have a consistent step between them.

data_completion is a logical value TRUE or FALSE, a numeric 1 or 0, default is 1 (TRUE). A value of 0 (FALSE) will add missing data points with zero as historical value. A value of 1 (TRUE) will add missing data points by interpolating between the neighboring data points. Although the timeline requires a constant step between data points, the function supports up to 30% missing data points and will add these data points.

aggregation is a numeric value from 1 to 7, with default 1. The aggregation parameter indicates which method will be used to aggregate identical time values:

Aggregation Function
1 AVERAGE
2 COUNT
3 COUNTA
4 MAX
5 MEDIAN
6 MIN
7 SUM Although the timeline requires a constant step between data points, the function will aggregate multiple points which have the same timestamp.

• If a constant step can't be identified in the sorted timeline, the function will return a numeric(#NUM!) error.
• If the ranges of the timeline and historical values aren't of the same size, the function will return an error value.
• If the timeline contains less than 2 periods of data, the function will return a value(#VALUE!) Error.

## Additional details:

• Exponential Smoothing is a method to smooth real values in time series in order to forecast probable future values.
• Exponential Triple Smoothing (ETS) is a set of algorithms in which both trend and periodical (seasonal) influences are processed. Exponential Double Smoothing (EDS) is an algorithm like ETS, but without the periodical influences. EDS produces linear forecasts.
• The same result is returned with FORECAST.ETS.STAT function when argument stat_type equals 9 (and period_length equals 1).
• For more details on Exponential smoothing algorithms, visit Wikipedia.

## Examples:

The table below contains a timeline and its associated values:

A B
1 Timeline Values
2 01/2013 112
3 02/2013 118
4 03/2013 132
5 04/2013 100
6 05/2013 121
7 06/2013 135
8 07/2013 148
9 08/2013 148
10 09/2013 136
11 10/2013 119
12 11/2013 104
13 12/2013 118
Formula Description Returns
=FORECAST.ETS.SEASONALITY(Values;Timeline;TRUE();1) with Values and Timeline as the named ranges from the table above The number of samples in period based on Values and Timeline named ranges above, no missing data, and AVERAGE as aggregation. 6
=FORECAST.ETS.SEASONALITY(Values;Timeline;5;1) with Values and Timeline as the named ranges from the table above The function returns an error value since data_completion cannot be anything except 0,FALSE,1 or TRUE. Err:502

None

## Equivalent Excel functions:

FORECAST.ETS.SEASONALITY