FORECAST.ETS.SEASONALITY Function
Returns the number of samples in period as calculated by Calc in case of FORECAST.ETS functions when argument period_length equals 1.
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 functions when argument stat_type equals 9 (and period_length equals 1).
This function is available since LibreOffice 5.2.
FORECAST.ETS.SEASONALITY (values, timeline, [data_completion], [aggregation])
values (mandatory): A numeric array or range. values are the historical values, for which you want to forecast the next points.
timeline (mandatory): A numeric array or range. The time line (xvalue) range for the historical values.
The time line doesn't have to to be sorted, the functions will sort it for calculations.
The time line values must have a consistent step between them.
If a constant step can't be identified in the sorted time line, the functions will return the #NUM! error.
If the ranges of the time line and historical values aren't of same size, the functions will return the #N/A error.
If the time line contains less than 2 periods of data, the functions will return the #VALUE! Error.
data_completion (optional): 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 time line requires a constant step between data points, the function support up to 30% missing data points, and will add these data points.
aggregation (optional): 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

α’αα»αααα

1

AVERAGE

2

COUNT

3

COUNTA

4

MAX

5

MEDIAN

6

MIN

7

SUM

Although the time line requires a constant step between data points, the functions will aggregate multiple points which have the same time stamp.
The table below contains a timeline and its associated values:

A

B

1

Timeline

ααααα

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

=FORECAST.ETS.SEASONALITY(Values;Timeline;TRUE();1)
Returns 6, the number of samples in period based on Values and Timeline named ranges above, no missing data, and AVERAGE as aggregation.