FORECAST.ETS.SEASONALITY

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# 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.

See the Wikipedia on Exponential smoothing algorithms for more information.

The same result is returned with FORECAST.ETS.STAT functions when argument stat_type equals 9 (and period_length equals 1).

### Syntax

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 timeline (x-value) range for the historical values.

The timeline does not have to be sorted, the functions will sort it for calculations.
The timeline values must have a consistent step between them.
If a constant step cannot be identified in the sorted timeline, the functions will return the #NUM! error.
If the ranges of both the timeline and the historical values are not the same size, the functions will return the #N/A error.
If the timeline contains fewer than 2 data periods, 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 | Function
--- | ---
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.

#### Esimerkki

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

=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.

#### Teknistä tietoa

Tämä funktio on käytettävissä LibreOffice 5.2:sta alkaen.

This function is NOT part of the Open Document Format for Office Applications (OpenDocument) Version 1.3. Part 4: Recalculated Formula (OpenFormula) Format standard. The name space is

COM.MICROSOFT.FORECAST.ETS.SEASONALITY

Aiheeseen liittyvää

FORECAST.ETS.SEASONALITY wiki page.

See also: FORECAST.ETS.ADD, FORECAST.ETS.MULT, FORECAST.ETS.STAT.ADD, FORECAST.ETS.STAT.MULT, FORECAST.ETS.PI.ADD, FORECAST.ETS.PI.MULT, FORECAST, FORECAST.LINEAR

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