Documentation/Calc Functions/FORECAST.ETS.SEASONALITY

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.

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.

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:


 * 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:

Related LibreOffice functions:
FORECAST

FORECAST.ETS.ADD

FORECAST.ETS.MULT

FORECAST.ETS.PI.ADD

FORECAST.ETS.PI.MULT

FORECAST.ETS.STAT.ADD

FORECAST.ETS.STAT.MULT

FORECAST.LINEAR

ODF standard:
None

Equivalent Excel functions:
FORECAST.ETS.SEASONALITY