exhibit a seasonal pattern.
If a pattern cannot be detected, FORECAST.ETS.SEASONALITY returns zero.
With these inputs, the FORECAST.ETS.SEASONALITY function returns 4.
The chart to the right shows this data plotted in ascatter plot.
Argument notes
Thevaluesargument contains the dependent array or range of data, also called y values.
These are existing historical values from which a season length will be calculated.
Thetimelineargument is the independent array or range of values, also called x values.
The timeline must consist of numeric values with a constant step interval.
For example, the timeline could be yearly, quarterly, monthly, daily, etc.
The timeline can also be a simple list of numeric periods, as in the example shown.
Thedata_completionargument is optional and specifies how FORECAST.ETS.SEASONALITY should handle missing data points.
The options are 1 (default) and zero.
By default, FORECAST.ETS.SEASONALITY will provide missing data points by averaging neighboring data points.
If zero is provided for data_completion, FORECAST.ETS.SEASONALITY will treat missing data points as zeros.
Theaggregationargument is optional, and controls how the function should aggregate data points when the timeline contains duplicate timestamps.
The default is 1, which specifies AVERAGE.
Other options are given in the table below.
Note: It is better to perform aggregation before usingFORECAST.ETS.SEASONALITYto make results as accurate as possible.
Errors
The FORECAST.ETS.SEASONALITY function will return errors, as shown below.
FORECAST.ETS can be used to predict numeric values like sales, inventory, expenses, etc.
with a seasonal pattern.
It is designed to be used along with theFORECAST.ETS functionas a way to show forecast accuracy.
Thestatistic_typeargument determines which statistic is returned by FORECAST.ETS.STAT…
