a target date or period).
It is designed to be used along with theFORECAST.ETS functionas a way to show forecast accuracy.
With these inputs, the FORECAST.ETS.CONFINT returns 198.92 in cell E13.
Argument notes
Thetarget_dateargument represents the point on the timeline that a confidence interval prediction should be calculated.
Thevaluesargument contains the dependent array or range of data, also called y values.
These are existing historical values from which a prediction 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.
Theseasonalityargument is optional and represents the length of the seasonal pattern expressed in timeline units.
The number 8784 = 366 x 24, the number of hours in a leap year.
Thedata_completionargument is optional and specifies how FORECAST.ETS.CONFINT should handle missing data points.
The options are 1 (default) and zero.
By default, FORECAST.ETS.CONFINT will provide missing data points by averaging neighboring data points.
If zero is provided, FORECAST.ETS.CONFINT will treat missing data points as zero.
The default is 1, which specifies AVERAGE.
Other options are given in the table below.
Note: FORECAST.ETS.CONFINT results will be more accurate if aggregation is performed beforehand.
Errors
The FORECAST.ETS.CONFINT 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…
