Statistical - FORECAST.ETS Function

The FORECAST.ETS function calculates or predicts a future value based on existing (historical) values by using the AAA version of the Exponential Smoothing (ETS) algorithm.  The predicted value is a continuation of the historical values in the specified target date, which should be a continuation of the timeline.
FORECAST.ETS (target_date, values, timeline, 
   [seasonality], [data_completion], [aggregation]) 

However, FORECAST.ETS supports up to 30% missing data, and will automatically adjust for it. The timeline is not required to be sorted, as FORECAST.ETS will sort it implicitly for calculations.
The data point for which you want to predict a value.
Target date can be date/time or numeric.
A numeric value.
The default value of 1 means Excel detects seasonality automatically for the forecast and uses positive, whole numbers for the length of the seasonal pattern.
0 indicates no seasonality, meaning the prediction will be linear.
Positive whole numbers will indicate to the algorithm to use patterns of this length as the seasonality.
Maximum supported seasonality is 8,760 (number of hours in a year).
FORECAST.ETS supports up to 30% missing data in the timeline and will automatically adjust for it based on Data_completion.
The default value of 1 will account for missing points by completing them to be the average of the neighboring points.
0 will indicate the algorithm to account for missing points as zeros.
Although the timeline requires a constant step between data points, FORECAST.ETS will aggregate multiple points which have the same time stamp.
The aggregation parameter is a numeric value indicating which method will be used to aggregate several values with the same time stamp.
The default value of 0 will use AVERAGE, while other options are SUM, COUNT, COUNTA, MIN, MAX, and MEDIAN.
FORECAST.ETS Function is added in Excel 2016.
FORECAST.ETS Function is added in Excel 2016.
This Function uses advanced machine learning algorithms, such as Exponential Triple Smoothing (ETS).
This Function uses advanced machine learning algorithms, such as Exponential Triple Smoothing (ETS).
This Function uses advanced machine learning algorithms, such as Exponential Triple Smoothing (ETS).
This Function uses advanced machine learning algorithms, such as Exponential Triple Smoothing (ETS).
If a constant step cannot be identified in the provided timeline, FORECAST.ETS returns the #NUM! error.
If a constant step cannot be identified in the provided timeline, FORECAST.ETS returns the #NUM! error.
If timeline contains duplicate values, FORECAST.ETS returns the #VALUE! Error.
If timeline contains duplicate values, FORECAST.ETS returns the #VALUE! Error.
If the ranges of the timeline and values are not of same size, FORECAST.ETS returns the #N/A error.
If the ranges of the timeline and values are not of same size, FORECAST.ETS returns the #N/A error.
If the Seasonality is <0, or >8760, or a non-numeric value, FORECAST.ETS returns the #NUM! error.
If the Seasonality is <0, or >8760, or a non-numeric value, FORECAST.ETS returns the #NUM! error.
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