Returns a statistical value as a result of time series forecasting.
Statistic type indicates which statistic is requested by this function.
Syntax
FORECAST.ETS.STAT(values, timeline, statistic_type, [seasonality], [data_completion], [aggregation])
The FORECAST.ETS.STAT function syntax has the following arguments:

Values Required. Values are the historical values, for which you want to forecast the next points.

Timeline Required. The independent array or range of numeric data. The dates in the timeline must have a consistent step between them and can’t be zero. The timeline isn't required to be sorted, as FORECAST.ETS.STAT will sort it implicitly for calculations. If a constant step can't be identified in the provided timeline, FORECAST.ETS.STAT will return the #NUM! error. If timeline contains duplicate values, FORECAST.ETS.STAT will return the #VALUE! error. If the ranges of the timeline and values aren't of same size, FORECAST.ETS.STAT will return the #N/A error.

Statistic_type Required. A numeric value between 1 and 8, indicating which statistic will be returned for the calculated forecast.

Seasonality Optional. 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. For any other value, FORECAST.ETS.STAT will return the #NUM! error.
Maximum supported seasonality is 8,760 (number of hours in a year). Any seasonality above that number will result in the #NUM! error.

Data completion Optional. Although the timeline requires a constant step between data points, FORECAST.ETS.STAT supports up to 30% missing data, and will automatically adjust for it. 0 will indicate the algorithm to account for missing points as zeros. The default value of 1 will account for missing points by completing them to be the average of the neighboring points.

Aggregation Optional. Although the timeline requires a constant step between data points, FORECAST.ETS.STAT 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, MEDIAN.
The following optional statistics can be returned:

Alpha parameter of ETS algorithm Returns the base value parameter—a higher value gives more weight to recent data points.

Beta parameter of ETS algorithm Returns the trend value parameter—a higher value gives more weight to the recent trend.

Gamma parameter of ETS algorithm Returns the seasonality value parameter—a higher value gives more weight to the recent seasonal period.

MASE metric Returns the mean absolute scaled error metric—a measure of the accuracy of forecasts.

SMAPE metric Returns the symmetric mean absolute percentage error metric—an accuracy measure based on percentage errors.

MAE metric Returns the symmetric mean absolute percentage error metric—an accuracy measure based on percentage errors.

RMSE metric Returns the root mean squared error metric—a measure of the differences between predicted and observed values.

Step size detected Returns the step size detected in the historical timeline.