Returns a confidence interval for the forecast value at the specified target date. A confidence interval of 95% means that 95% of future points are expected to fall within this radius from the result FORECAST.ETS forecasted (with normal distribution). Using confidence interval can help grasp the accuracy of the predicted model. A smaller interval would imply more confidence in the prediction for this specific point.
Syntax
FORECAST.ETS.CONFINT(target_date, values, timeline, [confidence_level], [seasonality], [data_completion], [aggregation])
The FORECAST.ETS.CONFINT function syntax has the following arguments:

Target_date Required. The data point for which you want to predict a value. Target date can be date/time or numeric. If the target date is chronologically before the end of the historical timeline, FORECAST.ETS.CONFINT returns the #NUM! error.

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.CONFINT will sort it implicitly for calculations. If a constant step can't be identified in the provided timeline, FORECAST.ETS.CONFINT will return the #NUM! error. If timeline contains duplicate values, FORECAST.ETS.CONFINT will return the #VALUE! error. If the ranges of the timeline and values aren't of same size, FORECAST.ETS.CONFINT will return the #N/A error.

Confidence_level Optional. A numerical value between 0 and 1 (exclusive), indicating a confidence level for the calculated confidence interval. For example, for a 90% confidence interval, a 90% confidence level will be computed (90% of future points are to fall within this radius from prediction). The default value is 95%. For numbers outside of the range (0,1), FORECAST.ETS.CONFINT will return the #NUM! error.

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.CONFINT 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.CONFINT 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.CONFINT 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.