WorksheetFunction.Forecast_ETS_Seasonality (Excel)

Returns the length of the repetitive pattern that Excel detects for the specified time series.

You can use Forecast_ETS_Seasonality following Forecast_ETS to identify which automatic seasonality was detected and used in Forecast_ETS. While you can also use it independently of Forecast_ETS, the methods are tied together, because the seasonality detected in this method is identical to the one used by Forecast_ETS, considering that the same input parameters that affect data completion are passed in both methods. It isn't necessary to sort the timeline (Arg2), because Forecast_ETS_Seasonality sorts it implicitly for calculations. If Forecast_ETS_Seasonality can't identify a constant step in the timeline, it returns run-time error 1004. If the timeline contains duplicate values, Forecast_ETS_Seasonality also returns an error. If the ranges of the timeline and values aren't all of the same size, Forecast_ETS_Seasonality returns run-time error 1004. Passing 0 for the data completions parameter (Arg3) instructs the algorithm to account for missing points as zeros. The default value of 1 accounts for missing points by computing them to be the average of the neighboring points. If there is more than 30% missing data, Forecast_ETS_Seasonality returns run-time error 1004. The aggregation parameter (Arg4) is a numeric value specifying the method to use to aggregate several values that have the same time stamp. The default value of 0 specifies AVERAGE, while other numbers between 1 and 6 specify SUM, COUNT, COUNTA, MIN, MAX, and MEDIAN.

Forecast_ETS_Seasonality (Arg1, Arg2, ..., Arg4)

Dim dblForecast_ETS_Seasonality As Double
dblForecast_ETS_Seasonality = WorksheetFunction.Forecast_ETS_Seasonality(Arg1:=, Arg2:=)

Arguments

Arg1 - Values: the historical values, for which you want to forecast the next points

Arg2 - Timeline: the independent array or range of dates or numeric data. The values in the timeline must have a consistent step between them and can't be zero. See Remarks

Arg3 - Data completions: Although the timeline requires a constant step between data points, Forecast_ETS_Seasonality supports up to 30% missing data, and automatically adjusts for it. See Remarks

Arg4 - Aggregation: Although the timeline requires a constant step between data points, Forecast_ETS_Seasonality aggregates multiple points that have the same time stamp. See Remarks