The Excel FORECAST.ETS.STAT function returns a chosen statistic about the exponential-smoothing (ETS) model behind a forecast — such as a smoothing coefficient or an error measure — selected by a numeric statistic_type.
Syntax
| Argument | Description | |
|---|---|---|
values | Required | The historical data range to model (e.g. B2:B40). |
timeline | Required | The matching, evenly spaced date/number range (e.g. A2:A40), the same size as values. |
statistic_type | Required | A number 1–8 selecting which statistic to return (see the how-to below). |
seasonality | Optional | 0 = none, 1 = auto-detect (default), or a fixed season length. |
data_completion | Optional | 1 (default) interpolates missing points; 0 treats gaps as zeros. |
aggregation | Optional | How duplicate timeline entries are combined (1 = AVERAGE, the default). |
How to use it
FORECAST.ETS.STAT exposes the internals of the forecasting model so you can judge its quality. The third argument picks which number you want:
| type | statistic returned |
|---|---|
| 1 | Alpha — base (level) smoothing |
| 2 | Beta — trend smoothing |
| 3 | Gamma — seasonal smoothing |
| 4 | MASE (mean absolute scaled error) |
| 5 | SMAPE (symmetric mean absolute percentage error) |
| 6 | MAE (mean absolute error) |
| 7 | RMSE (root mean square error) |
| 8 | Detected step size of the timeline |
Tip: the error stats (types 4–7) are the quickest way to compare model fit. A smaller SMAPE or RMSE means the ETS model tracks your history more closely — useful before you trust the forecast.
Try it: interactive demo
Pick a FORECAST.ETS.STAT example to see the formula and its result.
Practice workbook
Frequently asked questions
What do the statistic_type numbers mean?
Which statistic tells me if the forecast is any good?
What are alpha, beta and gamma?
Which Excel versions support it?
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