Excel FORECAST.ETS.STAT function
The FORECAST.ETS.STAT function returns a specified statistical value as a result of time series forecasting.
Note:This FORECAST.ETS function is only available in Excel 2016 and later versions, and not available in Excel for the Web, iOS, or Android.
FORECAST.ETS.STAT(values, timeline, statistic_type, [seasonality], [data_completion], [aggregation])
- Values (required): The existing or historical known values for which you want to predict the next point (y values);
- Timeline (required): A range of date/time or numeric values corresponding to the “Values” (x values);
- Statistic_type (required): A numeric value between 1 and 8 specifies which statistic will be returned. The values and the corresponding statistics are as follows:
| Numeric value
|| Alpha parameter of ETS algorithm
|| The base value parameter: a higher value gives more weight to recent data point.
|| Beta parameter of ETS algorithm
|| The trend value parameter: a higher value gives more weight to the recent trend.
|| Gamma parameter of ETS algorithm
|| The seasonality value parameter: a higher value gives more weight to the recent seasonal period.
|| MASE metric
|| The mean absolute scaled error metric: a measure of the accuracy of forecasts.
|| SMAPE matric
|| The symmetric mean absolute percentage error metric: an accuracy measure based on percentage errors.
|| MAE metric
|| The mean absolute error metric: an accuracy measure based on percentage errors.
|| RMSE metric
|| The root mean squared error metric: a measure of the differences between predicted and observed values.
|| Step size detected
|| The step size detected in the historical timeline.
- Seasonality (optional): A numeric value used to define the length of the seasonal pattern. It can be:
0: No seasonality, which means that Excel will return a linear forecast;
1 or omitted (default value): Auto-detect, which means that Excel will automatically detect the seasonality and use positive, whole numbers for the length of the seasonal pattern;
N (an integer number): 2 ≦ N ≦ 8784 (the number of hours in a leap year), which means that Excel will use this specified number as the length of the seasonal pattern.
- Data_completion (optional): A numeric value specifies how to handle the missing points in the timeline. It can be:
0: Missing points will be treated as zeros;
1 or omitted: Missing points will be calculated as the average of the neighboring points.
- Aggregation (optional): A numeric value specifies which function will be used to aggregate several values with the same time stamp. The values and the corresponding functions are listed below.
| Numeric value
| 1 or omitted
1. The #N/A error occurs if “values” and “timeline” arrays are not the same size;
2. The #NUM! error occurs when any of the following conditions is met:
-- All “timeline” values are the same;
-- A constant step cannot be identified in the provided timeline;
-- “seasonality” is not in the range (0-8784);
-- “data_completion” is any number other than 0 or 1;
-- “statistic_type” is not within 1 to 8;
-- “aggregation” is out of the valid range (1-7).
3. The #VALUE! error occurs when statictic_type, [seasonality], [data_completion] or [aggregation] is nonnumeric.
It returns a numeric value.
As shown in the table below, there is a monthly sales table for 2021 and you want to forecast the sales for Jan 2022 based on the existing sales using the FORECAST.ETS function. Before that, you want to know the statistical value relating to this forecast.
This case is going to calculate statistical values based on different statistic_type values listed in the range E6:E13.
1. Select a cell (F6 in this case), copy or enter the formula below and press the Enter key to get the result. Select this result cell and drag its AutoFill Handle down to the cells where you want to apply this formula.
Note: The arguments “seasonality”, “data_completion” and “aggregation” are omitted in this case.
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