FORECAST.ETS Function
The FORECAST.ETS function is one of the statistical functions. It is used to calculate or predict a future value based on existing (historical) values by using the AAA version of the Exponential Smoothing (ETS) algorithm.
Syntax
FORECAST.ETS(target_date, values, timeline, [seasonality], [data_completion], [aggregation])
The FORECAST.ETS function has the following arguments:
| Argument | Description |
|---|---|
| target_date | A date for which you want to predict a new value. Must be after the last date in the timeline. |
| values | A range of the historical values for which you want to predict a new point. |
| timeline | A range of date/time values that correspond to the historical values. The timeline range must be of the same size as the values range. Date/time values must have a constant step between them (although up to 30% of missing values can be processed as specified by the data_completion argument and duplicate values can be aggregated as specified by the aggregation argument). |
| seasonality | A numeric value that specifies which method should be used to detect the seasonality. It is an optional argument. The possible values are listed in the table below. |
| data_completion | A numeric value that specifies how to process the missing data points in the timeline data range. It is an optional argument. The possible values are listed in the table below. |
| aggregation | A numeric value that specifies which function should be used to aggregate identical time values in the timeline data range. It is an optional argument. The possible values are listed in the table belows. |
The seasonality argument can be one of the following:
| Numeric value | Behavior |
|---|---|
| 1 or omitted | Seasonality is detected automatically. Positive, whole numbers are used for the length of the seasonal pattern. |
| 0 | No seasonality, the prediction will be linear. |
| an integer greater than or equal to 2 | The specified number is used for the length of the seasonal pattern. |
The data_completion argument can be one of the following:
| Numeric value | Behavior |
|---|---|
| 1 or omitted | Missing points are calculated as the average of the neighbouring points. |
| 0 | Missing points are treated as zero values. |
The aggregation argument can be one of the following:
| Numeric value | Function |
|---|---|
| 1 or omitted | AVERAGE |
| 2 | COUNT |
| 3 | COUNTA |
| 4 | MAX |
| 5 | MEDIAN |
| 6 | MIN |
| 7 | SUM |
Notes
How to apply the FORECAST.ETS function.
Examples
The figure below displays the result returned by the FORECAST.ETS function.
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