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Calculates or predicts a future value based on existing (historical) values by using the AAA version of the Exponential Smoothing (ETS) algorithm. The predicted value is a continuation of the historical values in the specified target date, which should be a continuation of the timeline. You can use this function to predict future sales, inventory requirements, or consumer trends.
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This function requires the timeline to be organized with a constant step between the different points. For example, that could be a monthly timeline with values on the 1st of every month, a yearly timeline, or a timeline of numerical indices. For this type of timeline, it’s very useful to aggregate raw detailed data before you apply the forecast, which produces more accurate forecast results as well.
Syntax
FORECAST.ETS(target_date, values, timeline, [seasonality], [data_completion], [aggregation])
The FORECAST.ETS 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 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 will sort it implicitly for calculations. If a constant step can't be identified in the provided timeline, Forecast.ETS will return the #NUM! error. If timeline contains duplicate values, FORECAST.ETS will return the #VALUE! error. If the ranges of the timeline and values aren't of same size, FORECAST.ETS will return the #N/A 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 will return the #NUM! error.
Maximum supported seasonality is 8,760 (number of hours in a year). Windows vs mac sales. 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 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 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.
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Calculates or predicts a future value based on existing (historical) values by using the AAA version of the Exponential Smoothing (ETS) algorithm. The predicted value is a continuation of the historical values in the specified target date, which should be a continuation of the timeline. You can use this function to predict future sales, inventory requirements, or consumer trends.
This function requires the timeline to be organized with a constant step between the different points. For example, that could be a monthly timeline with values on the 1st of every month, a yearly timeline, or a timeline of numerical indices. For this type of timeline, it’s very useful to aggregate raw detailed data before you apply the forecast, which produces more accurate forecast results as well.
Syntax
FORECAST.ETS(target_date, values, timeline, [seasonality], [data_completion], [aggregation])
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The FORECAST.ETS 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 returns the #NUM! error.
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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 will sort it implicitly for calculations. If a constant step can't be identified in the provided timeline, Forecast.ETS will return the #NUM! error. If timeline contains duplicate values, FORECAST.ETS will return the #VALUE! error. If the ranges of the timeline and values aren't of same size, FORECAST.ETS will return the #N/A 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 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 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 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.