Exponential Smoothing is a method to smooth real values in time series in order to forecast probable future values.
Exponential Triple Smoothing (ETS) is a set of algorithms in which both trend and periodical (seasonal) influences are processed. Exponential Double Smoothing (EDS) is an algorithm like ETS, but without the periodical influences. EDS produces linear forecasts.
See the Wikipedia on Exponential smoothing algorithms for more information.
target (mandatory): A date, time or numeric single value or range. The data point/range for which to calculate a forecast.
values (mandatory): A numeric array or range. values are the historical values, for which you want to forecast the next points.
timeline (mandatory): A numeric array or range. The timeline (x-value) range for the historical values.
The timeline does not have to be sorted, the functions will sort it for calculations.
The timeline values must have a consistent step between them.
If a constant step cannot be identified in the sorted timeline, the functions will return the #NUM! error.
If the ranges of both the timeline and the historical values are not the same size, the functions will return the #N/A error.
If the timeline contains fewer than 2 data periods, the functions will return the #VALUE! error.
data_completion (optional): a logical value TRUE or FALSE, a numeric 1 or 0, default is 1 (TRUE). A value of 0 (FALSE) will add missing data points with zero as historical value. A value of 1 (TRUE) will add missing data points by interpolating between the neighboring data points.
Although the time line requires a constant step between data points, the function support up to 30% missing data points, and will add these data points.
aggregation (optional): A numeric value from 1 to 7, with default 1. The aggregation parameter indicates which method will be used to aggregate identical time values:
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集計 |
関数 |
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1 |
AVERAGE |
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2 |
COUNT |
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3 |
COUNTA |
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4< |