Calculates the multiplicative forecast(s) (future values) based on the historical data using ETS (Exponential Triple Smoothing) or EDS (Exponential Double Smoothing) algorithms. EDS is used when argument period_length is 0, otherwise ETS is used.
FORECAST.ETS.MULT(targets, values, timeline, [period_length], [data_completion], [aggregation])
Returns a real number which is the multiplicative forecast(s) calculated using ETS or EDS algorithms for the given arguments.
target is a date, time, or numeric single value or range. The data point/range for which to calculate a forecast.
values is a numeric array or range. values are the historical values, for which you want to forecast the next points.
timeline is a real number or dates or time array or a reference to the range to cells containing them. The timeline (x-value) range for historical values.
period_length is a numeric value >= 0, the default is 1. A positive integer indicating the number of samples in a period.
data_completion is 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.
aggregation is a numeric value from 1 to 7, with default 1. The aggregation parameter indicates which method will be used to aggregate identical time values:
- If a constant step can't be identified in the sorted timeline, the function will return a numeric(#NUM!) error.
- If the ranges of the timeline and historical values aren't of the same size, the function will return an error value.
- If the timeline contains less than 2 periods of data, the function will return a value(#VALUE!) Error.
- For values of period_length that not being a positive whole number, the function will return a numeric(#NUM!) Error.
- 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.
- FORECAST.ETS.MULT calculates with the model:
- For more details on exponential smoothing algorithms, visit Wikipedia.
The table below contains a timeline and its associated values:
|=FORECAST.ETS.MULT(DATE(2014;1;1);Values;Timeline;1;TRUE();1) with Values and Timeline as the named ranges from the table above||The multiplicative forecast for January 2014 based on Values and Timeline named ranges above, with one sample per period, no missing data, and AVERAGE as aggregation.||131.71437427439|
|=FORECAST.ETS.MULT(DATE(2014;1;1);Values;Timeline;4;TRUE();7) with Values and Timeline as the named ranges from the table above||The multiplicative forecast for January 2014 based on Values and Timeline named ranges above, with period length of 4, no missing data, and SUM as aggregation.||120.747806144882|
|=FORECAST.ETS.MULT(DATE(2014;1;1);Values;Timeline;4;5;7) with Values and Timeline as the named ranges from the table above||The function returns an error value since data_completion cannot be anything except 0,FALSE,1 or TRUE.||Err:502|
|=FORECAST.ETS.MULT(DATE(2014;1;1);Values;Timeline;1.5;TRUE();7) with Values and Timeline as the named ranges from the table above||The function returns a numeric error since period_length is not a positive whole number.||#NUM!|
Related LibreOffice functions:
Equivalent Excel functions: