Documentation/Calc Functions/FORECAST.ETS.PI.ADD

Function name:
FORECAST.ETS.PI.ADD

Category:
Statistical Analysis

Summary:
Calculates the prediction interval(s) for additive forecast 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.

Syntax:
FORECAST.ETS.PI.ADD(target, values, timeline, [confidence_level], [period_length], [data_completion], [aggregation])

Returns:
Returns a real number which is the prediction interval(s) for the additive forecast calculated using ETS or EDS algorithms for the given arguments.

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.

confidence_level is a numeric value between 0 and 1 (exclusive), default is 0.95. A value indicating a confidence level for the calculated prediction interval.

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:

For example, with a 90% Confidence level, a 90% prediction interval will be computed (90% of future points are to fall within this radius from forecast).

For ETS, Calc uses an approximation based on 1000 calculations with random variations within the standard deviation of the observation data set (the historical 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.
 * If confidence_level values are <= 0 or >= 1, the function will return the #NUM! error.
 * For values of period_length that is not a positive whole number, the function will return a numeric (#NUM!) Error.

Additional details:

 * 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.PI.ADD calculates with the model:
 * For more details on exponential smoothing algorithms, visit Wikipedia.

Examples:
The table below contains a timeline and its associated values:

Related LibreOffice functions:
FORECAST

FORECAST.ETS.ADD

FORECAST.ETS.MULT

FORECAST.ETS.PI.MULT

FORECAST.ETS.SEASONALITY

FORECAST.ETS.STAT.ADD

FORECAST.ETS.STAT.MULT

FORECAST.LINEAR

ODF standard:
None

Equivalent Excel functions:
None