Function forecasting::ExponentialSmoothingTrendTune(dataValues, noObservations, alpha, beta, alphaLow, alphaUpp, betaLow, betaUpp)

# forecasting::ExponentialSmoothingTrendTune

The forecasting::ExponentialSmoothingTrendTune() procedure is a time series forecasting helper procedure of forecasting::ExponentialSmoothingTrend() by computing the $$\alpha$$ and $$\beta$$ for which the mean squared error is minimized.

## Function Prototype

forecasting::ExponentialSmoothingTrendTune(
! Provides the alpha for which the mean squared error is minimized.
dataValues,      ! Input, parameter indexed over time set
noObservations,  ! Scalar input, length history
alpha,           ! Scalar output,
beta,            ! Scalar output,
alphaLow,        ! Optional input, default 0.01
alphaUpp,        ! Optional input, default 0.99
betaLow,         ! Optional input, default 0.01
betaUpp)         ! Optional input, default 0.99


## Arguments

dataValues

A one dimensional parameter containing the observations for the first $$T$$ elements of the time set.

noObservations

Specifies the number of elements that belong to the history of the time set. This parameter corresponds to $$T$$ in the notation presented in Time Series Forecasting Notation.

alpha,

beta, i.e. $$\alpha$$ and $$\beta$$ are scalar output parameters of this procedure. The values for $$\alpha$$ and $$\beta$$ are such that the mean squared error of the estimates returned by forecasting::ExponentialSmoothingTrend() are minimized.

alphaLow

Lowerbound on $$\alpha$$, default 0.01.

alphaUpp

Upperbound on $$\alpha$$, default 0.99.

betaLow

Lowerbound on $$\beta$$, default 0.01.

betaUpp

Upperbound on $$\beta$$, default 0.99.

Note

In order to use this function, the Forecasting system library needs to be added to the application.

Please note that this function performs an optimization step; a nonlinear programming solver should be available and, in an AIMMS PRO environment, it should be run server side.